Reviewerly/exHarmony huggingface dataset:
The Fake News about Fake News

Abstract It is widely believed that we are facing a problem, caused by something called ‘fake news’. Governments and other powerful institutions around the world have adopted a variety of measures to restrict the reporting and dissemination of claims they deem to be fake news. Many of these measures are clear breaches of fundamental rights, including freedom of speech and freedom of the press. This chapter arsgues that, contrary to common opinion, there is no new or growing problem of fake news. There is instead a new and growing problem caused by the term ‘fake news’. Although this term has no fixed meaning it does have a fixed function. It functions to restrict permissible public speech and opinion in ways that serve the interests of powerful people and institutions.
Path Planning based on Improved Artificial Potential Field Method

The artificial potential field method is regarded as a widely used method in path planning. However, the application of artificial potential field method in path planning will lead to the problem of unreachable target, easy to fall into traps and low success rate. Aiming at these shortcomings, this paper improves the repulsion field function to solve the problem of unreachable target, and selects a virtual target point when it falls into a trap to make it escape the trap. The effectiveness of the method is verified by simulation.
P4DB - The Case for In-Network OLTP

In this paper we present a new approach for distributed DBMSs called P4DB, that uses a programmable switch to accelerate OLTP workloads. The main idea of P4DB is that it implements a transaction processing engine on top of a P4-programmable switch. The switch can thus act as an accelerator in the network, especially when it is used to store and process hot (contended) tuples on the switch. In our experiments, we show that P4DB hence provides significant benefits compared to traditional DBMS architectures and can achieve a speedup of up to 8x.
Real-time human action recognition using raw depth video-based recurrent neural networks

Abstract This work proposes and compare two different approaches for real-time human action recognition (HAR) from raw depth video sequences. Both proposals are based on the convolutional long short-term memory unit, namely ConvLSTM, with differences in the architecture and the long-term learning. The former uses a video-length adaptive input data generator ( stateless ) whereas the latter explores the stateful ability of general recurrent neural networks but is applied in the particular case of HAR. This stateful property allows the model to accumulate discriminative patterns from previous frames without compromising computer memory. Furthermore, since the proposal uses only depth information, HAR is carried out preserving the privacy of people in the scene, since their identities can not be recognized. Both neural networks have been trained and tested using the large-scale NTU RGB+D dataset. Experimental results show that the proposed models achieve competitive recognition accuracies with lower computational cost compared with state-of-the-art methods and prove that, in the particular case of videos, the rarely-used stateful mode of recurrent neural networks significantly improves the accuracy obtained with the standard mode. The recognition accuracies obtained are 75.26% (CS) and 75.45% (CV) for the stateless model, with an average time consumption per video of 0.21 s, and 80.43% (CS) and 79.91%(CV) with 0.89 s for the stateful one.
Bi-directional Causal Graph Learning through Weight-Sharing and Low-Rank Neural Network

Discovering the causal graph in multivariate time series data is of great importance for industrial society, yet challenging due to the unknown nonlinearity in the data. Existing works only explore the data in chronological order, and rely on pre-assumed kernels or certain distribution assumption. In this paper, we present a Bi-directional neural network for Causal Graph Learning (Bi-CGL) through weight-sharing and low-rank neural network. It discovers the causal graph by simultaneously exploring input in forward and reverse chronological order. Both directions approach the same causal graph with shared low-rank approximation, which provides robustness and better accuracy against data noise. Experiments on synthetic and real world datasets prove our Bi-CGL's outperformance over existing baselines.
Analysis of Demand-side Business Opportunities in Iran, as a Digital Transformation Perspective

The digital transformation of electrical utilities has gained an ongoing attention in recent years; It means a tremendous change using disruptive technologies (including Internet of Things (IoT), Data Analytics, Artificial Intelligence, etc.) which leads to new data-driven business opportunities. On the other hand, various projects have been conducted in this field by the Joint Research Center (JRC) in the European Union (EU), mostly focused on the demand-side, and several commercial product and services have been developed. Iran is also moving through the way of transforming its grid digitally, which would lead to the appearance of several business opportunities. In this paper, after detailed review and classification of the commercial products developed or used in EU projects, the business opportunities of Iran electricity grid digital transformation, with emphasis on demand-side management, has been discussed.
Versioning for parametric design exploration process

Many design versions are produced during design exploration. However, these versions are typically maintained manually often using an individual's self-determined logic, and thus hamper design workflow when a specific file cannot be located, similar ideas and mistakes revisited, or even loss of a potentially good design direction. These issues, in contrast, have been effectively addressed in software engineering by using a versioning system. In this work, we propose that integrating a versioning system into parametric design exploration workflow provides following benefits: (1) version control, (2) cross-iteration versions comparison, (3) feedback platform, and (4) design project analytics. A computational design workshop facilitated with our customised cloud-based versioning tool revealed student's positive responses of versioning as a personal documentation and group collaboration tool. • Versioning is needed not only for software programming (textual code), but also for parametric modelling (visual code). • We present a Grasshopper plug-in for capturing and managing design versions generated in the§design exploration phase. • Study case shows it forms saving habit, aids design exploration, provides fail-proofing, and facilitates peer-learning. • Insights on the design process can be derived from the design versions data.
SAFIT: Segmentation-Aware Scene Flow with Improved Transformer

Scene flow prediction is a challenging task that aims at jointly estimating the 3D structure and 3D motion of dynamic scenes. The previous methods concentrate more on point-wise estimation instead of considering the correspondence between objects as well as lacking the sensation of high-level semantic knowledge. In this paper, we propose a concise yet effective method for scene flow prediction. The key idea is to extend the view of all points for computing point cloud features into object-level, thus simultaneously modeling the relationships of the object-level and point-level via an improved transformer. In addition, we introduce a novel unsupervised loss called segmentation-aware loss, which can model semanticaware details to help predict scene flow more accurately and robustly. Since this loss can be trained without any ground truth, it can be used in both supervised training and self-supervised training. Experiments on both supervised training and self-supervised training demonstrate the effectiveness of our method. On supervised training, 3.8%, 22.58%, 10.90% and 21.82 % accuracy boosts than FLOT [23] can be observed on FT3Ds, KITTIs, FT3Do and KITTIo datasets. On self-supervised scheme, 48.23% and 48.96% accuracy boost than PointPWC-Net [40] can be observed on KITTIo and KITTIs datasets.
S2ORC: The Semantic Scholar Open Research Corpus

We introduce S2ORC, a large corpus of 81.1M English-language academic papers spanning many academic disciplines. The corpus consists of rich metadata, paper abstracts, resolved bibliographic references, as well as structured full text for 8.1M open access papers. Full text is annotated with automatically-detected inline mentions of citations, figures, and tables, each linked to their corresponding paper objects. In S2ORC, we aggregate papers from hundreds of academic publishers and digital archives into a unified source, and create the largest publicly-available collection of machine-readable academic text to date. We hope this resource will facilitate research and development of tools and tasks for text mining over academic text.
A Survey on Prediction of PQoS Using Machine Learning on Wi-Fi Networks

Deployment of Wi-Fi networks increases considerably as a last hop for Internet access in homes and buildings. Wi-Fi as a best-effort network cannot satisfy user expectations for carrier-grade quality and today, users consider Internet service providers responsible for Wi-Fi quality degradation. Thus, notion of perceived quality of service (PQoS) and quality of experience (QoE) have been extensively used as quality indicators of user satisfaction. This paper provides a review of state-of-the-art literature to investigate machine learning techniques for estimating PQoS in Wi-Fi networks. In this paper, only literature that attempted to estimate QoE based on network performance parameters on Wi-Fi networks have been investigated and categorized based on their problem area and machine learning techniques. The review indicated promising achievements using machine learning techniques to identify wireless problems and accordingly improve the quality. However, estimation of PQoS requires further research with recent IEEE 802.11 technology advancements and machine learning approaches.
An Energy-Efficient Human Activity Recognition System Based on Smartphones

Smartphone-based human activity recognition (HAR) has become a considerable research field as a subdomains of pattern recognition and pervasive computing. With the increasing popularity of smartphones, HAR has prominent applications in number of fields such as health care, education, entertainment and etc. Smart devices have a huge advantage in convenience as the main acquisition and processing equipment, but the battery life of smartphone and other resources are limited for long-duration tasks. In this paper, we propose a lightweight HAR system. The system realizes HAR algorithm with deep learning algorithm. Beyond that, we introduce a clustering-center based pre-classification strategy to reduce the call frequency of the DL model. Meanwhile, we add a sampling frequency control mechanism to the inertial sensor. The goal of the whole system is to achieve low power consumption and time delay. According to the final experiment results, the energy consumption reduces about 49% and time delay reduces about 55% while the overall recognition accuracy only suffers about 10% reduction.
Tune-In: Training Under Negative Environments with Interference for Attention Networks Simulating Cocktail Party Effect

We study the cocktail party problem and propose a novel attention network called Tune-In, abbreviated for training under negative environments with interference. It firstly learns two separate spaces of speaker-knowledge and speech-stimuli based on a shared feature space, where a new block structure is designed as the building block for all spaces, and then cooperatively solves different tasks. Between the two spaces, information is cast towards each other via a novel cross- and dual-attention mechanism, mimicking the bottom-up and top-down processes of a human's cocktail party effect. It turns out that substantially discriminative and generalizable speaker representations can be learnt in severely interfered conditions via our self-supervised training. The experimental results verify this seeming paradox. The learnt speaker embedding has superior discriminative power than a standard speaker verification method; meanwhile, Tune-In achieves remarkably better speech separation performances in terms of SI-SNRi and SDRi consistently in all test modes, and especially at lower memory and computational consumption, than state-of-the-art benchmark systems.
Moving forward passwordless authentication: challenges and implementations for the private cloud

Authentication is necessary anytime we need to certify our identity. The most used type of authentication is done by using a username and a password. Historically, multi-factor authentication (MFA) came as next level of authentication. It is a great way to secure your organization, but most of the users get frustrated with the additional security feature on top of having to remember their passwords. Passwordless authentication is more convenient because the password now is removed and replaced with something you have, and it cannot be forgotten. These solutions permit users to access their workstation, application, or network by using their fingerprint, eye scan or voice recognition. Passwordless authentication nowadays is used on large corporations, and environments where authentication security is very important. As novelty, the current article is presenting a Passwordless implementation solution for OpenStack private cloud. The solution can be implemented with minimal configuration changes also on other private cloud environments. A comparison between classic authentication and Passwordless is made at the end of the article with the purpose of understanding the advantages of this new authentication system.
Walking Through Normativities of Reproductive Bodies: A Method for Critical Analysis of Tracking Applications

Menstruation and fertility tracking applications are of increasing concern in HCI research, as their use becomes more widespread. Methods are needed to understand how such applications become entangled in everyday practices. While these apps promise increased self-knowledge of reproductive potential by collecting intimate data about reproductive bodies, they also restrict the knowledge produced about users’ bodies and embed normative understandings of reproduction and gender. In this paper, we scrutinize the normativities of reproductive bodies by deploying the “walkthrough method” to uncover sociotechnical entanglements of the menstruation and fertility apps Clue, Tilly, and Drip. We discuss how the walkthrough method contributes to HCI's methodological repertoire for studying intimate bodily tracking apps and unpacking their normativities. We offer suggestions for using this method to critically analyze existing apps and extend approaches to design with and for a plurality of in/fertile bodies.
Speaker identification using fuzzy i-vector tree

In this paper, we propose a new method for concurrent accuracy and computational efficiency optimization using a fuzzy clusters tree for i-vector speaker identification. The design assumptions and an algorithm for a new type of fuzzy i-vector tree construction were introduced. The obtained solution was evaluated using the NIST 2014 i-Vector Speaker Recognition Machine Learning Challenge dataset. A 15% relative equal error rate reduction for a 74% reduction in computation time was achieved when compared to the baseline with only a 5.5% relative identification rate loss for discussed tree configurations.
Utilizing Google Translated Reviews from Google Maps in Sentiment Analysis for Phuket Tourist Attractions

Well-known tourist attractions receive reviews in various languages. Google Maps has a reviewing system with translation service. The translated reviews may not perfectly represent the original review, but may still capture valuable information. This paper studies the effectiveness of nonusing translated reviews in sentiment analysis for tourist attractions in Phuket, Thailand. Reviews for beaches in Phuket and nearby islands are collected. The reviews are separated into two groups, reviews originally written in English and non-English. Rather than discarding non-English reviews or constructing models for each origin language, we experiment with different ways of utilizing the non-English reviews by translating them into English. Results show that models constructed using the reviews originally written in English are also effective for translated reviews. This can significantly reduce the effort to understand the sentiment of reviews written in various languages by training only one model and applying it to text translated from any language.
Blockchain Technologies in Practice

Suppose that you are developing a critical application that handles sensitive data. Access to the data should be limited, and data should be changed only under specific circumstances. Moreover, at any point in time you may be asked to provide an audit trail for the entire history of any piece of data, for example, the entire history of changes for a specific record. How will you make sure that you can provide the history as it happened, certain that it has not been tampered with in any way?
KORGAN: An Efficient PKI Architecture Based on PBFT Through Dynamic Threshold Signatures

Abstract During the past decade, several misbehaving certificate authorities (CAs) have issued fraudulent TLS certificates allowing man-in-the-middle (MITM) kinds of attacks that result in serious security incidents. In order to avoid such incidents, Yakubov et al. ((2018) A blockchain-based PKI management framework. NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium, Taipei, Taiwan, April, pp. 16. IEEE) recently proposed a new public key infrastructure (PKI) architecture where CAs issue, revoke and validate X.509 certificates on a public blockchain. However, in their proposal TLS clients are subject to MITM kinds of attacks, and certificate transparency is not fully provided. In this paper, we eliminate the issues of the Yakubov et al.’s scheme and propose a new PKI architecture based on permissioned blockchain with PBFT consensus mechanism where the consensus nodes utilize a dynamic threshold signature scheme to generate signed blocks. In this way, the trust to the intermediary entities can be completely eliminated during certificate validation. Our scheme enjoys the dynamic property of the threshold signature because TLS clients do not have to change the verification key even if the validator set is dynamic. We implement our proposal on private Ethereum network to demonstrate the experimental results. The results show that our proposal has negligible overhead during TLS handshake. The certificate validation duration is less than the duration in the conventional PKI and Yakubov et al.’s scheme.
Tracking of Moving Target Based on SiamMask for Video SAR System

Video Synthetic Aperture Radar (Video SAR) technology has developed rapidly in recent years, realizing the automatic tracking of moving targets has gradually become the focus of research in the field of SAR. In this work, “SiamMask”, the newly included algorithm in CVPR2019 with the highest accuracy in the field of computer vision, is applied to the tracking of Video SAR images. Firstly, the high-quality SAR images of the imaging area are obtained, and then on the basis of these data, the initial position of the moving target is acquired. Finally, the effect of SiamMask in tracking teat data is compared with the effect of the traditional tracking algorithm - KCF. The result shows that SiamMask algorithm performs better than the traditional one in dealing with test data. In addition, since the moving target's shadow does not defocus or shift, it can be used to assist tracking. The shadow data set used also verifies the feasibility of tracking moving target's shadow, which provides ideas for subsequent research.
Performance Verification of a Target Tracking System With a Laser Rangefinder

A target tracking system with a laser rangefinder is developed, and the performance of the developed system is verified by a numerical simulation and field tests. To develop the target tracking system, appropriate coordinate systems, motion models, and estimation methods are selected, and a range filter is designed to compensate for the measurement errors of the laser rangefinder. The range filter independently processes the range measurements so that the target filter continuously produces the target information. The entire target tracking system development process is described in detail to facilitate future work of a similar nature. A scenario-based Monte Carlo simulation is performed to compare the performances of various state estimation methods. Experimental tests involving a low-speed fishing boat and a drone are performed to verify the performance and effectiveness of the proposed scheme. A separate performance analysis program with a graphical user interface is developed to analyze the simulation and field test results.
Longformer for MS MARCO Document Re-ranking Task

Two step document ranking, where the initial retrieval is done by a classical information retrieval method, followed by neural re-ranking model, is the new standard. The best performance is achieved by using transformer-based models as re-rankers, e.g., BERT. We employ Longformer, a BERT-like model for long documents, on the MS MARCO document re-ranking task. The complete code used for training the model can be found on: this https URL
Security, Reliability and Test Aspects of the RISC-V Ecosystem

RISC-V has emerged as a viable solution on academia and industry. However, to use open source hardware for safety-critical applications, we need a deep understanding of the way in which well established mechanisms for testing and reliability could be integrated and deployed on the RISC-V ecosystem, and we need a clear knowledge on how such an ecosystem can be leveraged to improve security. This paper includes four contributions presenting the potential of RISC-V in security research, the way in which RISC-V can be hardened against power analysis attacks, how to implement, using RISC-V, software and hardware/software solutions for dual core lock step, and how to perform system-level testing in the RISC-V ecosystem.
Toward Multilabel Image Retrieval for Remote Sensing

The availability of large-scale remote sensing (RS) data facilitates a wide range of applications, such as disaster management and urban planning. An approach for such problems is image retrieval, where, given a query image, the goal is to find the most relevant match from a database. Most RS literature has been focused on single-label retrieval, where we assume an image has a single label. The primary challenge in single-label RS retrieval is that performance in most datasets is saturated, and it has become difficult to compare the performance of different methods. In this work, we extend the major multilabel classification datasets to the multilabel retrieval problem. We also define protocols, provide evaluation metrics, and study the impact of commonly used loss functions and reranking methods for multilabel retrieval. To this end, a novel multilabel loss function and a reranking technique are proposed, which circumvent the challenges present in conventional single-label image retrieval. The developed loss function considers both class and feature similarity. The proposed reranking technique achieves high performance with computation cost that is well-suited for fast online retrieval.