Reviewerly/exHarmony huggingface dataset:
Industrial Process Fault Classification Based on Weighted Stacked Extreme Learning Machine

Stacked extreme learning machine (SELM) has emerged as an effective industrial process fault classification method. However, complex industrial processes usually involve a large number of variables, which may have different effects on fault classification performance. To distinguish process variables' effects on fault classification result, we propose a partial F value based weighted stacked extreme learning machine (FSELM). Firstly, the partial F value method is utilized to analyze the importance of process variables. Then, the weight of each process variable is determined based on contribution analysis. Lastly, SELM is utilized to learn the projection from the weighted process variables to fault types. Simulation results on the Tennessee Eastman process show that, the classification accuracy of the proposed method is higher, compared to the extreme learning machine and the SELM.
Artificial Intelligent ChatBot for Food Related Question

This work wants to simulate dialogue with an artificial intelligent ChatBot, which reading the questions from clients, analysing each word and searching for a keyword tries to build the correct answer. The application’s domain chosen concerns a culinary context, in which our ChatBot represents the waiter, ready to take orders and give some more information about the restaurant. The user will be able to: Order one or more courses from the menu, get restaurant’s information about daily opening and closing times, get restaurant’s information about taking order away, get the full list containing his/her orders.
Intelligent Virtual Lipstick Trial Makeup Based on OpenCV and Dlib

In order to try different lipstick products in a reusable, low-cost, and hygienic manner, an intelligent virtual lipstick trial algorithm is proposed with assistance of Dlib and OpenCV. In the proposed method, we perform face detection through the pre-trained face detection model of dlib.get_frontal_face_detector in the Dlib library, and then extract feature points from the face in the video according to the above pre-trained model. When lips and other important face parts are recognized, they are then filled with specific color patterns according to lighting environment, highlighting the import characteristics for target lipstick, making it reliably displayed on a smart phone screen. The process is full of simplicity, quickness and convenience.
Dynamic fringe analysis using GPU assisted root-MUSIC method

Estimation of phase and its derivatives from fringe patterns, commonly referred to as fringe analysis is an important step in many non-destructive optical measurement techniques such as Fringe projection, Electronic speckle pattern interferometry (ESPI) and Digital holographic interferometry (DHI). Some of the applications of these techniques include deformation analysis, stress analysis, profilometry and defect detection. Here, the quantities of interest like displacement, stress and refractive index are encoded in the form of phase and phase derivatives in the recorded fringe patterns. In dynamic systems, large number of such fringe patterns are recorded necessitating the use of reliable and fast methods for fringe analysis. In this work, we proposed a GPU assisted subspace based method called multiple signal classification (MUSIC) for estimating both the phase and its derivatives. The method relies on noise subspace to calculate a polynomial equation whose roots are computed numerically. Both the phase and its derivatives are computed by selecting a relevant root according to the stability conditions. The performance of the proposed method was tested using 250 simulated fringe patterns with gradually increasing phase at signal to noise ratio of 5 dB. Collective processing of all 250 fringe patterns using Python’s Numpy library took approximately 14535 seconds whereas the graphics processing unit (GPU) had taken only 260 seconds, thus resulting in substantial reduction in execution time. These results show that the proposed method is robust against noise and the use of GPU makes it computationally very efficient.
A Continuous Probabilistic Origin Association Filter for Extended Object Tracking

One major challenge in extended object tracking is the association of a point measurement to its true origin on a target object. The origins of measurements are often spatially distributed over the full extent of the target. The association of measurements to the possible origins within the targets’ extent is difficult, especially for low-resolution sensors which provide only a few measurements per object. We address this using a soft association of a point measurement to its origin candidates on the target. Therefore, association probabilities to different possible origins are calculated for each measurement. These probabilities are weighted according to their probability in the filtering step. We also extend this filter to continuous and not just discrete association possibilities. This allows us to associate point measurements to lines.This paper outlines the derivation of the filter and gives three exemplary applications. A simulation compares the performance of this approach with other filter techniques for tracking a moving line. The transfer of the filter to a moving circle is discussed. Additionally, we discuss its usage for a Doppler-radar-based detection association which exploits the radial speed information. We discuss the advantages and the drawbacks of this approach and give recommendations for the optimization of computation time.
Research and Design of Context UX Data Analysis System

At present, user researchers have problems in UX (User experience) data analysis, such as low efficiency, inaccurate context data identification, and low satisfaction of analysis process. Therefore, in order to solve these problems, this paper proposes a design context UX data analysis system to compensate for the shortcomings in the data analysis process. This paper takes the analysis of UX data collected by the CAUX (Context-Aware User Experience) tool as an example, using the relevant methods in Cognitive Task Analysis (CTA), and on the basis of sensemaking loop model, explore the data analysis process of UX researchers through experiments. And carry out demand research for each stage of the analysis process, design a context UX data analysis system according to the requirements. This thesis summarizes the model of UX data analysis process, completes the design of context UX data analysis system, and evaluation experiment proves that the system can effectively solve the problems in the UX data analysis process, and provides a new idea for the UX research practice in the mobile Internet environment.
The Way to Improve the Consciousness for Water Resources Protection of Chinese College Students

Xiong, W.J., 2020. The way to improve the consciousness for water resources protection of Chinese college students. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 48–53. Coconut Creek (Florida), ISSN 0749-0208.Waste and pollution of water resources have always been a hot issue of social concern. In recent years, governments of all countries have been increasing their efforts in water resources protection legislation. Taking China as an example, the government has formulated strict water resources protection laws to control water resources pollution, but the results are not very good. The research of this paper is carried out under this background. This paper studies the college students, a group widely existing in China, hoping to let this group participate in the protection of water resources. This paper puts forward many methods, which have been proved to be of significance in theory and practice.
On the importance of pre-training data volume for compact language models

Recent advances in language modeling have led to computationally intensive and resource-demanding state-of-the-art models. In an effort towards sustainable practices, we study the impact of pre-training data volume on compact language models. Multiple BERT-based models are trained on gradually increasing amounts of French text. Through fine-tuning on the French Question Answering Dataset (FQuAD), we observe that well-performing models are obtained with as little as 100 MB of text. In addition, we show that past critically low amounts of pre-training data, an intermediate pre-training step on the task-specific corpus does not yield substantial improvements.
Realizing Body-Machine Interface for Quadrotor Control Through Kalman Filters and Recurrent Neural Network

Unmanned Aerial Vehicles (UAV) have been recently applied in several various civilian applications. Based on this, there is a growing need for intuitive UAV control interfaces. In this work, we report on the Body-Machine Interface (BMI), helping a human operator to control a quadrotor through the gesture commands. We perform the human motion capture through wearable sensors and Kalman filter to reduce the noise. For the gesture command recognition, we designed the Recurrent Neural Network recognizing gestures within 65 ms. For the quadrotor orientation estimation, we designed the Extended Kalman Filter (EKF). We assess the proposed BMI via the simulations and experiments: the standard deviation of the trajectories varies for up to 10 cm.
An Examination of Regional Tech Meetups Found on Meetup.com

This study grew out of the Amazon announcement in 2017 that it would create a second headquarters with most of the competing cities being in the East-Midwest section of the United States. The city of Cincinnati threw in its hat as did another 237 cities. Cincinnati was eventually eliminated in part because 'it was not technical' enough according to Amazon. Amazon narrowed down the 238 'contestants' to 20. Part of the author's research has been on the technical community within the Cincinnati area and the Amazon new headquarters search got one wondering 'just how does Cincinnati compare to other cities'? This study looks at cities within 1000km radius of Cincinnati plus the other US members of the Amazon20. This report looks at only those IT organizations that were found on Meetup.com and covered a total of 68 cities mostly in the Midwest and Eastern US.
Text classification algorithm based on sparse distributed representation

The effect of automatic text classification depends on training data to a great extent. However, the actual data often contains noise. It is often difficult, expensive or time consuming to improve the quality of data without noise at all. Aiming at this problem, a novel text classification algorithm is proposed based on sparse distributed representation (SDR) which is extremely tolerant to noise. The algorithm first created class-SDR for each class label by merging category feature vectors with the subsample technique. Then, the algorithm assigns a class label for a document by comparing the overlap value of SDR with class-SDRs. The experimental results show that the algorithm has better performance in classification with noise training data compared with six frequently used text classification algorithms.
Social Antinomies of Linguistic Consciousness

This article focuses on the debate over Crimea's accession. The content analysis relies on data collected during the first and most turbulent year of Crimea's incorporation, which started with the decision to conduct a referendum on the Crimean status and then to declare Crimea's independence in March 2014. The sample consists of 50 entries published on LiveJournal, both posts and commentaries. We have discovered and problematized severe disagreements in bloggers' worldview that give rise to the antinomies of bloggers' linguistic consciousness. By this, we mean the use of words with opposite connotations relating to the same event within the same blog and an inconsistency between bloggers' perception of the event and the affective meanings of lexical items attached to it. Our main point is that Crimea's accession prompts bloggers to reduce this dissonance by “rolling up the semantic rainbow,” that is, by destroying meanings with rigid binary semantic opposition, which thereby further exacerbates deep-rooted divisions within Russian society where patriots and liberals increasingly keep apart.
Trajectory-aware playback to overcome user experience challenges for streaming media in the vehicle

New trends in the automotive industry, such as emobility and autonomous driving, give birth to challenges of engaging passengers in a vehicle. Once relieved from driving tasks, passengers may take up productive and entertainment-related activities, such as media consumption. It is assumed that by 2030 in-car cabin would mostly resemble a living room, whereas internet connectivity would become one of the most important enablers for new content delivery. Streaming media in such an environment poses a challenge, given that reliability and adequate coverage in 4G and coming 5G networks are mandatory. In this paper, we give a brief analysis of challenges which streaming media would bring to the user experience. We give a proposal of a software stack for a trajectory-aware playback, capable of predicting streaming quality drops and proposing appropriate fallbacks.
WEB ARCHIVING AS CULTURE: TUMBLR AND THE CULTURAL CONSTRUCTION OF THE ARCHIVED WEB

Web archives - broadly conceived as any attempt to capture and preserve the Web for future use - are evermore central to discussions of digital access in the public sphere, as they provide tools for accessing parts of the Web that have been subject to neglect, removal or state and platform-based forms of content moderation and censorship. In this paper I discuss the cultural significance of web archiving through the example of Tumblr’s 2018 efforts to remove so-called ‘Not Safe for Work’ (NSFW) posts from the platform. The paper examines the archiving of Tumblr NSFW by Archive Team, a self-described ‘loose collective of rogue archivists, programmers, writers and loudmouths dedicated to saving our digital heritage’. Findings are presented through the concept of culture which provides a dual lens through which to understand web archiving practices as contingent upon the cultural worlds which they create and operate within. Here, web archiving as culture reveals the ways that practices shape (and are shaped by) online community membership, the nature of how and why the Web is archived and the reflexive significance participants place on their own web archival activities. The paper contributes to broader discussions of online community formation and raises further questions about the ethics and role of power in the production of web archives, as well as their positioning as historical representations of online cultures.
AndroidPropTracker

Android operating system introduces new releases frequently. This fact led to the existence of several Android Application Programming Interfaces (APIs) which is one of the causes of the Android fragmentation phenomenon. As a consequence of fragmentation, many apps became not ready for new Android releases. Aiming at investigation of the readiness of Android apps, we developed a software repository mining tool to understand how ready apps are (and were) for Android releases. The tool tracks the changes of Android projects properties over time, contributing for a deeper analysis through collecting data since the beginning of the projects. It allows researchers to examine when exactly Android properties were changed, how many times they were changed, as well as all their values along time. This mechanism can support researchers to understand the evolution of Android projects and to answer research questions. In addition, developers can use the tool to track their apps evolution and perform comparisons and analysis with other open source apps. The tool can help developers to have a broader view of their apps evolution as well as to analyze competitor apps evolution.
‘Music and Language Tilt into Each Other’: Annette Schmucki Interviewed by Alistair Zaldua

Swiss composer Annette Schmucki (*1968) has written compositional work for the concert hall, a laptop duet with Petra Ronner (band) and collaborative radio plays (Hörspiele) with Reto Friedmann (blablabor). In this interview, she describes her work as related to, but essentially different from, the ideas presented and exemplified by Cornelius Schwehr. Responding to Schwehr’s article on the topic of music and language, during this discussion Schmucki explains her aesthetic position, her lifelong engagement with music and language, and the ways that these concerns translate practically into her compositional approach. In relation to this, Schmucki describes her growing distance to the traditional composer/performer divide, in preference for her increasing interest in collaborative work. One of the main consequences of this has been the ways that her scores are notated, becoming more instruction-based and inviting her performers to share responsibility for the piece. In 2018, Lauren Redhead and Alistair Zaldua commissioned Schmucki to write a piece for organ and live electronics. The piece that ensued, a discussion of which concludes this interview, is 54 stops/gresillement/alphabet des rauschens (2019). As a score that consists almost entirely of instructions, the piece exemplifies her specific approach to collaborative work in the field of music and language.
Beam-ACO for the Lock Chamber Arrangement

With the development of waterway transportation in China, there are many problems to be solved in waterway transportation, among which the lock congestion is a common problem in waterway transportation in China, especially in some dams with frequent waterway transportation. Water transport congestion will not only affect the normal travel of water transport ships, but also cause certain economic losses to ships. Water transport lock congestion has seriously hindered the development of water transport in China. This paper mainly analyzed the current situation and problems of waterway transportation, and established a hybridizing ant colony optimization with beam search method for the lock chamber arrangement optimization to alleviate traffic congestion from the actual needs, implemented corresponding algorithm through further design, and tested the validity of it through the Three Gorges-Gezhou Dam lock chamber arrangement and the Qingyuan Water Conservancy lock chamber arrangement. The experimental results showed that the hybridizing ant colony optimization with beam search method is effective in application of the lock chamber arrangement for the improving the ship lock practical navigation capacity.
VLSI Implementation of ESF and QTL Lightweight Ciphers

In order to provide security in resource scare environment, various lightweight ciphers are proposed. Many optimized architectural designs for these ciphers are presented by several authors to enhance the compatibility with lightweight domain. In this work, in order to provide security in lightweight environment, three optimized hardware architectures of eight-sided fortress (ESF) and QTL ciphers are proposed. Two architectures for ESF cipher are presented based on different S-box generation techniques. RAM-based hardware design of QTL cipher is also presented. Designs are evaluated and compared in various FPGA platforms, on the basis of resource consumption, performance and power requirement for their implementation. ESF architecture with RAM-based S-box requires least resource consumption for its implementation.
Towards Hybrid Camera Sensor Simulation for Autonomous Vehicles

To accurately test and validate algorithms used in autonomous vehicles, numerous test vehicles and very large data sets are required, resulting in safety constraints and increased financial cost. For this reason, it is desired to train the algorithms at least partly in simulation. In this work we lay the focus on the camera sensor and propose a novel methodology for injecting the instance of simulated vehicles into the camera data of real vehicles. To get qualitative results and improve generalization capabilities, the simulated data must sufficiently correspond to real-world sensor data in order to prevent loss of performance when moving the model to a real environment after training. The realism of the output is evaluated by object detection systems and a realism score produced by a CNN. Results show the potential of this approach for improving hybrid simulators for the validation of autonomous vehicles.
Outdoor navigation using two quadrotors and adaptive sliding mode control

In this work, a solution to outdoor trajectory tracking using quadrotors is presented. The unmanned aerial vehicles (UAVs) navigate under the leader-follower formation, where the pose of the leader is estimated using its onboard sensors, and the pose of the follower by position-based visual servoing. To handle the outdoor environment perturbations, unmodeled vehicle dynamics, and dynamic disturbances caused by wind and varying lightning conditions, a robust sliding mode controller is used. The proposal is experimentally validated using two Parrot Bebop 2 quadrotors in an outdoor scenario. The obtained results allow concluding that the proposed control system is able to accomplish trajectory tracking under parametric uncertainties and real-world disturbances.
How to compare adversarial robustness of classifiers from a global perspective

Adversarial robustness of machine learning models has attracted considerable attention over recent years. Adversarial attacks undermine the reliability of and trust in machine learning models, but the construction of more robust models hinges on a rigorous understanding of adversarial robustness as a property of a given model. Point-wise measures for specific threat models are currently the most popular tool for comparing the robustness of classifiers and are used in most recent publications on adversarial robustness. In this work, we use recently proposed robustness curves to show that point-wise measures fail to capture important global properties that are essential to reliably compare the robustness of different classifiers. We introduce new ways in which robustness curves can be used to systematically uncover these properties and provide concrete recommendations for researchers and practitioners when assessing and comparing the robustness of trained models. Furthermore, we characterize scale as a way to distinguish small and large perturbations, and relate it to inherent properties of data sets, demonstrating that robustness thresholds must be chosen accordingly. We release code to reproduce all experiments presented in this paper, which includes a Python module to calculate robustness curves for arbitrary data sets and classifiers, supporting a number of frameworks, including TensorFlow, PyTorch and JAX.
Review of sparse sufficient dimension reduction: comment

High-dimensional data are frequently collected in a large variety of areas such as biomedical imaging, functional magnetic resonance imaging, tomography, tumor classifications, and finance. With re...