Reviewerly/exHarmony huggingface dataset:
A novel intelligent model integrating PLSR with RBF-Kernel based Extreme Learning Machine: Application to modelling petrochemical process

With the petrochemical process data getting complicated, building accurate and robust process analysis models has becoming a hot research. In this study, a novel intelligent model integrating PLSR with RBF-Kernel based Extreme Learning Machine (PLSR-RBFKELM) is proposed for overcoming the difficulties found in the conventional extreme learning machine when dealing with collinearity. In the proposed model, radial basis function kernel is employed instead of activation functions in the ELM hidden layer to effectively deal with the high nonlinearity problem of modeling data, and partial least square regression is utilized to solve the collinearity problem. In order to verify the performance, the proposed PLSR-RBFKELM model is applied to modeling one real-world petrochemical process - high density polyethylene process in the steady state. Simulation results demonstrate that the proposed model can achieve good performance in terms of accuracy and stability for static process modeling.
Order-3 stability analysis of particle swarm optimization

Particle swarm optimization (PSO) is a stochastic optimization algorithm, whose distribution is completely determined by its moments. Up to now, most of previous theoretical researches pay attention to the first and/or second moments only to give its stable condition, ignoring the analysis of third moment probably due to its complex third-order recurrence equation. To enrich theoretical cornerstones of PSO, this work analyzes its first, second and third moments, respectively. Built on first-order fixed point and two novel simplified strategies, we present the second and third order standardized recurrence equations for PSO. Their corresponding characteristic equations can be analyzed through the derivative functions to obtain the necessary and sufficient convergence condition. Besides, we show that the specific third-order stable region is slightly less than the second-order one in illustrations.
Automated Text Readability Assessment for German Language: A Quality of Experience Approach

Data-driven approaches towards readability assessment, using automated linguistic analysis and machine learning methods, is a viable road forward for readability rankings. This paper describes the development of an automated readability assessment estimator based on supervised learning algorithms over German text corpora. For this purpose, natural language processing tools are used to extract 73 linguistic features grouped in traditional, lexical and morphological features. Feature engineering approaches are employed to select informative features. Different supervised learning models are implemented, with the top-ranked features fed as input. The results obtained depict that Random Forest Regressor yielding best result (0.847) for RMSE measure.
Predicting Quality of Experience of Popular Mobile Applications from a Living Lab Study

In this paper, we present a hybrid method (qualitative and quantitative) to model and predict the Quality of Experience (QoE) of mobile applications used on WiFi or cellular network. Our 33 living lab participants rated their mobile applications' QoE in various contexts for four weeks resulting in a total of 5663 QoE ratings. At the same time, our smartphone logger (mQoL-Log) collected background information such as network information, user activity, battery statistics and more. We focused this study on frequently used and highly interactive applications including Google Chrome, Google Maps, Spotify, Instagram, Facebook, Facebook Messenger and WhatsApp. After pre-processing the dataset, we used classical machine learning techniques and algorithms (Extreme Gradient Boosting) to predict the QoE of the application usage. The results showed that our model can predict the user QoE with 94±0.77 accuracy. Surprisingly, after the following top three features: session length, battery level and network QoS, the user activity (e.g., if walking) and intended action to accomplish with the app were the most predictive features. Longer application use sessions often have worse QoE than shorter sessions.
An Advanced Particle Swarm Optimization Based Feature Selection Method for Tri-script Handwritten Digit Recognition

Handwritten digit recognition is a well-studied pattern recognition problem. Most of the techniques, reported in the literature, have been concentrated on designing several feature vectors which represent the digits in a better way. But, at times, such attempt not only increases the dimensionality of extracted feature vector but also suffers from having irrelevant and/or redundant features. To address this, in the present work, a recently introduced Particle Swarm Optimization (PSO) based feature selection method has been applied with suitable modifications. For the course of this experiment, we have confined ourselves to a newly employed feature vector for handwritten digit recognition, namely DAISY feature descriptor. The proposed feature selection method is tested on three handwritten digit databases written in Bangla, Devanagari and Roman scripts. The experimental results show that significant amount of feature dimension is reduced without compromising on recognition accuracy. Comparison of the present feature selection method with two of its ancestors also reveals that the present method outperforms the others.
Application of ultrafine fraction soil extraction and analysis for mineral exploration

The need to innovate to advance exploration success is ongoing and, while regional geochemical surveys are common, increasing the data we collect and improving survey results has stagnated. A new adaption of existing methods known as UltraFine+™ extracts the
Deep activity recognition on imaging sensor data

Inspired by the recent success of deep learning (DL) approaches in computer vision domain, this Letter proposes a framework to encode the sensor data into an image representation for the activity recognition task. The signal from sensors is encoded based on the Gramian Angular Field. The encoding technique increases the dimension of the data, captures a local temporal relationship in terms of temporal correlation between time intervals on the geometric interpretation, and can be easily applied to the pre-trained DL architecture. The proposed framework is examined with respect to six popular sensor-based activity recognition datasets. Using the authors’ framework, the results show that their approach outperforms most of the state-of-the-art approaches.
A taxonomy generation tool for semantic visual analysis of large corpus of documents

Taxonomies are semantic resources that help to categorize and add meaning to data. In a hyperconnected world where information is generated at a rate that exceeds human capacities to process and make sense of it, such semantic resources can help to access relevant information more efficiently by extracting knowledge from large and unstructured data sets. Taxonomies are related to specific domains of knowledge in which they identify relevant topics. However, they have to be validated by experts to guarantee that its terms and relations are meaningful. In this paper, we introduce a semiautomatic taxonomy generation tool for supporting domain experts in building taxonomies that are then used to automatically create semantic visualizations of data. Our proposal combines automatic techniques to extract, sort and categorize terms, and empowers domain experts to take part at any stage of the process by providing a visual edition tool. We tested the tool’s usability in two use cases from different domains and languages. Results show that all the functionalities are easy to use and interact with. Lessons learned from this experience will guide the design of a utility evaluation involving domain experts interested in data analysis and knowledge modeling.
Platform-based service composition for manufacturing: A conceptualization

With the implementation of cyber-physical production systems, a multitude of isolated industrial services utilizing versatile hardware and software components in heterogeneous and rigid system architectures are an obstacle for the consequent extension of holistic automation within production networks. To address this issue, we conceptualize a platform-based system architecture enabling value-adding service bundles based on and extending proven architectural styles. Our proposal also aims on qualifying communication systems to work transboundary in value-adding networks. The presented architecture is applied to a use case in context of maintenance. Our findings provide an adaptive conceptualization for application-specific service composition and integration.
Temporally Attributed Description Logics

Knowledge graphs are based on graph models enriched with (sets of) attribute-value pairs, called annotations, attached to vertices and edges. Many application scenarios of knowledge graphs crucially rely on the frequent use of annotations related to time. Building upon attributed logics, we design description logics enriched with temporal annotations whose values are interpreted over discrete time. Investigating the complexity of reasoning in this new formalism, it turns out that reasoning in our temporally attributed description logic $$\mathcal {ALCH} ^{\mathbb {T}}_@$$ is highly undecidable; thus we establish restrictions where it becomes decidable, and even tractable.
View Position Impact on QoE in an Immersive Telepresence System for Remote Operation

In this paper, we investigate how different viewing positions affect a user's Quality of Experience (QoE) and performance in an immersive telepresence system. A QoE experiment has been conducted with 27 participants to assess the general subjective experience and the performance of remotely operating a toy excavator. Two view positions have been tested, an overhead and a ground-level view, respectively, which encourage reliance on stereoscopic depth cues to different extents for accurate operation. Results demonstrate a significant difference between ground and overhead views: the ground view increased the perceived difficulty of the task, whereas the overhead view increased the perceived accomplishment as well as the objective performance of the task. The perceived helpfulness of the overhead view was also significant according to the participants.
Continuous Space-Bounded Non-malleable Codes from Stronger Proofs-of-Space

Non-malleable codes are encoding schemes that provide protections against various classes of tampering attacks. Recently Faust et al. (CRYPTO 2017) initiated the study of space-bounded non-malleable codes that provide such protections against tampering within small-space devices. They put forward a construction based on any non-interactive proof-of-space (NIPoS). However, the scheme only protects against an a priori bounded number of tampering attacks. We construct non-malleable codes that are resilient to an unbounded polynomial number of space-bounded tamperings. Towards that we introduce a stronger variant of $$\text {NIPoS}$$ called proof-extractable $$\text {NIPoS}$$ ( $$\text {PExt-NIPoS}$$ ), and propose two approaches of constructing such a primitive. Using a new proof strategy we show that the generic encoding scheme of Faust et al. achieves unbounded tamper-resilience when instantiated with a $$\text {PExt-NIPoS}$$ . We show two methods to construct $$\text {PExt-NIPoS}$$ : We conclude the paper with a comparative study of all our non-malleable code constructions with an estimation of concrete parameters.
Stuck in The Middle: Removing Obstacles to New Program Features through Batch Refactoring

Developers may introduce poor code structures spotted by code smells along the software maintenance. However, only some of these structures eventually become obstacles to the addition of new features. Developers are forced to remove these obstacles before adding features. Identifying which poor structures are actual obstacles is hard, due to their subtlety and scattering across code elements. Such identification has been largely debated by developers in public platforms such as Gerrit Code Review. Fully removing obstacles is also hard, as developers often have to perform non-trivial sets of interrelated code transformations. Despite enabling the feature addition, certain sets of transformations tend to introduce rather than remove code smells. The scarce knowledge on recurring obstacles, and how refactoring can cope with them, helps little in guiding the feature addition. This doctoral research aims to address the current scarceness via empirical studies with projects and their developers. Our major goal is three-fold: (1) to assess past feature additions in order to elicit recurring obstacles; (2) to understand when interrelated transformations unexpectedly introduce poor code structures; and (3) to propose a refactoring recommender system. Contrarily to the few existing ones, our system aims to guide developers along the feature addition while removing poor code structures.
Android-based Physics Comic Media Development on Thermodynamic Experiment for Mapping Cooperate Attitude for Senior High School

Abstract The research is aimed to (1) produce a physics comic based android on thermodynamic experiment suitable for use in learning (2) know the cooperate attitude mapping of senior high school with physics comic. This study used the 4D Model development model. In the Define stage to define the problem. Design stage to design physics comic learning media. Develop stage to validation of product draft by validators and learning experiments. The data obtained were analyzed quantitatively. Instrument validity is calculated using Content Validity Index (CVI). The results showed that (1) Media of physics comic developed suitable for use in learning, (2) Students cooperate attitude mapping when implementing and modelling class in thermodynamic material based on observation and questionnaires were categorized as good for all students.
On the Benefits of using Dedicated Models in Validation Processes for Behavioral Specifications

Background: In model-based engineering models need to be regularly validated by manual assessment. Therefore, review processes have established. This commonly means that a visual inspection of the respective models is conducted. Aims: In this paper, we report a study that aims at investigating whether automatically generated review models can aid the manual review of model-based specifications. We investigate this for the case of embedded systems' functional design. Method: For that purpose, we compared the manual review of the functional design with the review of an automatically generated review model. In this paper, we report on a controlled experiment to compare effectiveness, efficiency, user confidence, and subjective supportiveness of both review artifacts. Results: The experiment results show that the use of the review model as review artifact for the functional design is significantly more effective, leads to a significantly higher user confidence in decision making and is valued as significantly more supportive than the review of the original functional design. Conclusions: Our experiment provides evidences that reviewing a generated review model instead of the original model-based specification of the functional design increases the quality of the reviews. Our findings also indicate that the use of generated review models have the potential to improve the review of model-based specifications in general.
Corporate Technologies and the Tech Nirvana Fallacy

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On the Performance of Hierarchical Temporal Memory Predictions of Medical Streams in Real Time

Machine learning is widely used on stored data, recently it is developed to model real time streams. Applying machine learning on medical streams might lead to a breakthrough on emergency and critical care through online predictions. Modeling real time streams implies limitations to the current state-of-the-art of machine learning and requires different learning paradigm. In this paper, we investigate and evaluate two different machine learning paradigms for real time predictions of medical streams. Both the hierarchical temporal memory (HTM) and long short-term memory (LSTM) are employed. The performance assessment using both algorithms is provided in terms of the root mean square error (RMS) and mean absolute percentage error (MAPE). HTM is found advantageous as it provides efficient unsupervised predictions compared to the semi-supervised learning supported by LSTM in terms of the error measures.
Pitfalls and drawbacks in engineering education in Russia

The fi nancial and economic crisis of [2008][2009] was the fi rst call, which was to make the Russian society to focus on important issues, one of which is the quality of education as a basis for creating and sustaining an innovation ecosystem.Investment in education and the development of the necessary competencies are crucial in improving the competitiveness of the country: these are the competences of graduates that determine the potential productivity of the territory.Russia is facing the challenge of creating the type of economy that would enable attracting highly educated and skilled workers.This initiative is refl ected in the federal document -the Conceptual of the Federal Action Programme for the Development of Education for 2016-2020 [1], which determines the quality and competitiveness of the Russian education as the prerogative of the state policy.The paper describes the current state of the educational market on the territory of the Bologna process members.It provides a comparative analysis of the effectiveness of the system of higher education in Russia with countries leading in the Global Innovation Index.The authors determined the range of problems associated with the 'insufficient effectiveness' of the educational system in Russia.Moreover, the authors proposed the view for establishing the policy aiming at development of innovative ecosystem.
Effects of a Virtual Human Appearance Fidelity Continuum on Visual Attention in Virtual Reality

In this contribution we studied how different rendering styles of a virtual human impacted users' visual attention in an interactive medical training simulator. In a mixed design experiment, 78 participants interacted with a virtual human representing a sample from the non-photorealistic (NPR) to the photorealistic (PR) rendering continuity. We presented five rendering style samples scenarios, namely low fidelity all Pencil Shaded (APS), Low to Mid Fidelity Pencil Shader on virtual patient (VP) only (PS), Mid Fidelity All Cartoon Shaded (ACT), Mid to High Fidelity Cartoon Shader on VP only (CT), and relatively High Fidelity Human Like (HL) appearance, and compared how visual attention differed between groups of users. For this study, we employed an eye tracking system for collecting and analyzing users' gaze during interaction with the virtual human in a failure to rescue medical training simulation. Results suggests that users may spend more time in the simulations on the non-realistic fidelity continuum that necessarily do not involve interaction with the virtual human. However, users preferred visually attending to virtual humans in the middle and high fidelity visual appearance conditions when engaging virtual humans in simulated social face-to-face dialogue as compared to the other conditions
Digital forensic artifacts of the Your Phone application in Windows 10

Your Phone is a Microsoft system that comprises two applications: a smartphone app for Android 7 + smartphones and a desktop application for Windows 10/18.03+. It allows users to access their most recent smartphone-stored photos/screenshots and send/receive short message service (SMS) and multimedia messaging service (MMS) within their Your Phone-linked Windows 10 personal computers. In this paper, we analyze the digital forensic artifacts created at Windows 10 personal computers whose users have the Your Phone system installed and activated. Our results show that besides the most recent 25 photos/screenshots and the content of the last 30-day of sent/received SMS/MMS, the contact database of the linked smartphone(s) is available in a accessible SQLite3 database kept at the Windows 10 system. This way, when the linked smartphone cannot be forensically analyzed, data gathered through the Your Phone artifacts may constitute a valuable digital forensic asset. Furthermore, to explore and export the main data of the Your Phone database as well as recoverable deleted data, a set of python scripts – Your Phone Analyzer (YPA) – is presented. YPA is available wrapped within an Autopsy module to assist digital practitioners to extract the main artifacts from the Your Phone system.
Modeling with Thinging for Intelligent Monitoring System

Global positioning technology combined with a satellite navigation system has many advantages and reduces the negative effects of many problems; nevertheless, the technology is still relatively new and raises many issues regarding its specification and design levels. More progress is needed to build the involved system's foundation. This paper focuses on conceptual modeling of tracking systems where object-oriented methods and languages are typically used to produce a description of the system. Such a description serves such purposes as documentation and management and as a guide for the subsequent system design phase. The paper applies a new model, Thinging Machine (TM), as a diagrammatic tool to describe notions and concepts of tracking systems. To substantiate TM's applicability in this area, the model is utilized to set up a tracking and management system for a public transportation fleet through the installation of a tracking device on each bus in the fleet. The results point to the viability of applying the TM model in this type of application.
#Drink Or #Drunk

The understanding of alcohol consumption patterns, especially those indicating negative drinking behavior, is an important issue to researchers and health policymakers. On social media, people share daily activities, including alcohol consumption, representing these moments through images and text. This work, using a five-year dataset from Instagram, analyzes what machine-extracted textual and visual cues reveal about trends of casual drinking (concepts gathered around #drink) and possible negative drinking (concepts gathered around #drunk). Our analysis reveals that #drunk posts occur more frequently in party occasions and nightlife locations, with a higher presence of people, while #drink posts occur at food locations, with a higher presence of drink containers. Manual coding further shows that #drunk posts have a higher chance of being perceived as potentially objectionable. A random forest classifier shows that #drink and #drunk posts can be discriminated with accuracy up to 82.3%. These results have important implications for alcohol research among youth.
Forty years of Computers and Chemical Engineering: Analysis of the field via text mining techniques

Since its launch in 1977, Computers & Chemical Engineering has published numerous papers on the application of computing technology to chemical engineering problems. In this paper, we present a topic analysis of the journal using various text mining techniques. In particular, we examine the dramatic growth of the journal’s topic coverage since the 1970s. We found that the journal exhibits a similar popularity across all 18 topics covered today. Certain topics have grown rapidly in the last decade, and are now among the journal’s most covered topics. We also studied the relationship between topic coverage and citation count. According to our results, the highest cited articles have similar characteristics and are often unique.