Reviewerly/exHarmony huggingface dataset:
Smartphone Location Recognition: A Deep Learning-Based Approach

One of the approaches for indoor positioning using smartphones is pedestrian dead reckoning. There, the user step length is estimated using empirical or biomechanical formulas. Such calculation was shown to be very sensitive to the smartphone location on the user. In addition, knowledge of the smartphone location can also help for direct step-length estimation and heading determination. In a wider point of view, smartphone location recognition is part of human activity recognition employed in many fields and applications, such as health monitoring. In this paper, we propose to use deep learning approaches to classify the smartphone location on the user, while walking, and require robustness in terms of the ability to cope with recordings that differ (in sampling rate, user dynamics, sensor type, and more) from those available in the train dataset. The contributions of the paper are: (1) Definition of the smartphone location recognition framework using accelerometers, gyroscopes, and deep learning; (2) examine the proposed approach on 107 people and 31 h of recorded data obtained from eight different datasets; and (3) enhanced algorithms for using only accelerometers for the classification process. The experimental results show that the smartphone location can be classified with high accuracy using only the smartphone's accelerometers.
Proposed Solution for Trackable Donations using Blockchain

The lack of transparency has made people lose trust in charities, making social funding stagnant. The donor is unaware of the legitimate utilization of his funds. Corruption adds to the distrust of the donor. This paper proposes a system called Charity-Chain that is a decentralised network built on the Ethereum blockchain. It helps social organisations to run projects transparently, using smart contract-based incentives to ensure their impact is independently verified and accessible to everyone. This makes it much easier for funders (philanthropic organisations, impact investors, small donors) to monitor their transactions and hence restore their trust in giving to such social organizations.
Sentiment Analysis of Twitter Data Through Machine Learning Techniques

Cloud computingCloud computing is a revolutionary technologySentiment analysis for businessesTwitter, governments, and citizens. Some examples of Software-as-a-Services (SaaS) of cloud computing are banking apps, e-mail, blog, online news, and social networksSocial networks. In this chapter, we analyze dataData sets generated by trending topics on TwitterTwitter that emerged from Mexican citizens that interacted during the earthquake of September 19, 2017, using sentiment analysisSentiment analysis and supervised learning, based on the Ekman'sEkman's six emotional modelEkman's model. We built three classifiers to determine the emotionsEmotions of tweets that belong to the same topic. The classifiers with the best accuracy for predicting emotions were Naive BayesNaive Bayes and support vector machineSupport Vector Machine. We found that the most frequent predicted emotions were happinessHappiness, angerAnger, and sadnessSadness; also, that 6.5% of predicted tweets were irrelevant. We provide some recommendations about the use of machine learning techniques in sentiment analysisSentiment analysis. Our contribution is the expansion of the emotions range, from three (negative, neutral, positive) to six in order to provide more elements to understand how users interact with social mediaSocial media platforms. Future research will include validation of the method with different data setsDatasets and emotions, and the addition of new artificial intelligence techniques to improve accuracy.
Key regeneration-free ciphertext-policy attribute-based encryption and its application

Attribute-based encryption (ABE) provides a promising solution for enabling scalable access control over encrypted data stored in the untrusted servers (e.g., cloud) due to its ability to perform data encryption and decryption defined over descriptive attributes. In order to bind different components which correspond to different attributes in a user's attribute-based decryption key together, key randomization technique has been applied in most existing ABE schemes. This randomization method, however, also empowers a user the capability of regenerating a newly randomized decryption key over a subset of the attributes associated with the original decryption key. Because key randomization breaks the linkage between this newly generated key and the original key, a malicious user could leak the new decryption key to others without taking any responsibility for the key abuse. To solve this problem, we think of key regeneration-free ABE to disallow a user from randomizing his/her decryption key in any manner, i.e., a user can only delegate his/her decryption key in exactly the same form without any modification so that any abused or pirated key can be traced back to its original owner. Motivated by strongly unforgeable signature, we first define a security notion called strong key unforgeability, and show that ABE schemes equipped with the strong key unforgeability are immune to key regeneration. We then provide a generic transformation to convert ciphertext-policy ABE (CP-ABE) schemes of certain type to key regeneration-free CP-ABE schemes, and show how the transformation works by presenting two concrete constructions.
Process-Based Composition of Permissioned and Permissionless Blockchain Smart Contracts

Blockchains are distributed systems that facilitate the interaction of autonomous entities with limited mutual trust. Many of them support transactional applications known as smart contracts, which access and modify the shared world state. Permissionless blockchains are completely decentralized and do not require mutual trust between interacting peers, but at the expense of having low performance and limited data confidentiality capabilities. On the other hand, permissioned blockchains solve these issues, but sacrifice complete decentralization and involve more trust assumptions. Therefore, there is no single blockchain system suitable for all use-cases. However, this becomes a serious integration challenge for enterprises that need to interact with multiple permissioned and permissionless blockchains in the same context. To facilitate this, we propose an approach that enables composing smart contract functions of various permissioned and permissionless blockchain systems by providing the ability to invoke them directly from business process models using a new task type. To keep this task blockchain-agnostic, we designed a generic technique to identify smart contract functions, as well as a generic metric to describe the degree-of-confidence in the finality of blockchain transactions. Thereby, the proposed approach extends our previous work, BlockME, which provides business modeling extensions only suitable for interacting with permissionless blockchains. To validate the practical feasibility of our approach, we provide a detailed system architecture and a prototypical implementation supporting multiple blockchains.
A Flexible and Efficient Real-Time ORB-Based Full-HD Image Feature Extraction Accelerator

This article proposes a hardware accelerator for image feature extraction based on the oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (ORB) algorithm. The architecture adopts a hybrid workflow to process three scales in parallel as an octave, and multiple octaves are processed in serial by time-sharing the DRAM. In order to support any image resolution, a block-wise dataflow is used on the serial-parallel architecture. The overlapped data between two blocks is reused. As a result, the on-chip memory is limited to 1.47 Mb, and the DRAM bandwidth is compressed by 33%. Besides, 3×3 nonmaximum suppression with heap sorting is applied to balance keypoint distribution in 2-D. It improves the valid keypoint match ratio by 7.09%. In addition, parallel processing in keypoint detection increases 2× throughout. Moreover, approximate computing and superscalar processing reduce the timing cost in orientation estimation and descriptor generation by 92.78% and 67% respectively. Compared to the non-optimized baseline architecture, the proposed architecture saves the total timing cost by 82.4%. The accelerator is implemented in a Xilinx MPSoC, and it achieves 108 fps on full-HD images at 200 MHz under 873 mW.
On the Structure of Cyclic Codes Over $\mathbb{F}_{q}{RS}$ and Applications in Quantum and LCD Codes Constructions

Let p be an odd prime, q = p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> , R = F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> + uF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> with u <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 1, and S = F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> + uF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> + vF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> + uvF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> with u <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 1, v <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 1, uv = vu. In this paper, F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> RS-cyclic codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> RS are studied. As an application, we present a construction of quantum error-correcting codes (QECCs) from the F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> RS-cyclic codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> RS, which provides new QECCs. We also consider linear complementary dual (LCD) codes from the F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> RS-cyclic codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> RS. Among others, we construct a Gray map over FqRS and discuss the Gray images of F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> RS-cyclic codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> .
A Multi-Channel Deep Neural Network for Relation Extraction

The task of relation recognition identifies semantic relationships between two named entities in a sentence. In neural network based models, a convolutional layer is often conducted to extract representative local features of a sentence. The convolution operation is implemented through a whole sentence, without considering the structure of a sentence. Because the task to recognize entity relation is processed in sentence level, many ambiguous phenomena (e.g., polysemy) are influential rather than in a document. Capturing structural information of a sentence is helpful to solve this problem. In this paper, a multi-channel framework is presented, which uses two named entities to divide a sentence into several channels. Each channel is stacked with layered neural networks. These channels do not interact during recurrent propagation, which enables a neural network to learn different representations. In our experiments, it outperforms the widely used position embedding approach. Comparing with the state-of-the-art approaches, its performance shows a meaningful improvement.
All Optical Integrate and Fire Neuromorphic Node Based on Single Section Quantum Dot Laser

In this work we provide numerical results concerning an all-optical inhibitory integrate and fire neuron based on a single section quantum-dot InAs/GaAs laser. The numerical model employs a detailed multi-population approach that accommodates electron-hole dynamics and can efficiently describe waveband transitions from both the ground and excited energy state. The underlying physical mechanism of waveband switching in an inhibitory neuron is unveiled and is attributed to hole-electron's transport time asymmetries. A detailed dynamical analysis allowed the identification of suitable optical injection regimes so as to trigger highly sought neuro-computational behaviors such as rate encoding and neural spikes with picosecond temporal resolution.
My body until proven otherwise: Exploring the time course of the full body illusion

Evidence from the Full Body Illusion (FBI) has shown that adults can embody full bodies which are not their own when they move synchronously with their own body or are viewed from a first-person perspective. However, there is currently no consensus regarding the time course of the illusion. Here, for the first time, we examined the effect of visuomotor synchrony (synchronous/asynchronous/no movement) on the FBI over time. Surprisingly, we found evidence of embodiment over a virtual body after five seconds in all conditions. Embodiment decreased with increased exposure to asynchronous movement, but remained high in synchronous and no movement conditions. We suggest that embodiment of a body seen from a first-person perspective is felt by default, and that embodiment can then be lost in the face of contradictory cues. These results have significant implications for our understanding of how multisensory cues contribute to embodiment.
Crowd Detection for Drone Safe Landing Through Fully-Convolutional Neural Networks

In this paper, we propose a novel crowd detection method for drone safe landing, based on an extremely light and fast fully convolutional neural network. Such a computer vision application takes advantage of the technical tools some commercial drones are equipped with. The proposed architecture is based on a two-loss model in which the main classification task, aimed at distinguishing between crowded and non-crowded scenes, is simultaneously assisted by a regression task, aimed at people counting. In addition, the proposed method provides class activation heatmaps, useful to semantically augment the flight maps. To evaluate the effectiveness of the proposed approach, we used the challenging VisDrone dataset, characterized by a very large variety of locations, environments, lighting conditions, and so on. The model developed by the proposed two-loss deep architecture achieves good values of prediction accuracy and average precision, outperforming models developed by a similar one-loss architecture and a more classic scheme based on MobileNet. Moreover, by lowering the confidence threshold, the network achieves very high recall, without sacrificing too much precision. The method also compares favorably with the state-of-the-art, providing an effective and efficient tool for several safe drone applications.
Content-Preserving Image Stitching With Piecewise Rectangular Boundary Constraints

e r G r a p hi c s 2 7 (7) , p p . 3 1 9 8-3 2 1 2 .
Location-Based Augmented Reality In-situ Visualization Applied for Agricultural Fieldwork Navigation

Nowadays unbalanced crop yield and population growth have led to people worry about a potential future “Agricultural Crisis”. To increase the arable land use efficiency, this research has focused on the development of Augmented Reality based Agricultural information visualization and fieldwork navigation tool. This tool has been developed to provide professional aids for farmers' daily fieldwork. To provide continuous fieldwork assistance, this system integrates non-distraction field navigation, the overall visualization of fields and subfields in the form of overlaid polygons, and the detail sensor data visualization for pertinent subfields in the form of Point of Interests. Due to the limitation of current commercially available AR headsets, we first validate this test bench on Virtual Reality video see-through headsets and AR tablets. At this stage, this test bench has only been validated with simulated data. In future, Situated Analytics concepts are planned to be integrated into the system to in-situ visualize real-time datachunks acquired from local sensors, so as to provide timely-accurate information and decision support even when the whole dataset is in a high volume.
Deep Learning in Natural Language Processing: A State-of-the-Art Survey

Deep learning raises interests of research community as their overwhelming successes in information processing such specific tasks as video/speech recognition. In this paper, we provide a state-of-the-art analysis of deep learning with its applications in an important direction: natural language processing. We attempt to provide a clear and critical summarization for researchers and participators who are interested in incorporating the deep learning techniques in their specific domains.
A Blockchain-based Framework for Collaborative Production in Distributed and Social Manufacturing

In recent years, novel collaborative production paradigms, such as (re-)distributed manufacturing and social manufacturing, have attracted intensive attention due to their potential of further changing the existing manufacturing modes and industrial structures. Blockchain, as a new distributed computing architecture, is expected to be successfully used in manufacturing, and help solve issues related to interoperability and collaboration, security and supervision, marketization and protocol, democratic organization and global value chain governance, and so on. In this paper, a blockchain-based framework for collaborative production is proposed, aiming at providing novel ideas for the design, implementation and optimization of collaborative production, and also inspiring the upgrading and transformation of manufacturing industries.
Multi-Object Tracking of 3D Cuboids Using Aggregated Features

The unknown correspondences of measurements and targets, referred to as data association, is one of the main challenges of multi-target tracking. Each new measurement received could be the continuation of some previously detected target, the first detection of a new target or a false alarm. Tracking 3D cuboids, is particularly difficult due to the high amount of data, which can include erroneous or noisy information coming from sensors, that can lead to false measurements, detections from an unknown number of objects which may not be consistent over frames or varying object properties like dimension and orientation. In the self-driving car context, the target tracking module holds an important role due to the fact that the ego vehicle has to make predictions regarding the position and velocity of the surrounding objects in the next time epoch, plan for actions and make the correct decisions. To tackle the above mentioned problems and other issues coming from the self-driving car processing pipeline we propose three original contributions: 1) designing a novel affinity measurement function to associate measurements and targets using multiple types of features coming from LIDAR and camera, 2) a context aware descriptor for 3D objects that improves the data association process, 3) a framework that includes a module for tracking dimensions and orientation of objects. The implemented solution runs in real time and experiments that were performed on real world urban scenarios prove that the presented method is effective and robust even in a highly dynamic environment.
Steps towards an ecology of money infrastructures: materiality and cultures of Ripple

Money’s materiality produces an ontological conundrum for social theory: should the analysis of money foreground the objects used as money, or the abstract relations that underpin it? Provoked by the emergence of cryptocurrencies, this paper develops a conceptualization of money as a technological and social infrastructure which directly addresses this theoretical impasse. Cryptocurrencies’ sole form of material existence coincides with their underpinning infrastructure of records, accounting and payments. In the past decade, cryptocurrencies have skyrocketed in number, and they have been applied to a host of use cases. This paper focuses on cross-border payments through the example of the fintech company Ripple, the cryptocurrency XRP, and the design of the XRP Ledger. Combining literatures from the social theory of money, science and technology studies and new materialisms, this article develops steps towards an ecological conceptualization of money infrastructures. Infrastructures, understood ecologically, include devices, active forms, and imaginaries in seamless webs of mutual relations of co-evolution. These ecologies are always potentially prone to slippage, dissolution, disassembling, reassembling and reappropriation, dependence, and competition.
Using DICOM Metadata for Radiological Image Series Categorization: a Feasibility Study on Large Clinical Brain MRI Datasets

The growing interest in machine learning (ML) in healthcare is driven by the promise of improved patient care. However, how many ML algorithms are currently being used in clinical practice? While the technology is present, as demonstrated in a variety of commercial products, clinical integration is hampered by a lack of infrastructure, processes, and tools. In particular, automating the selection of relevant series for a particular algorithm remains challenging. In this work, we propose a methodology to automate the identification of brain MRI sequences so that we can automatically route the relevant inputs for further image-related algorithms. The method relies on metadata required by the Digital Imaging and Communications in Medicine (DICOM) standard, resulting in generalizability and high efficiency (less than 0.4 ms/series). To support our claims, we test our approach on two large brain MRI datasets (40,000 studies in total) from two different institutions on two different continents. We demonstrate high levels of accuracy (ranging from 97.4 to 99.96%) and generalizability across the institutions. Given the complexity and variability of brain MRI protocols, we are confident that similar techniques could be applied to other forms of radiological imaging.
Opportunistic sensing for inferring in-the-wild human contexts based on activity pattern recognition using smart computing

In recent years, with the evolution of internet-of-things and smart sensing technologies, sensor-based physical activity recognition has gained substantial prominence, and numerous research works have been conducted in this regard. However, the accurate recognition of in-the-wild human activities and the associated contexts remains an open research challenge to be addressed. This research work presents a novel activity-aware human context recognition scheme that explicitly learns human activity patterns in diverse behavioral contexts and infers in-the-wild user contexts based on physical activity recognition. In this aspect, five daily living activities, e.g., lying, sitting, standing, walking, and running, are associated with overall fourteen different behavioral contexts, including phone positions. A public domain dataset, i.e., ExtraSensory, is used for evaluating the proposed scheme using a series of machine learning classifiers. Random Forest classifier achieves the best recognition rate of 88.4% and 89.8% in recognizing five physical activities and the associated behavioral contexts, respectively, which demonstrates the efficacy of the proposed method.