Reviewerly/exHarmony huggingface dataset:
DriverAuth: A risk-based multi-modal biometric-based driver authentication scheme for ride-sharing platforms

On-demand ride and ride-sharing services have revolutionized the point-to-point transportation market and they are rapidly gaining acceptance among customers worldwide.Alone, Uber and Lyft are providing over 11 million rides per day [1, 2].These services are provided using a clientserver infrastructure.The client is a smartphone-based application used for: i) registering riders and drivers, ii) connecting drivers with riders, iii) car-sharing to share the expenses, minimize traffic congestion and saving traveling time, iv) allowing customers to book their rides.The server typically, run by multi-national companies such as Uber, Ola, Lyft, BlaBlaCar, manages drivers and customers registrations, allocates ride-assignments, sets tariffs, guarantees payments, ensures safety and security of riders, etc.However, the reliability of drivers have emerged as a critical problem, and as a consequence, issues related to riders safety and security have started surfacing.The lack of robust driver verification mechanisms has opened a room to an increasing number of misconducts (i.e., drivers subcontracting ride-assignments to an unauthorized person, registered drivers sharing their registration with other people whose eligibility to drive is not justified, etc.) [3,4,5].This paper proposes DriverAuth -a novel risk-based multi-modal biometric-based authentication solution, to make the on-demand ride and ride-sharing services safer and more secure for riders.DriverAuth utilizes three biometric modalities, i.e., swipe, text-independent voice, and face, in a multi-modal fashion to verify the identity of registered drivers.We evaluated Driver-Auth on a dataset of 10, 320 samples collected from 86 users and achieved a True Acceptance Rate (TAR) of 96.48% at False Acceptance Rate (FAR) of 0.02% using Ensemble Bagged Tree (EBT) classifier.Furthermore, the architecture used to design DriverAuth enables easy integration with most of the existing on-demand ride and ride-sharing systems.
A fuzzy logic approach to reliable real-time recognition of facial emotions

This paper represents our newly developed software for emotion recognition from facial expressions. Besides allowing emotion recognition from image files and recorded video files, it uses webcam data to provide real-time, continuous, and unobtrusive facial emotional expressions. It uses FURIA algorithm for unordered fuzzy rule induction to offer timely and appropriate feedback based on learners' facial expressions. The main objective of this study was first to validate the use of webcam data for a real-time and accurate analysis of facial expressions in e-learning environments. Second, transform these facial expressions to detected emotional states using the FURIA algorithm. We measured the performance of the software with ten participants, provided them with the same computer-based tasks, requested them a hundred times to mimic specific facial expressions, and recorded all sessions on video. We used the recorded video files to feed our newly developed software. We then used two experts' opinions to annotate and rate participants' recorded behaviours and to validate the software's results. The software provides accurate and reliable results with the overall accuracy of 83.2%, which is comparable to the recognition by humans. This study will help to increase the quality of e-learning.
An evaluation thermometer for assessing city sustainability and livability

The real estate industry is an important indicator of national economic growth and development, which is influenced by the environment in which it operates. Various countries have been seriously affected by the most recent international financial crisis. Nevertheless, regardless of the challenges some cities currently face and the impacts on their sustainable livability, urban real estate is still of interest to investors. Given this context, researchers have sought to develop and apply methods of evaluating sustainable livability in cities. However, most practical applications have been hampered by methodological limitations (e.g., how to select and weight criteria in evaluations), which has hampered progress in this area. The present study thus aimed to develop a knowledge-based decision support system to evaluate city sustainability and livability in a transparent and informed way. To achieve its goal, this research combined cognitive mapping techniques and the analytic hierarchy process (AHP). Based on real-world data, the advantages and limitations of this integrative evaluation system are discussed.
Applications of image processing in robotics and instrumentation

Modern applications of industrial automation and robotics are increasingly relying on image processing techniques. This paper shows ways in which image processing can be applied to solve actual problems in robotics and instrumentation. The paper starts by presenting the fundamentals of camera models, digital acquisition of images and selected processing techniques, followed by examples of applications of such knowledge. Four examples of image processing applications are shown: rim detection in automotive wheel images, dimensional verification of crankshafts, measurement of wheel alignment angles of a car and a stereo visual odometry algorithm for mobile robotics. The examples not only illustrate the uses of different image processing techniques, but may also inspire the development of new robotic and industrial automation products.
Securing multiple information using wavelet transform and Yang-Gu mixture amplitude-phase retrieval algorithm

In this paper, a novel multiple information security system based on wavelet transform and Yang-Gu mixture amplitude-phase retrieval algorithm is proposed. In this method, four color images are independently single-level 2-D discrete wavelet transformed. A single color image constructed by fusing LL sub-bands of four color images is separated into R, G, and B channels. The amplitude of each channel and corresponding chaotic spiral phase function (CSPF) are, respectively, exploited as an input function and a phase function in Yang-Gu mixture amplitude-phase retrieval algorithm in gyrator transform domain. The detectable retrieved amplitude is obtained by one-way binary phase modulation, which is more convenient for key management, storage, and transmission. The decryption keys are generated by executing binary phase modulation to CSPF. The parameters (initial value, bifurcation parameter, chosen integer, order, wavelength, focal length, and radius) of CSPF are used as decryption keys. Consequently, the proposed asymmetric system is protected by multiple-decryption keys and it can avoid various types of attacks. The proposed method can be implemented by using optoelectronic setup. Numerical simulation results prove the security and feasibility of the strategy.
Generating Product Descriptions from User Reviews

Product descriptions play an important role in the e-commerce ecosystem, conveying to buyers information about a merchandise they may purchase. Yet, on leading e-commerce websites, with high volumes of new items offered for sale every day, product descriptions are often lacking or missing altogether. Moreover, many descriptions include information that holds little value and sometimes even disrupts buyers, in an attempt to draw attention and purchases. In this work, we suggest to mitigate these issues by generating short crowd-based product descriptions from user reviews . We apply an extractive approach, where review sentences are used in their original form to compose the product description. At the core of our method is a supervised approach to identify candidate review sentences suitable to be used as part of a description. Our analysis, based on data from both the Fashion and Motors domains, reveals the top reasons for review sentences being unsuitable for the product's description and these are used, in turn, as part of a deep multi-task learning architecture. We then diversify the set of candidates by removing redundancies and, at the final step, select the top candidates to be included in the description. We compare different methods for each step and also conduct an end-to-end evaluation, based on rating from professional annotators, showing the generated descriptions are of high quality.
The verified CakeML compiler backend

Abstract The CakeML compiler is, to the best of our knowledge, the most realistic verified compiler for a functional programming language to date. The architecture of the compiler, a sequence of intermediate languages through which high-level features are compiled away incrementally, enables verification of each compilation pass at an appropriate level of semantic detail. Parts of the compiler’s implementation resemble mainstream (unverified) compilers for strict functional languages, and it supports several important features and optimisations. These include efficient curried multi-argument functions, configurable data representations, efficient exceptions, register allocation, and more. The compiler produces machine code for five architectures: x86-64, ARMv6, ARMv8, MIPS-64, and RISC-V. The generated machine code contains the verified runtime system which includes a verified generational copying garbage collector and a verified arbitrary precision arithmetic (bignum) library. In this paper, we present the overall design of the compiler backend, including its 12 intermediate languages. We explain how the semantics and proofs fit together and provide detail on how the compiler has been bootstrapped inside the logic of a theorem prover. The entire development has been carried out within the HOL4 theorem prover.
Asymptotic security of discrete-modulation protocols for continuous-variable quantum key distribution

We consider discrete-modulation protocols for continuous-variable quantum key distribution (CV-QKD) that employ a modulation constellation consisting of a finite number of coherent states and that use a homodyne- or a heterodyne-detection receiver. We establish a security proof for collective attacks in the asymptotic regime, and we provide a formula for an achievable secret-key rate. Previous works established security proofs for discrete-modulation CV-QKD protocols that use two or three coherent states. The main constituents of our approach include approximating a complex, isotropic Gaussian probability distribution by a finite-size Gauss-Hermite constellation, applying entropic continuity bounds, and leveraging previous security proofs for Gaussian-modulation protocols. As an application of our method, we calculate secret-key rates achievable over a lossy thermal bosonic channel. We show that the rates for discrete-modulation protocols approach the rates achieved by a Gaussian-modulation protocol as the constellation size is increased. For pure-loss channels, our results indicate that in the high-loss regime and for sufficiently large constellation size, the achievable key rates scale optimally, i.e., proportional to the channel's transmissivity.
A study on multi-kernel intuitionistic fuzzy C-means clustering with multiple attributes

Fuzzy C-means (FCM) clustering has been widely applied in various data-driven applications. While traditional FCM clustering algorithms handle uncertainty with type-2 Fuzzy Sets (T2 FSs), the recently-proposed intuitionistic fuzzy sets (IFSs) have shown advantages for describing vague and uncertain data by taking both membership degree and non-membership degree into account. However, intuitionistic fuzzy C-means (IFCM) algorithms generally do not take the importance of individual attributes and the structure of the data into account, when multi-modal and imbalanced features are involved in an application. Therefore, in this paper, we propose to address this issue of IFCM with multi-kernel mapping. First, different types of features are grouped. Second, a composite kernel is constructed to map each attribute group into an individual kernel space and to linearly combine these kernels with optimimal weights. Comprehensive experiments have been conducted on a wide range of datasets, such as machine learning repository (UCI) dataset, fabric dataset, hyperspectral imaging classification and MRI brain images segmentation to demonstrate the superior performance of the proposed clustering algorithm in comparison with the state-of-the-art in the field.
Organised and Ambient Digital Racism: Multidirectional Flows in the Irish Digital Sphere

This article is concerned with the distinction between acceptable race talk in social media and organised, extreme or ‘frozen’ racism which is considered hate speech and removed. While in the literature this distinction is used to point to different variants, styles and mutations of racism, in social media platforms and in European regulatory frameworks it becomes policy. The empirical part of the article considers this distinction drawing upon a series of posts following a stabbing incident in a small Irish town, which organised Twitter accounts sought to connect to terrorism. The empirical analysis examines the tweets of those accounts and the comments left on the Facebook page and website of one of the main Irish online news outlets. The analysis shows few if any differences between the two, concluding that there is a blending of supremacist and everyday, ambient racist discourses. This blending indicates the operation of a transnational contagion, given the shared vocabularies and discourses. It further problematises the distinction between ‘illegal hate speech’ and ‘acceptable race talk’, and throws into question the principle underlying both the policies of social media as well as the European efforts to de-toxify the digital public sphere.
Bitcoin return: Impacts from the introduction of new altcoins

The model predicting Bitcoin price formation remains a mystery to academia and investors. Newly invented cryptocurrencies (alternative coins – altcoins) with enhanced features may become close substitutes to Bitcoin in terms of risk diversification. Employing Autoregressive-Distributed-Lag (ARDL) estimations, we document the evidence that the introduction of a new altcoin tends to lower Bitcoin return by 0.7% which is substantial given that the average and median daily returns of Bitcoin are 0.63% and 0.27%, respectively. Our study suggests that the negative impact of an Initial Public Offering on existing stock prices can also be observed in the cryptocurrency market: altcoin introductions reduce Bitcoin return.
Authorized Equality Test on Identity-Based Ciphertexts for Secret Data Sharing via Cloud Storage

With the higher rate of using cloud storage, protecting data privacy becomes an important issue. The most effective solution is to encrypt data before uploading to the cloud. However, how to efficiently search over data encrypted with different keys is still an open problem. To address this problem, we introduce a new notion of the identity-based encryption with equality test supporting flexible authorization (IBEET-FA). It supports the test of whether two ciphertexts encrypted under the different keys encapsulate the same message, and in the meanwhile supports fine-grained authorization of the test. Based on the equality test on ciphertexts, there is a direct way to support an authorized user to search over ciphertexts of different users, which accelerates secret data sharing among a group of users. Besides, IBEET-FA does not suffer from the complex key management problem of its counterpart in the traditional public key infrastructure. We propose a concrete construction of IBEET-FA and prove it to be securely based on simple mathematical assumptions. The experimental results show that our IBEET-FA scheme is efficient and can satisfy various types of search over encrypted data.
Intelligent context-awareness system for energy efficiency in smart building based on ontology

Context-awareness is an important research area. Many energy efficiency applications of ubiquitous computing need to access some related contexts to provide the best and adequate energy saving services at the right time and at the right place. One of the big challenges is the difficulty and the complexity to make systems identify and understand situations in the building business, especially in complex situations to provide adequate energy-saving services for each of which. This issue is divided into two parts: On one hand, the smart building and its environment must be modeled in a way that provides context-awareness. On the other hand is the how to effectively exploit this context-awareness to reduce energy consumption and increase the user comfort. In this paper, we propose an intelligent context-awareness Building Energy Management System (ICA-BEMS). ICA-BEMS uses hybrid energy-saving techniques based on a Smart Context-Awareness Management (Smart-CAM). Smart-CAM uses smart building ontology to organize smart building knowledge and utilizes a new context-awareness mechanism to provide contextual information. The former exploits the contextual information in reasoning to reduce the building energy consumption, promoting users behavioral change and maximizing the user's comfort to bring about a better energy efficiency policy. To evaluate our system, we have developed a smart building simulator (Open-SBS) which simulates smart building comportments and human behaviors. We have created various scenarios of daily life in the smart building simulator. We have tested our ICA-BEMS effect on energy consumption having a positive result. Energy consumption has been decreased by 40% of total energy consumption.
Ontology-based approach for the provision of simulation knowledge acquired by Data and Text Mining processes

Numerical simulation techniques such as Finite Element Analyses are essential in today's engineering design practices. However, comprehensive knowledge is required for the setup of reliable simulations to verify strength and further product properties. Due to limited capacities, design-accompanying simulations are performed too rarely by experienced simulation engineers. Therefore, product models are not sufficiently verified or the simulations lead to wrong design decisions, if they are applied by less experienced users. This results in belated redesigns of already detailed product models and to highly cost- and time-intensive iterations in product development. Thus, in order to support less experienced simulation users in setting up reliable Finite Element Analyses, a novel ontology-based approach is presented. The knowledge management tools developed on the basis of this approach allow an automated acquisition and target-oriented provision of necessary simulation knowledge. This knowledge is acquired from existing simulation models and text-based documentations from previous product developments by Text and Data Mining. By offering support to less experienced simulation users, the presented approach may finally lead to a more efficient and extensive application of reliable FEA in product development.
Unsupervised clustering and empirical fuzzy memberships for mineral prospectivity modelling

We propose to increase the role of empirical methods in mineral prospectivity modelling for two reasons: 1) to make use of data more effectively and 2) to decrease the effect of subjectivity included in expert interpretation. We present two approaches for using known mineral occurrences to define the relationship between observed or measured geoscientific parameters and the occurrence of mineralizations. In the first approach, we define the fuzzy memberships of each geoscientific parameter separately for fuzzy logic modelling. Our approach proves to be highly useful for investigating the quality of the data in addition to defining the membership transformation functions. In our test case, the data are somewhat scattered due to the inherent variability of ore-forming environments, and manual evaluation was required to guide the computations. For the second approach, we present a technique for delineating non-prospective regions to be able to focus more detailed prospectivity modelling to potentially prospective regions. Our study not only highlights the advantages of using computational methods in prospectivity modelling, but also emphasizes the important role of geological expertise in the modelling process.
Adversarial correlated autoencoder for unsupervised multi-view representation learning

To eliminate the view discrepancy of multi-view data due to different distributions, the key is to learn the common representation for multi-view data in many practical applications. To achieve the end, we propose a novel unsupervised multi-view representation learning method (called Adversarial Correlated AutoEncoder, AdvCAE). In brief, AdvCAE utilizes a deep structure to achieve nonlinear representation and adversarial learning scheme for distribution matching. To be specific, AdvCAE performs like an adversarial autoencoder (AAE) which could conduct variational inference by matching the aggregated posteriors of the latent variable with a specific prior distribution. Benefiting from our model, the representations of different views could follow the same distribution, thus learning the common representation for different views. To the best of our knowledge, AdvCAE could be one of the first unsupervised multi-view representation learning approaches that work in the manner of adversarial learning. To verify the effectiveness of the proposed method, we conduct experiments on five public real-world datasets w.r.t. the applications of cross-view classification and cross-view retrieval tasks. The experimental results show that our method remarkably outperforms than 15 state-of-the-art methods.
Delay Impact on MPEG OMAF’s Tile-Based Viewport-Dependent 360° Video Streaming

With the continuously increasing attention that 360° video streaming is drawing, several solutions have been developed lately. They can be grouped into two main categories, with the first one being the viewport-independent category that consists of encoding the whole 360° video content using a particular projection, e.g., Equirectangular Projection or Cubemap Projection, without taking any viewport orientation into account. Such approaches waste resources since content not being watched by the user is encoded with the same visual fidelity as the content actually watched. The second category, the viewport-dependent category, relies on techniques that allow for viewport adaptivity. One approach is to apply viewport-dependent projections, wherein the spherical 360° video is mapped onto a rectangular frame in such a way that a specific viewport is mapped to a comparatively larger part of the rectangular frame than the rest of the content. Another approach, as specified in MPEG OMAF, is to use tile-based streaming, with the 360° video being offered in a tiled manner at various resolutions. Thus, each user can retrieve the tiles at different resolutions so that the high-resolution tiles match its viewport. Although viewport-dependent approaches allow providing better visual quality at the viewport, the end-toend delay is critical, since it has an impact on the time needed by clients to adapt to user movements so that movements are reflected on the retrieved content. In this paper, we analyze the impact of the delay and introduce various means to reduce the impact on the observed fidelity.