Reviewerly/exHarmony huggingface dataset:
An Improved Version of the Fuzzy Set Based Evolving Modeling with Multitask Learning

This paper introduces two novel contributions to the online learning algorithm called Fuzzy set Based evolving Modeling with Multitask Learning (FBeM_MTL), the first algorithm in the literature to consider multitask learning in the context of data stream, adaptive and evolving systems. In this new version, the degree of intersection of the information granules is directly used to define a real-valued matrix representing the relationship among the learning tasks, responsible for defining the parameters of the consequent part of all functional IF-THEN fuzzy rules. Unlike the original FBeM_MTL, in this new version, we eliminated the need for the binarization of the matrix representing the connected rules, guiding to both performance improvement and reduction in the number of user-defined parameters. The second contribution is the adoption of the Weighted Least Squares (WLS) method to define the parameters of the consequent part of the rules, using the similarity measure between every pair of samples to the mean point to set their corresponding weights in the WLS problem. Computational experiments on time series prediction of weather temperature, rain precipitation, wind speed in eolian farms and stock exchange are used to validate the performance of this new version. When compared to the original FBeM_MTL and also to several other state-of-the-art evolving systems in the literature, our approach guides to competitive results using a reduced number of parameters.
Analyzing Worldwide Social Distancing through Large-Scale Computer Vision

In order to contain the COVID-19 pandemic, countries around the world have introduced social distancing guidelines as public health interventions to reduce the spread of the disease. However, monitoring the efficacy of these guidelines at a large scale (nationwide or worldwide) is difficult. To make matters worse, traditional observational methods such as in-person reporting is dangerous because observers may risk infection. A better solution is to observe activities through network cameras; this approach is scalable and observers can stay in safe locations. This research team has created methods that can discover thousands of network cameras worldwide, retrieve data from the cameras, analyze the data, and report the sizes of crowds as different countries issued and lifted restrictions (also called ''lockdown''). We discover 11,140 network cameras that provide real-time data and we present the results across 15 countries. We collect data from these cameras beginning April 2020 at approximately 0.5TB per week. After analyzing 10,424,459 images from still image cameras and frames extracted periodically from video, the data reveals that the residents in some countries exhibited more activity (judged by numbers of people and vehicles) after the restrictions were lifted. In other countries, the amounts of activities showed no obvious changes during the restrictions and after the restrictions were lifted. The data further reveals whether people stay ''social distancing'', at least 6 feet apart. This study discerns whether social distancing is being followed in several types of locations and geographical locations worldwide and serve as an early indicator whether another wave of infections is likely to occur soon.
Rethinking the Objectives of Extractive Question Answering

This work demonstrates that, contrary to a common belief, using the objective with independence assumption for modelling the span probability $P(a_s,a_e) = P(a_s)P(a_e)$ of span starting at position $a_s$ and ending at position $a_e$ has adverse effects. Therefore we propose multiple approaches to modelling joint probability $P(a_s,a_e)$ directly. Among those, we propose a compound objective, composed from the joint probability while still keeping the objective with independence assumption as an auxiliary objective. We find that the compound objective is consistently superior or equal to other assumptions in exact match. Additionally, we identified common errors caused by the assumption of independence and manually checked the counterpart predictions, demonstrating the impact of the compound objective on the real examples. Our findings are supported via experiments with three extractive QA models (BIDAF, BERT, ALBERT) over six datasets and our code, individual results and manual analysis are available online.
Reputable List Curation from Decentralized Voting

Abstract Token-curated registries (TCRs) are a mechanism by which a set of users are able to jointly curate a reputable list about real-world information. Entries in the registry may have any form, so this primitive has been proposed for use—and deployed—in a variety of decentralized applications, ranging from the simple joint creation of lists to helping to prevent the spread of misinformation online. Despite this interest, the security of this primitive is not well understood, and indeed existing constructions do not achieve strong or provable notions of security or privacy. In this paper, we provide a formal cryptographic treatment of TCRs as well as a construction that provably hides the votes cast by individual curators. Along the way, we provide a model and proof of security for an underlying voting scheme, which may be of independent interest. We also demonstrate, via an implementation and evaluation, that our construction is practical enough to be deployed even on a constrained decentralized platform like Ethereum.
Acoustic Emission Recognition Based on Spectrogram and Acoustic Features

In order to improve the level and efficiency of fault diagnosis, an acoustic emission recognition method based on spectrum and acoustic features is proposed. The proposed system is composed of CNN and BiLSTM networks. Firstly, the amplitude spectrum and group delay phase spectrum of AE signals are extracted, and the amplitude-phase spectrum composed of the two extracted spectrum is input into CNN network to obtain the global features of AE signals. Secondly, the acoustic features such as short-term energy, zero crossing rate and kurtosis of AE signals are extracted to obtain the features of AE signals Finally, the features extracted from CNN network and BiLSTM network are fused to get the fused features, which are classified and recognized by softmax, so as to realize acoustic emission recognition. Simulation results show that the performance of the proposed system is improved by more than 17% compared with the other algorithm, and the effectiveness of the feature fusion model is verified by experiments.
Using Augmented Reality and Step by Step Verification in Industrial Quality Control

Quality control procedures are extremely important among industrial applications. Generally, these tasks include many repetitive tasks that require manual intervention. Given their complexity, quality control tests are often detailed in video recordings, paper instructions, photos or diagrams to guide workers throughout the process. Augmented Reality (AR) has been making significant progress in the last decades, becoming mature enough to be used in industrial scenarios. While some AR systems have been proposed to support quality control procedures, most of them only present information to workers but do not track or validate the process in real-time being used only to guide it. Another limitation of existing systems is the generation of virtual instructions used by AR systems to guide the operator. In this work, we propose an AR-based tool to guide users by overlaying information in a video stream while performing real-time validation during the execution of quality control procedures. The main objective is to provide dynamic support and decrease the mental workload needed to complete the procedure as well as the number of errors, facilitating the procedure execution by untrained workers. Besides this, the tool allows to create virtual content that can be used to generate step-by-step instructions automatically based on human demonstrations. By making the virtual instruction creation effortlessly it is possible to eliminate the user’s need for memorizing new instructions with each change of the product lines. While presenting task relevant information the system uses computer vision techniques to keep track of the procedure stage, verifying its completion and switching automatically to the next step without requiring any interaction from the user. A comparison between the time taken to perform the procedure with and without validation was made. The results show that the validation process would confer the process a significant efficiency boost, while avoiding possible human errors.
WAAT: A Workstation AR Authoring Tool for Industry 4.0

In this paper, we present WAAT, an AR authoring tool dedicated to industrial environments. The interest of such a tool in an industrial context is to allow non-expert users to quickly create and dynamically modify 3D models of workstations, and also test the AR guidance placement. WAAT makes on-site authoring possible, which should really help to have an accurate 3D representation of assembly lines. The verification of AR guidance should also be very useful to make sure everything is visible and doesn’t interfere with technical tasks. We deployed WAAT in an assembly line of an elm.leblanc/Bosch boiler factory to assess its features in an ecological context. We also made a comparison to another AR authoring tool to better estimate its advantages.
Experimental quantum decoherence control by dark states of the environment

Abstract Coherent interaction of a quantum system with environment usually induces quantum decoherence. However, remarkably, in certain configurations the coherent system–environment coupling can be simultaneously explored to engineer a specific dark state of the environment that eliminates the decoherence. Here we report on experimental demonstration of such protocol for suppression of quantum decoherence by quantum decoherence itself. The protocol is based on indirect control of the environment via quantum measurements on quantum probes interacting with the environment prior to the system that should be protected. No direct manipulation with the environment is required to suppress the decoherence. In our proof-of-principle experiment, we demonstrate protection of a single qubit coupled to another single qubit. We implement the required quantum circuits with linear optics and single photons, which allows us to maintain very high degree of control and flexibility in the experiment. Our results clearly confirm the decoherence suppression achieved by the protocol and pave the way to its application to other physical platforms.
The Road Not Taken: Re-thinking the Feasibility of Voice Calling Over Tor

Abstract Anonymous VoIP calls over the Internet holds great significance for privacy-conscious users, whistle-blowers and political activists alike. Prior research deems popular anonymization systems like Tor unsuitable for providing the requisite performance guarantees that real-time applications like VoIP need. Their claims are backed by studies that may no longer be valid due to constant advancements in Tor. Moreover, we believe that these studies lacked the requisite diversity and comprehensiveness. Thus, conclusions from these studies, led them to propose novel and tailored solutions. However, no such system is available for immediate use. Additionally, operating such new systems would incur significant costs for recruiting users and volunteered relays, to provide the necessary anonymity guarantees. It thus becomes an imperative that the exact performance of VoIP over Tor be quantified and analyzed, so that the potential performance bottlenecks can be amended. We thus conducted an extensive empirical study across various in-lab and real world scenarios to shed light on VoIP performance over Tor. In over half a million calls spanning 12 months, across seven countries and covering about 6650 Tor relays, we observed that Tor supports good voice quality (Perceptual Evaluation of Speech Quality (PESQ) &gt; 3 and one-way delay &lt; 400 ms) in more than 85% of cases . Further analysis indicates that in general for most Tor relays, the contentions due to cross-traffic were low enough to support VoIP calls, that are anyways transmitted at low rates (&lt;120 Kbps). Our findings are supported by concordant measurements using iperf that show more than the adequate available bandwidth for most cases. Hence, unlike prior efforts, our research reveals that Tor is suitable for supporting anonymous VoIP calls.
Developing Pop-Up Book Based Media To Improve The First Grader Students’ Learning Achievement on The Theme of Natural Event of Min 4 Jember

Teacher creativity to create a fun teaching and learning atmosphere plays an important role in developing the potential of students. For example, teachers can develop an effective learning media to engage students in meaningful learning. However, few teachers can develop learning media as is the case in MIN 4 Jember. From the preliminary study, they found it difficult to develop an effective instructional media which caused student learning outcomes low. Thus, this study aims to develop an effective pop-up book based media to improve the learning outcomes of natural event themes of students in class I of MIN 4 Jember. The present research employs the development study by modifying the Brog and Gall development model. The model consists of several stages, namely: finding a problem; collecting data; design / product development; design / product validation; design / product revision; and trial use. The final product of this research is in the form of pop-up book based media developed from two aspects, material aspects and media aspects. The results of the validity trials obtained from experts on learning materials were 88% and experts on learning media were 82% with valid qualifications. The t-related test results also showed a significant difference between the acquisition of the learning outcomes of the control class and the experimental class that was 25%. It can be concluded that this pop-up book based media is appropriate for use in learning and is effective in improving the first grader students learning outcomes of natural event theme of MIN 4 Jember.
Adversarial Privacy Preserving Graph Embedding against Inference Attack

Recently, the surge in popularity of Internet of Things (IoT), mobile devices, social media, etc. has opened up a large source for graph data. Graph embedding has been proved extremely useful to learn low-dimensional feature representations from graph structured data. These feature representations can be used for a variety of prediction tasks from node classification to link prediction. However, existing graph embedding methods do not consider users' privacy to prevent inference attacks. That is, adversaries can infer users' sensitive information by analyzing node representations learned from graph embedding algorithms. In this paper, we propose Adversarial Privacy Graph Embedding (APGE), a graph adversarial training framework that integrates the disentangling and purging mechanisms to remove users' private information from learned node representations. The proposed method preserves the structural information and utility attributes of a graph while concealing users' private attributes from inference attacks. Extensive experiments on real-world graph datasets demonstrate the superior performance of APGE compared to the state-of-the-arts. Our source code can be found at https://github.com/uJ62JHD/Privacy-Preserving-Social-Network-Embedding.
Solving a Modified Syndrome Decoding Problem using Integer Programming

In this article, we model a variant of the well-known syndrome decoding problem as a linear optimization problem. Most common algorithms used for solving optimization problems, e.g. the simplex algorithm, fail to find a valid solution for the syndrome decoding problem over a finite field. However, our simulations prove that a slightly modified version of the syndrome decoding problem can be solved by the simplex algorithm. More precisely, the algorithm returns a valid error vector when the syndrome vector is an integer vector, i.e.,the matrix-vector multiplication, is realized over Z, instead of Fq.
Named Entity Disambiguation at Scale

Named Entity Disambiguation (NED) is a crucial task in many Natural Language Processing applications such as entity linking, record linkage, knowledge base construction, or relation extraction, to name a few. The task in NED is to map textual variations of a named entity to its formal name. It has been shown that parameter-less models for NED do not generalize to other domains very well. On the other hand, parametric learning models do not scale well when the number of formal names expands above the order of thousands or more. To tackle this problem, we propose a deep architecture with superior performance on NED and introduce a strategy to scale it to hundreds of thousands of formal names. Our experiments on several datasets for alias detection demonstrate that our system is capable of obtaining superior results with a large margin compared to other state-of-the-art systems.
Soil Salinity Detection in Semi-Arid Region Using Spectral Unmixing, Remote Sensing and Ground Truth Measurements

Soil salinity is one of the serious environmental problems ravaging the soils of arid and semi-arid region, thereby affecting crop productivity, livestock, increase level of poverty and land degradation.Hyperspectral remote sensing is one of the important techniques to monitor, analyze and estimate the extent and severity of soil salt at regional to local scale.In this study we develop a model for the detection of salt-affected soils in arid and semi-arid regions and in our case it's Ghannouch, Gabes.We used fourteen spectral indices and six spectral bands extracted from the Hyperion data.Linear Spectral Unmixing technique (LSU) was used in this study to improve the correlation between electrical conductivity and spectral indices and then improve the prediction of soil salinity as well as the reliability of the model.To build the model a multiple linear regression analysis was applied using the best correlated indices.The standard error of the estimate is about 1.57 mS/cm.The results of this study show that hyperion data is accurate and suitable for differentiating between categories of salt affected soils.The generated model can be used for management strategies in the future.
An Algorithm for Detecting the Minimal Sample Frequency for Tracking a Preset Motion Scenario

Inertial sensors are used for human motion capture in a wide range of applications.Some kinds of human motion can be tracked by inertial sensors incorporated in smartphones or smartwatches.However, the latter can scarcely be used if misclassification of user activities is highly undesirable.In this case electronics and embedded software engineers should design, implement and verify their own human motion capture embedded systems, and oftentimes they have to do so from scratch.One of the issues the engineers should face is selection of suitable components, primarily accelerometers, gyroscopes and magnetometers, after thorough examination of commercially available items.Among technical characteristics of inertial sensors their sample frequency determines whether the sensor will be able to capture a specific motion kind or not.We propose a novel algorithm that allows the researcher or embedded software engineer to calculate the minimal sample frequency sufficient for tracking a prescribed motion scenario without significant signal losses.The algorithm utilizes the Poisson equation for motion of a triaxial rigid body, the Shoemake's algorithm for interpolating quaternions on the unit hypersphere, and the frequency analysis of a discrete-time signal.One can use the proposed algorithm as an argument for acceptance or rejection of a gyroscope when selecting hardware components for a human motion tracking system.
Simple High-Level Code For Cryptographic Arithmetic

We introduce an unusual approach for implementing cryptographic arithmetic in short high-level code with machinechecked proofs of functional correctness. We further demonstrate that simple partial evaluation is sufficient to transform such initial code into highly competitive C code, breaking the decades-old pattern that the only fast implementations are those whose instruction-level steps were written out by hand. These techniques were used to build an elliptic-curve library that achieves competitive performance for a wide range of prime fields and multiple CPU architectures, showing that implementation and proof effort scales with the number and complexity of conceptually different algorithms, not their use cases. As one outcome, we present the first verified highperformance implementation of P-256, the most widely used elliptic curve. Implementations from our library were included in BoringSSL to replace existing specialized code, for inclusion in several large deployments for Chrome, Android, and CloudFlare. This is an abridged version of the full paper originally presented in IEEE S&amp;P 2019 [10]. We have omitted most proof-engineering details in favor of a focus on the system's functional capabilities.
Javanese Cultural Paradoxism: A Visual Semiotics Study on Wayang Purwa Characters of Satria and Raseksa Figures

This study aims to explore the paradoxical character values on visual semiotics wayang purwa of Raseksa and Satria profiles. Satria and Raseksa figures were selected using a purposive sampling technique, through wayang Pandavas and Kuravas. The study was designed with qualitative research approach; the data were collected using the techniques of interviews, observation, and document analysis. The data were analyzed following the steps of data reduction, data display, and verification. The results of the research revealed that in wayang, the Raseksa and Satria figures represent a paradoxical representation of both the external structure and the inner structure. The outer structure is in the form of wayang purwa visual elements, which turns out to be the opposite shape from both Pandavas and Kuravas figures. The inner structure is more about the inseparable meaning and value from the outer structure. In semiotics, the visual element (outer structure) of Pandavas and Kuravas figures can be represented or interpreted based on their values and meanings (inner structure) in addition to show a conflict of meaning. The paradoxical cultural value in Javanese culture is essentially learning experienced-based humanity values manifested through puppet characters. This is exemplified by the Islamic example of the Prophet Muhammad and its mandatory and impossible natures. The wayang Satria may represent the obligatory nature (good character), and wayang Raseksa represent the impossible (bad character) of humans.