Reviewerly/exHarmony huggingface dataset:
Suppressing Gender and Age in Face Templates Using Incremental Variable Elimination

Recent research on soft-biometrics showed that more information than just the person's identity can be deduced from biometric data. Using face templates only, information about gender, age, ethnicity, health state of the person, and even the sexual orientation can be automatically obtained. Since for most applications these templates are expected to be used for recognition purposes only, this raises major privacy issues. Previous work addressed this problem purely on image level regarding function creep attackers without knowledge about the systems privacy mechanism. In this work, we propose a soft-biometric privacy enhancing approach that reduces a given biometric template by eliminating its most important variables for predicting soft-biometric attributes. Training a decision tree ensemble allows deriving a variable importance measure that is used to incrementally eliminate variables that allow predicting sensitive attributes. Unlike previous work, we consider a scenario of function creep attackers with explicit knowledge about the privacy mechanism and evaluated our approach on a publicly available database. The experiments were conducted to eight baseline solutions. The results showed that in many cases IVE is able to suppress gender and age to a high degree with a negligible loss of the templates recognition ability. Contrary to previous work, which is limited to the suppression of binary (gender) attributes, IVE is able, by design, to suppress binary, categorical, and continuous attributes.
Exploring factors that influence teachers’ intentions to integrate digital literacy using the decomposed theory of planned behavior

This study explored factors that influence teachers’ intentions to integrate digital literacy into their classrooms based on the Decomposed Theory of Planned Behavior (DTPB). Path analysis was used to analyze quantitative data collected through an online survey (n = 144) and constant comparison approach was used to analyze open-ended survey responses. Findings revealed that the components of the DTPB–attitude, subjective norms and perceived behavioral control–explained significant variance in teachers' intentions to integrate digital literacy in their classrooms. Also, positive attitude, perceived usefulness, and self-efficacy are the strongest indicators of teachers’ intentions to integrate digital literacy into their classrooms. Additional findings indicate that teachers perceive the benefits of digital literacy for improving students’ 21st century skills and preparing them for college and professional careers.
Full quantum one‐way function for quantum cryptography

One-way functions are fundamental tools for cryptography. Until now, quantum one-way functions have several input-output categories such as “classical-to-classical,” “classical-to-quantum,” and “quantum-to-classical,” which are used for postquantum cryptography or quantum cryptography. However, there are still no intrinsic “quantum-to-quantum” quantum one-way functions. In this article, we propose the full quantum one-way function to design full quantum cryptographic schemes. By concatenating the “quantum-classical” one-way function and the rotation operation of single qubit, the full quantum one-way function has the input and output of quantum states. We prove its one-way property from “easy computation” and “computationally difficult to invert.” Then we apply the full quantum one-way function to quantum identity authentication. Security analysis shows that the proposed quantum identity authentication scheme based on the full quantum one-way function is secure even under active attacks.
Post-quantum distributed ledger for internet of things

Distributed ledger (DL) technology can help us to overcome security and privacy issues in IoT systems, however, merging of these two technologies engenders certain challenges. Cryptocurrencies initiated DL technology to maintain a history of the financial transactions. The size of a DL in a cryptocurrency approaches to hundreds of GBs, whereas, IoT nodes are limited in storage. Similarly, cryptocurrencies incorporate computationally expensive consensus mechanisms, whereas, IoT nodes are limited in computation and energy. Furthermore, the classical DLs (like Bitcoin) are not quantum-resilient. This paper proposes a quantum-secure DL for IoT environments, namely DL-for-IoT. The core of our design is a novel one-time signature (OTS) scheme namely, DL-OTS, which is a compact and efficient scheme. DL-OTS offer 75% reduction in signature size and 76% reduction in signature creation time, as compared to the popular Winternitz-OTS+ scheme. Furthermore, DL-OTS is an energy-efficient scheme, which saves 48.7% energy as compared to Winternitz-OTS scheme.
Hydra: a method for strain-minimizing hyperbolic embedding of network- and distance-based data

Abstract We introduce hydra (hyperbolic distance recovery and approximation), a new method for embedding network- or distance-based data into hyperbolic space. We show mathematically that hydra satisfies a certain optimality guarantee: it minimizes the ‘hyperbolic strain’ between original and embedded data points. Moreover, it is able to recover points exactly, when they are contained in a low-dimensional hyperbolic subspace of the feature space. Testing on real network data we show that the embedding quality of hydra is competitive with existing hyperbolic embedding methods, but achieved at substantially shorter computation time. An extended method, termed hydra+, typically outperforms existing methods in both computation time and embedding quality.
A Multiviewpoint Outdoor Dataset for Human Action Recognition

Advancements in deep neural networks have contributed to near perfect results for many computer vision problems such as object recognition, face recognition and pose estimation. However, human action recognition is still far from human-level performance. Owing to the articulated nature of the human body, it is challenging to detect an action from multiple viewpoints, particularly from an aerial viewpoint. This is further compounded by a scarcity of datasets that cover multiple viewpoints of actions. To fill this gap and enable research in wider application areas, we present a multi-viewpoint outdoor action recognition dataset collected from YouTube and our own drone. The dataset consists of 20 dynamic human action classes, 2324 video clips and 503086 frames. All videos are cropped and resized to 720x720 without distorting the original aspect ratio of the human subjects in videos. This dataset should be useful to many research areas including action recognition, surveillance and situational awareness. We evaluated the dataset with a two-stream CNN architecture coupled with a recently proposed temporal pooling scheme called kernelized rank pooling that produces nonlinear feature subspace representations. The overall baseline action recognition accuracy is 74.0%.
Blockchain-Based Framework for Data Storage in Peer-to-Peer Scheme Using Interplanetary File System

Interplanetary file system (IPFS) is a version-controlled file system in a peer-to-peer scheme that provides a content-addressable block storage model for storing and sharing files in a distributed environment. According to the feature of IPFS, we have proposed a blockchain-based framework to share the file using content-addressable block storage in the peer-to-peer model. To overcome the availability, reliability, storage overhead, and another issue of centralized service providers, we have introduced blockchain- and IPFS-based distributed storage model that ensures the immutability, integrity, and availability of the resource. In this framework, we are storing the file on IPFS and the addressable content (hash) to the blockchain as a transaction. Our proposed scheme can effectively solve the problem of availability, reliability, and storage of a centralized service provider. In this proposed framework, we are storing the academic record of the student (jpg format) on IPFS (peer-to-peer scheme). To implement the blockchain framework, we have used python(anaconda), python flask, postman, and IPFS.
Comparative Computational Analysis on High Stable Hexacopter for Long Range Applications

The primary lack in the multi-rotor Unmanned Aerial Vehicle (UAV) is unstable platform while flying, which might affect the usage of UAVs. The existing solution methodology for this lack is to increase the propellers has capable to provide more amount of thrust, which supports the stable platform while in the surveillance. Increase the propellers may chance to decrease the operational efficiency of the Hexacopter so in this paper suggested the alternate efficient method. This article recommended the Hexacopter attached with inclined arms, which has high stable platform than the Hexacopter with straight connecting arms. Comparison of these two cases have been completed using numerical simulation also optimization of inclined angle of connecting arms is done with the help of ANSYS Workbench 17.2 in which the physical model is designed by using CATIA V5. As an outcome of this simulation work is efficient Hexacopter, which capable to provide good aerial images for long range applications with low cost.
ProBLeSS: A Proactive Blockchain Based Spectrum Sharing Protocol Against SSDF Attacks in Cognitive Radio IoBT Networks

Spectrum Sharing Data Falsification (SSDF) attacks can cause heavy performance degradation to Cognitive Radio (CR) based Internet of Battlefield Things (IoBT) networks. The challenge in such networks is to handle this security problem real time, in addition to spectrum sharing. This requires a robust CR architecture and protocol that can provide integrity of the spectrum sensing data being shared between Secondary Users (SUs) for collaborative spectrum decisions in protocols like PROLEMus. We propose one such protocol called Proactive Blockchain based Spectrum Sharing (ProBLeSS) protocol which leverages a blockchain to provide security against SSDF attacks in CR-IoBT networks.
Short Utterance Compensation in Speaker Verification via Cosine-Based Teacher-Student Learning of Speaker Embeddings

The short duration of an input utterance is one of the most critical threats that degrade the performance of speaker verification systems. This study aimed to develop an integrated text-independent speaker verification system that inputs utterances with short duration of 2 seconds or less. We propose an approach using a teacher-student learning framework for this goal, applied to short utterance compensation for the first time in our knowledge. The core concept of the proposed system is to conduct the compensation throughout the network that extracts the speaker embedding, mainly in phonetic-level, rather than compensating via a separate system after extracting the speaker embedding. In the proposed architecture, phonetic-level features where each feature represents a segment of 130 ms are extracted using convolutional layers. A layer of gated recurrent units extracts an utterance-level feature using phonetic-level features. The proposed approach also adopts a new objective function for teacher-student learning that considers both Kullback-Leibler divergence of output layers and cosine distance of speaker embeddings layers. Experiments were conducted using deep neural networks that take raw waveforms as input, and output speaker embeddings on VoxCelebl dataset. The proposed model showed 16.6 % relative improvement compared to a baseline approach.
Robust Path Planning for Slope Traversing Under Uncertainty in Slip Prediction

This letter addresses the path planning problem for a rover on deformable, sloped terrains. As such terrains induce high slip and possible immobilization of rovers, finding a path that avoids critical slip is important for traversing the terrains. However, it is difficult to predict rover slippage precisely, especially on steep slopes, and a certain level of prediction uncertainty is inevitable. Although several path planning algorithms that consider rover slippage have been proposed thus far, they do not consider the uncertainty in slip prediction. This letter proposes a robust path planning algorithm that finds a safe and efficient path based on a chance-constrained planning approach. The proposed algorithm probabilistically guarantees safety against immobilization, with the safety level specified by user-definable parameters. The simulation results demonstrate the effectiveness and flexibility of the proposed algorithm.
Design of a Mobile Augmented Reality Platform with Game-Based Learning Purposes

Augmented reality (AR) is an emergent technology that overlays virtual objects into the real environment. Lately, AR is gaining prominence in education because of its increasing affordability through computers or mobile technologies. In addition, research sustaining the benefits of AR to promote student’s engagement to learn is increasing every day. However, the literature identifies lack of studies about the use of AR in education, namely, studies focused on the development of AR games designed over specific learning objectives (game-based learning). This paper presents a mobile augmented reality platform with learning purposes. The platform includes a mobile application that consists of a location-based game targeted to promote learning about the universe. Furthermore, it includes a back-office that allows teachers to introduce information about celestial bodies and also develop a set of multiple-choice questions to assess student’s learning about the subject matters they teach. The mobile application provides the users with physical movement and social interaction in the real world, while playing the game and for this reason it is included in the pervasive games’ paradigm. Besides engaging the students to play the game, we argue that this platform may be used as a resource to be implemented in informal and formal learning environments.
Detailed investigation of deep features with sparse representation and dimensionality reduction in CBIR: A comparative study

Research on content-based image retrieval (CBIR) has been under development for decades, and numerous methods have been competing to extract the most discriminative features for improved representation of the image content. Recently, deep learning methods have gained attention in computer vision, i ncluding CBIR. In this paper, we present a comparative investigation of different features, including low-level and high-level features, for CBIR. We compare the performance of CBIR systems using different deep features with state-of-the-art low-level features such as SIFT, SURF, HOG, LBP, and LTP, using different dictionaries and coefficient learning techniques. Furthermore, we conduct comparisons with a set of primitive and popular features that have been used in this field, including colour histograms and Gabor features. We also investigate the discriminative power of deep features using certain similarity measures under different validation approaches. Furthermore, we investigate the effects of the dimensionality reduction of deep features on the performance of CBIR systems using principal component analysis, discrete wavelet transform, and discrete cosine transform. Unprecedentedly, the experimental results demonstrate high (95% and 93%) mean average precisions when using the VGG-16 FC7 deep features of Corel-1000 and Coil-20 datasets with 10-D and 20-D K-SVD, respectively.
News Recommender System Considering Temporal Dynamics and News Taxonomy

In the past, news recommender systems have been built to recommend list of news items similar to those that a user has accessed before (content-based); or similar to those that have been read by similar users (collaborative filtering). However, the highly volatile nature of the news content and the dynamic and evolving user preferences are either ignored or not taken into full consideration in these systems. In a news recommender system, it is very likely that a user's short-term interest or preference may have a sudden change due to an emerging social or personal event or breaking news while their long-term interests may change gradually or remain. For these long-term interests of the readers, it is often more appropriate to associate them with news categories than with individual news items. In this paper, we propose a biased matrix factorization model with consideration of both temporal dynamics of user preferences and news taxonomy to build a news recommender system. By conducting an extensive experiment on a collection of news data, we demonstrate the effectiveness of our proposed model against traditional matrix factorization models as well as other neural recommender baselines. The findings from our experiments show that news category is an important factor when readers choose news articles to read, and temporal factors with consideration of different temporal resolution also play a role in this process.
Publicly Verifiable Databases With All Efficient Updating Operations

The primitive of verifiable database (VDB) can enable a resource-limited client to securely outsource an encrypted database to an untrusted cloud server and the client could efficiently retrieve and update the data at will. Meanwhile, the client can undoubtedly detect any misbehavior by the server if the database has been tampered with. We argue that most of the existing VDB schemes can only support the updating operation of replacement, rather than other common updating operations such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">insertion</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deletion</i> . Recently, the first publicly verifiable VDB schemes that supports all updating operations was proposed based on the idea of hierarchical vector commitment. However, one disadvantage of the proposed VDB scheme is that the computation and storage complexity increases linearly when the client continually inserts data records in the same index of the database. As a result, it remains an open problem how to construct an efficient (and publicly verifiable) VDB scheme that can support all updating operations regardless of the manner of insertion. In this paper, we first introduce a new primitive called committed invertible Bloom filter (CIBF) and utilize it to propose a new publicly verifiable VDB scheme that can support all kinds of updating operations. Additionally, the proposed construction is efficient regardless of the manner of updating operations and thus provides an affirmative answer to the above open problem.
An Efficient Calibration Method of Line Structured Light Vision Sensor in Robotic Eye-in-Hand System

For line structured light vision sensor in robotic eye-in-hand system, the calibration of camera parameters, robot hand eye parameters and structured light plane parameters is an indispensable procedure before conducting 3-D measurement. Traditionally, these three sets of parameters are calibrated separately, making the calibration process complicated and inefficient. To address this, an efficient calibration method is proposed in this paper. By changing the poses of robot and calibration target, a set of images of the calibration target is captured, upon which the camera calibration is conducted. The extrinsic parameters obtained from camera calibration are utilized in the calibration of the structured light plane parameters and hand eye parameters, using the same set of images. Therefore, the three sets of parameters can be simultaneously calibrated with simple on-site operations. Moreover, the calibration target used in this method is only a planar checkerboard, which can be easily acquired at low cost. It is verified in the experiment that the proposed method is efficient and accurate, with the calibration duration within 10 minutes and the measurement accuracy within 0.40mm. Additionally, object pose measurement experiment is conducted to validate the effectiveness of the proposed method in practical application.
LoGANv2: Conditional Style-Based Logo Generation with Generative Adversarial Networks

Domains such as logo synthesis, in which the data has a high degree of multi-modality, still pose a challenge for generative adversarial networks (GANs). Recent research shows that progressive training (ProGAN) and mapping network extensions (StyleGAN) enable both increased training stability for higher dimensional problems and better feature separation within the embedded latent space. However, these architectures leave limited control over shaping the output of the network. This paper explores a conditional extension to the StyleGAN architecture with the aim of firstly, improving on the low resolution results of previous research and, secondly, increasing the controllability of the output through the use of synthetic class-conditions. Furthermore, methods of extracting such class conditions are explored, where the challenge lies in the fact that, visual logo characteristics are hard to define. The introduced conditional style-based generator architecture is trained on the extracted class-conditions in two experiments and studied relative to the performance of an unconditional model. Results show that, whilst the unconditional model more closely matches the training distribution, high quality conditions enabled the embedding of finer details onto the latent space, leading to more diverse output.
The political imaginaries of blockchain projects: discerning the expressions of an emerging ecosystem

Abstract There is a wealth of information, hype around, and research into blockchain’s ‘disruptive’ and ‘transformative’ potential concerning every industry. However, there is an absence of scholarly attention given to identifying and analyzing the political premises and consequences of blockchain projects. Through digital ethnography and participatory action research, this article shows how blockchain experiments personify ‘prefigurative politics’ by design: they embody the politics and power structures which they want to enable in society. By showing how these prefigurative embodiments are informed and determined by the underlying political imaginaries, the article proposes a basic typology of blockchain projects. Furthermore, it outlines a frame to question, cluster, and analyze the expressions of political imaginaries intrinsic to the design and operationalization of blockchain projects on three analytic levels: users, intermediaries, and institutions.