Reviewerly/exHarmony huggingface dataset:
Learning based Predictive Error Estimation and Compensator Design for Autonomous Vehicle Path Tracking

Model predictive control (MPC) is widely used for path tracking of autonomous vehicles due to its ability to handle various types of constraints. However, a considerable predictive error exists because of the error of mathematics model or the model linearization. In this paper, we propose a framework combining the MPC with a learning-based error estimator and a feedforward compensator to improve the path tracking accuracy. An extreme learning machine is implemented to estimate the model based predictive error from vehicle state feedback information. Offline training data is collected from a vehicle controlled by a model-defective regular MPC for path tracking in several working conditions, respectively. The data include vehicle state and the spatial error between the current actual position and the corresponding predictive position. According to the estimated predictive error, we then design a PID-based feedforward compensator. Simulation results via Carsim show the estimation accuracy of the predictive error and the effectiveness of the proposed framework for path tracking of an autonomous vehicle.
A Cognitive Robotic System for a Human-Following Robot

In this paper we propose a cognitive robotic system that utilizes computational psychology (the Soar cognitive architecture) and an obstacle avoidance method (modified dynamic window approach) in ROS (Robot Operating System) platform for controlling a mobile robot. This system is applied to perform a task of human-following, aiming to help the robot navigate itself to the target person avoiding collision. A cognitive agent based on Soar cognitive architecture is created to reason its current situation and make decisions on movement direction such as go-straight, turn-left or turn-right, whereas the dynamic window approach is modified to avoid collision by computing appropriate velocities for driving the robot motors. To the end, a part of implementation is presented to describes how the system works.
Plan-CVAE: A Planning-Based Conditional Variational Autoencoder for Story Generation

Story generation is a challenging task of automatically creating natural languages to describe a sequence of events, which requires outputting text with not only a consistent topic but also novel wordings. Although many approaches have been proposed and obvious progress has been made on this task, there is still a large room for improvement, especially for improving thematic consistency and wording diversity. To mitigate the gap between generated stories and those written by human writers, in this paper, we propose a planning-based conditional variational autoencoder, namely Plan-CVAE, which first plans a keyword sequence and then generates a story based on the keyword sequence. In our method, the keywords planning strategy is used to improve thematic consistency while the CVAE module allows enhancing wording diversity. Experimental results on a benchmark dataset confirm that our proposed method can generate stories with both thematic consistency and wording novelty, and outperforms state-of-the-art methods on both automatic metrics and human evaluations.
nnDPI: A Novel Deep Packet Inspection Technique Using Word Embedding, Convolutional and Recurrent Neural Networks

Traffic Characterization, Application Identification, Per Application Classification, and VPN/Non-VPN Traffic Characterization have been some of the most notable research topics over the past few years. Deep Packet Inspection (DPI) promises an increase in Quality of Service (QoS) for Internet Service Providers (ISPs), simplifies network management and plays a vital role in content censoring. DPI has been used to help ease the flow of network traffic. For instance, if there is a high priority message, DPI could be used to enable high-priority information to pass through immediately, ahead of other lower priority messages. It can be used to prioritize packets that are mission-critical, ahead of ordinary browsing packets. Throttling or slowing down the rate of data transfer can be achieved using DPI for certain traffic types like peer-to-peer downloads. It can also be used to enhance the capabilities of ISPs to prevent the exploitation of Internet of Things (IoT) devices in Distributed Denial-Of-Service (DDOS) attacks by blocking malicious requests from devices. In this paper, we introduce a novel architecture for DPI using neural networks utilizing layers of word embedding, convolutional neural networks and bidirectional recurrent neural networks which proved to have promising results in this task. The proposed architecture introduces a new mix of layers which outperforms the proposed approaches before.
A Model-View-ViewModel (MVVM) Application Framework for Hearing Impairment Diagnosis - Class Inheritance Architecture

Around 466 million people worldwide (over 5% of the world's population) have disabling hearing loss, and out of these 34 million are children. Estimates suggest that by 2050, over 900 million people worldwide will have disabling hearing loss. The annual global cost of unaddressed hearing loss amounts to US$ 750 billion. Early detection of hearing loss can reduce its impact on an individual's life in addition to saving a huge cost. This paper, the last one in a series of three papers, presents the class inheritance architecture of an open-source application framework for hearing impairment diagnosis. The framework is built using the Model-View-ViewModel (MVVM) pattern which separates the development of graphical user interface (GUI) from the development of business and back-end logic. Some of the benefits of the MVVM pattern include reusable components, independent development of GUI and business or back-end logic, flexibility to modify GUI without having to change business or back-end logic, ease of unit testing, and reduced maintenance overhead. The proposed framework along with the open-source code makes it possible to easily extend the application functionality thus enabling other researchers and practitioners to develop their own versions of hearing loss diagnosis applications. The proposed software was evaluated by an otolaryngologist and found to be very beneficial in assisting a clinician to reach a hearing impairment diagnosis conclusion more methodically, swiftly and accurately.
A radionuclide outlier identification and correlation analysis on low- and intermediate-level radioactive waste

In this paper, we collected the analysis data of radionuclide concentrations and performed verification analysis on the data integrity and outliers in radioactive waste. These analysis results investigated the correlation between the radionuclide concentration data. As a result of analyzing the characteristics and outliers of the radioactive waste radionuclide data by using the collected original and repackaged drum information, the several generation facilities with high average radionuclide concentrations were identified. As a result of discriminating radioactive concentration outliers by standardizing the values for each nuclide, it was confirmed that the I-129(2.45), Mn-54(2.31) and Nb-94(2.31) nuclides had outliers. Also, it was found that there was a significant interaction of concentration in the middle class of Cs-137 and Tc-99 according to the waste generation facility and middle-class classification. We expect to be able to efficiently manage and dispose the radioactive waste inventory through analyzing the radionuclide related data.
A modular toolkit for visual tracking performance evaluation

a b s t r a c tWe present a modular software package for conducting single-target visual object tracking experiments and analyzing results.Our software supports many of the common usage patterns in visual tracking evaluation out of the box, but is also modular and allows various extensions.Users are able to integrate existing implementations of visual tracking algorithms with little additional effort using a standardized and flexible communication protocol.The software has been the technical backbone of the VOT Challenge initiative for many years and has grown and evolved with the competitions that it supported.We present its current state and the capabilities of the package and conclude with some plans for future development.
Just Wait for it... Mining Sequential Patterns with Reliable Prediction Delays

Suppose we are given an event sequence X of observed events and an equally long binary sequence Y that indicates whether something of interest happened at that particular point in time. We consider the problem of mining sequential patterns from X that reliably predict those interesting events. With reliable we mean those patterns that not only predict that an interesting event is likely to follow but especially those patterns for which we can with high precision tell how long until that event will happen. That is, we are after patterns that have highly skewed distributions of delays between pattern occurrences and predicted events. In particular, we are after the smallest, least redundant set of such patterns that together explain the interesting events well. We formally define this problem in terms of the Minimum Description Length principle, by which we identify the best patterns as those that describe the data most succinctly. As discovering the optimal explanation of Y given a set of patterns, as well as the discovery of optimal pattern set are both hard problems that do not allow for straightforward optimization, we propose the heuristic Omen algorithm. Through extensive empirical evaluation we show that Omen works well in practice and beats the state of the art both quantitatively and qualitatively.
Systematic Literature Review: Metaheuristics-based Approach for Workflow Scheduling in Cloud

Workflow scheduling is one of the fundamental challenges of cloud computing, which aims to execute the workflows while considering the Quality of Service (QoS) requirements. This paper provides a thorough analysis of the different studies that have been conducted on workflow scheduling in cloud computing in last ten years. A thorough statistical analysis is provided in this review which was conducted by extracting specific information from 59 papers published between the years 2010 and 2020. Moreover, a classification of the proposed methods based on the type of scheduling algorithm applied in each scheme is presented. Finally, future direction in the domain of workflow scheduling in cloud is stated.
Improved Learning Outcomes of Descriptive Statistics Through the Test Room and Data Processing Features in the Mobile Learning Model

Descriptive statistics plays an important role in processing and presenting data. Therefore descriptive statistics is one of the topics that must be mastered by all students, including Informatics Engineering students. Unfortunately, the high mathematical content in this topic is often considered as a threat to students. It makes them get bored and distracted their attention to other things, mostly to their smartphones. This study conducted to develop a mobile-based descriptive statistical learning media that facilitate student work feedback. The feedback was given through the Test Room and Data Processing Features. The media was developed using the prototyping method. The analysis is done by conducting interviews, distributing questionnaires, and trying similar applications. Verification is done using the blackbox testing method, which provides input and sees the output. Validation is done by analyzing the results of examinations from two groups of students, the treated group and the control group. The validation results show that the learning outcome from the treated group was improved. The percentage of students who experienced an increase in exam scores from the treated group was 144% greater than the control group.
Spatio-Temporal Scale Coded Bag-of-Words

The Bag-of-Words (BoW) framework has been widely used in action recognition tasks due to its compact and efficient feature representation. Various modifications have been made to this framework to increase its classification power. This often results in an increased complexity and reduced efficiency. Inspired by the success of image-based scale coded BoW representations, we propose a spatio-temporal scale coded BoW (SC-BoW) for video-based recognition. This involves encoding extracted multi-scale information into BoW representations by partitioning spatio-temporal features into sub-groups based on the spatial scale from which they were extracted. We evaluate SC-BoW in two experimental setups. We first present a general pipeline to perform real-time action recognition with SC-BoW. Secondly, we apply SC-BoW onto the popular Dense Trajectory feature set. Results showed SC-BoW representations to successfully improve performance by 2–7% with low added computational cost. Notably, SC-BoW on Dense Trajectories outperformed more complex deep learning approaches. Thus, scale coding is a low-cost and low-level encoding scheme that increases classification power of the standard BoW without compromising efficiency.
ELMV

Many real-world Electronic Health Record (EHR) data contain a large proportion of missing values. Leaving a substantial portion of missing information unaddressed usually causes significant bias, leading to invalid conclusions to be drawn. On the other hand, training a machine learning model with a much smaller nearly-complete subset can drastically impact the reliability and accuracy of model inference. Data imputation algorithms that attempt to replace missing data with meaningful values, inevitably increase the variability of effect estimates with increased missingness, making it unreliable for hypothesis validation. We propose a novel Ensemble-Learning for Missing Value (ELMV) framework, an effective approach to construct multiple subsets with much lower missing rates of the original EHR data as well as to mobilize dedicated support data for ensemble learning, for the purpose of reducing the bias caused by substantial missing values. ELMV has been evaluated on real-world healthcare data for critical feature identification and simulation data with different missing rates for outcome prediction. In both experiments, ELMV outperforms conventional missing value imputation methods and traditional ensemble learning models. The source code of ELMV is available at https://github.com/lucasliu0928/ELMV.
A Quantum Finite Automata Approach to Modeling the Chemical Reactions

In recent years, the modeling interest has increased significantly from the molecular level to the atomic and quantum scale. The field of computational chemistry plays a significant role in designing computational models for the operation and simulation of systems ranging from atoms and molecules to industrial-scale processes. It is influenced by a tremendous increase in computing power and the efficiency of algorithms. The representation of chemical reactions using classical automata theory in thermodynamic terms had a great influence on computer science. The study of chemical information processing with quantum computational models is a natural goal. In this paper, we have modeled chemical reactions using two-way quantum finite automata, which are halted in linear time. Additionally, classical pushdown automata can be designed for such chemical reactions with multiple stacks. It has been proven that computational versatility can be increased by combining chemical accept/reject signatures and quantum automata models.
A Real-Time Nonlinear Hammerstein Model For Bidirectional DC Motor Based on Multi-Layer Neural Networks

System identification is finding a model that can describe the dynamic characteristic of the examined system and predict the next output depending on the collected input/output data for that system at previous times. All the real dynamic systems have a nonlinear behavior, but this non-linearity is graduating from a simple to a brutal degree; Mechatronic systems are not spared from this rule. This article presents a real-time nonlinear model for bidirectional DC motor based on block-oriented Hammerstein to avoid the Coulomb friction and its dead zone nonlinear effect with the viscous friction. The recursive weighted least squares (RWLS) method is used to train the Hammerstein network. The mean square error for the system’s closest model is about 9.5 relative to fluctuated output speed from 1870 to -1035 (rpm).
Implementing the Draft RISC-V Scalar Cryptography Extensions

RISC-V is an increasingly popular, free and open Instruction Set Architecture (ISA). Many standard extensions to RISC-V are currently being designed and evaluated, including one for accelerating cryptographic workloads. Unlike most incumbent ISAs which re-use existing large SIMD state and data-paths to accelerate cryptographic operations, RISC-V also adds support for smaller machines with narrow 32 and 64-bit data-paths. For embedded, IoT class devices, this significantly lowers the barrier to entry for secure and efficient accelerated cryptography. In this paper, we describe (to our knowledge) the first complete, free and open-source implementation of the draft 32-bit RISC-V Cryptography Extension. We detail the performance benefits for several important algorithms, and associated hardware costs. Our experiences help to guide the ongoing standardisation work and provide a platform for other researchers to experiment with a complete and representative CPU system, implementing the draft cryptography extension.
Secured Voting Using Blockchain

Democratic elections are the building blocks of a Democracy. In India elections play a huge role in deciding the government. But, during the years, the people are losing their hope in the elections conducted due to many allegations of bribery, identity theft in voting, repeated votes, false votes and many others. There is a need for establishing a highly secure election system to protect the integrity of the elections. A blockchain based voting system using multichain platform can address this problem and provide a highly secure voting system. At the same time, it also allows voters the ability to vote directly from their homes with an access to a PC. The blockchain technology is computationally secure and cannot be easily tampered with. With today's systems, successfully hacking a blockchain network is near impossible. This system makes the election process very simple and robust while at the same time saving a lot of resources. There is however an overhead of time associated with blockchain transactions. But that is a small price for a highly secure voting system.
Attention tracking for developers

For improving speed and quality of development of IT projects it is essential to study how to increase the efficiency of developers. One way to improve the quality of developers' work is to help them concentrate better, timely detect a drop in concentration and brain fatigue, which could be done by controlling the level of attention during programming. This study focuses on the study of the level of attention of programmers using EEG (electroencephalogram), as the most promising and realistic for use in this environment. We found that the level of attention can be determined using the control of alpha and beta waves measured using EEG, as well as specific features of the functional state of the brain compared to another type of mental load - driving.
Attack-Defence Frameworks: Argumentation-Based Semantics for Attack-Defence Trees

This position paper connects the areas and communities of abstract argumentation and attack-defence trees in the area of security. Both areas deal with attacks, defence and support and both areas rely on applications dealing with human aggressive activities. The unifying idea we use in this paper is to regard arguments as AND-OR attack trees as proposed by Schneier in the security domain. The core model, which is acceptable for both communities, is a pair $$(\mathbf{S},\twoheadrightarrow )$$ , where S is a set of attack trees (the “arguments”) and $$\twoheadrightarrow $$ is a binary relation on attack trees (the “attack” relation). This leads us to the notion of an attack-defence framework, which provides an argumentation-based semantics for attack-defence trees and more general attack-defence graphs.
WITHDRAWN: Industrial park innovation ecosystem based on FPGA and Internet of Things

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Application of Knowledge Graph Technology in Unified Management Platform for Wind Power Data

The data from the field has reached a new stage in scale and type with the development of the wind power industry. They are scattered in each business department, it will be of great help to the industry if these data are managed and applied uniformly. Therefore, a wind power data management method knowledge graph-based is proposed. Firstly, the knowledge graph elements including entity, relation, and property are extracted from multi-source wind power data. And the extraction methods for structured and unstructured data are given respectively in this step. Then, the obtained elements are used to construct a panoramic knowledge graph of wind power data in the graph database Neo4j to complete the connection between different types and business data. Finally, the typical case such as fault reasoning and business query is compared with the traditional management mode based on relational database, the results show that the method proposed in this paper has a better application effect.
Batched quantum state exponentiation and quantum Hebbian learning

Machine learning is a crucial aspect of artificial intelligence. This paper details an approach for quantum Hebbian learning through a batched version of quantum state exponentiation. Here, batches of quantum data are interacted with learning and processing quantum bits (qubits) by a series of elementary controlled partial swap operations, resulting in a Hamiltonian simulation of the statistical ensemble of the data. We decompose this elementary operation into one and two qubit quantum gates from the Clifford+T set and use the decomposition to perform an efficiency analysis. Our construction of quantum Hebbian learning is motivated by extension from the established classical approach, and it can be used to find details about the data such as eigenvalues through phase estimation. This work contributes to the near-term development and implementation of quantum machine learning techniques.
Maximum Likelihood Estimates and a Kernel k-Means Iterative Algorithm for Normal Mixtures

Duda et al. established a connection between maximum likelihood estimates and a k-Means algorithm approximating the Mahalanobis distance by the Euclidean distance for Normal Mixtures. They suggested that a more accurate result might be possible if identical covariance matrices were assumed. In this paper that is shown to be true by using a kernel K-means algorithm that does not rely on approximating the Mahalanobis distance.