Reviewerly/exHarmony huggingface dataset:
VRCAuth: Continuous Authentication of Users in Virtual Reality Environment Using Head-Movement

The traditional PIN (Personal Identification Number) and password-based mechanisms are challenging in terms of remembering by the user and eavesdropping in VR (Virtual Reality) environment. The easy PINs and passwords weaken user authentication. The biometric-based authentication provides the solution to this problem but does not verify the user continuously during activities because it needs the active participation of the user for every authentication. The VRCAuth, a novel continuous authentication system is implemented for the user in the VR environment. In our system, head motion is used to provide continuous authentication. VRCAuth uses different machine learning techniques to classify the head motion of an authorized and unauthorized user. VRCAuth does not require any OS-level modifications or extra hardware, and it is suitable to use with existing hardware. VRCAuth performs better by attaining the accuracy of 99 % in 0.02 seconds providing a continuous authentication to the user in VR environment.
Capturing Subjective First-Person View Shots with Drones for Automated Cinematography

We propose an approach to capture subjective first-person view (FPV) videos by drones for automated cinematography. FPV shots are intentionally not smooth to increase the level of immersion for the audience, and are usually captured by a walking camera operator holding traditional camera equipment. Our goal is to automatically control a drone in such a way that it imitates the motion dynamics of a walking camera operator, and, in turn, capture FPV videos. For this, given a user-defined camera path, orientation, and velocity, we first present a method to automatically generate the operator’s motion pattern and the associated motion of the camera, considering the damping mechanism of the camera equipment. Second, we propose a general computational approach that generates the drone commands to imitate the desired motion pattern. We express this task as a constrained optimization problem, where we aim to fulfill high-level user-defined goals, while imitating the dynamics of the walking camera operator and taking the drone’s physical constraints into account. Our approach is fully automatic, runs in real time, and is interactive, which provides artistic freedom in designing shots. It does not require a motion capture system, and works both indoors and outdoors. The validity of our approach has been confirmed via quantitative and qualitative evaluations.
Catfishing: A Look into Online Dating and Impersonation

This study investigates catfishing and online impersonation. Catfishing is a relatively new social phenomenon that happens online. The term, catfishing is still foreign to many online users. It is still unclear to many people what constitutes catfishing and how it is the same or different from online impersonation or phishing. In this paper, we discuss catfishing and how it relates to other online threats like online impersonation and phishing. To see how catfishing affects online users, we interviewed sixteen college students who use social media and online dating platforms at a Historically Black College and University. Among the sixteen participants, nine said they were catfish victims, and four said they were online impersonation victims. Three participants said they had catfished other people online. In this paper, we share the stories of catfish and catfish victims. Our findings show that catfishing has affected our participants’ social media use and prevented some of them from trying online dating services.
Automatic Shape Feature Recognition for Ceramic Finds

Ceramic sherds are the most common finds in archaeology. They are complex to analyze and onerous to process. A large number of indistinct sherds coming from excavations must be preliminarily grouped in some categories. This clusterization helps the next phase, in which archaeologists classify the ceramics. Due to the difficulty of these preliminary, repetitive, and routine phases, a great deal of archaeological material remains unstudied in museum repositories or archaeological sites. An effective method to automate these routine phases is presented in this article. The proposed method performs a shape feature segmentation of the sherds, which is fundamental to undertake any further analysis, such as potsherds classification, reconstruction, or cataloging. A set of specific shape features, useful to understand the find properties, is defined and methods for recognizing them are proposed. The method's performance is tested in the analysis of some real, critical cases.
A residual network framework based on weighted feature channels for multispectral image compression

Abstract Deep learning has achieved great success in many computer vision tasks, such as recognition, image enhancement and image compression. However, it is difficult to use a residual network that can efficiently consider the characteristics of multispectral images for multispectral image compression. In this paper, a novel end-to-end multispectral image compression framework based on a weighted feature channel residual network is proposed to efficiently remove the spatial and spectral redundancy of multispectral images by extracting the importance of each channel. The multispectral image compression framework includes a forward coding network, a rate-distortion optimizer, a quantizer/inverse quantizer, an entropy encoder/decoder and an inverse decoding network. In the encoder, multispectral images are directly fed into the forward coding network, and the main spectral and spatial features of the multispectral images are extracted by the residual block. Additionally, the weighted feature channel module can explicitly model the relationship between feature channels when extracting features from multispectral images and adaptively allocate different weights for each feature channel through training. The rate-distortion optimizer is added to make the main features compact. Then, the intermediate feature data are quantized and encoded by lossless entropy coding to obtain a code stream. In the decoder, the code stream is approximately restored to the intermediate features through the entropy decoder and the inverse quantizer. Then, the intermediate features are reconstructed to the multispectral images by the inverse decoding network. Experimental results on 7-band multispectral images of the Landsat 8 satellite and 8-band multispectral images of WorldView-3 satellite demonstrate that the proposed algorithm can achieve a better PSNR than conventional 2D schemes (which are JPEG2000 and JPEG in this paper) and 3D scheme (which is 3D-SPIHT in this paper) and can effectively preserve more spectral information of multispectral images.
Tuning genetic algorithm parameters using design of experiments

Tuning evolutionary algorithms is a persistent challenge in the field of evolutionary computing. The efficiency of an evolutionary algorithm relates to the coding of the algorithm, the design of the evolutionary operators and the parameter settings for evolution. In this paper, we explore the effect of tuning the operators and parameters of a genetic algorithm for solving the Traveling Salesman Problem using Design of Experiments theory. Small scale problems are solved with specific settings of parameters including population size, crossover rate, mutation rate and the extent of elitism. Good values of the parameters suggested by the experiments are used to solve large scale problems. Computational tests show that the parameters selected by this process result in improved performance both in the quality of results obtained and the convergence rate when compared with untuned parameter settings.
Dual Encoder-Decoder Based Generative Adversarial Networks for Disentangled Facial Representation Learning

To learn disentangled representations of facial images, we present a Dual Encoder-Decoder based Generative Adversarial Network (DED-GAN). In the proposed method, both the generator and discriminator are designed with deep encoder-decoder architectures as their backbones. To be more specific, the encoder-decoder structured generator is used to learn a pose disentangled face representation, and the encoder-decoder structured discriminator is tasked to perform real/fake classification, face reconstruction, determining identity and estimating face pose. We further improve the proposed network architecture by minimizing the additional pixel-wise loss defined by the Wasserstein distance at the output of the discriminator so that the adversarial framework can be better trained. Additionally, we consider face pose variation to be continuous, rather than discrete in existing literature, to inject richer pose information into our model. The pose estimation task is formulated as a regression problem, which helps to disentangle identity information from pose variations. The proposed network is evaluated on the tasks of pose-invariant face recognition (PIFR) and face synthesis across poses. An extensive quantitative and qualitative evaluation carried out on several controlled and in-the-wild benchmarking datasets demonstrates the superiority of the proposed DED-GAN method over the state-of-the-art approaches.
MM-GLMB Filter-Based Sensor Control for Tracking Multiple Maneuvering Targets Hidden in the Doppler Blind Zone

To track multiple maneuvering targets hidden in the Doppler blind zone (DBZ), we have proposed an MM-GLMB-DBZ tracker based on the latest multiple model generalized labeled multi-Bernoulli (MM-GLMB) filter. To further enhance the tracking performance, this paper combines the sensor control technique to the MM-GLMB-DBZ tracker. Macroscopically, the proposed algorithm consists of the MM-GLMB-DBZ tracker and a controller. Unlike conventional control approaches where separate prediction and update implementations are usually adopted, the proposed control algorithm constructs a systematic process flow for the joint prediction and update implementation of GLMB-like filters. Moreover, inside the core controller module, we apply the previously designed safety indicator and reward function for avoiding the DBZ, and derive the Cauchy-Schwarz divergence (CSD) compatible with the tracker. Hence, this control algorithm considers such factors as the safety of the sensor itself, the DBZ avoidance, and the improvement of the tracking accuracy. Numerical examples verify the effectiveness of the proposed control scheme, showing that it is significantly better than the random control strategy, the original MM-GLMB-DBZ tracker without the control technology, and the state-of-the-art control approach.
Fraud reporting in Catalonia in the Internet era: Determinants and motives

Fraud has been identified as one of the most prevalent property crimes in the Internet era and underreporting represents one of the most pressing challenges to prevention and policing. Like fraud itself, the issue is not new and therefore traditional findings and approaches designed to foster reporting may also be applicable to online fraud. This study performs a series of regression models to analyse data on fraud victims from two editions of the representative Catalan Public Security Survey. The aim is to identify and compare socio-demographic, context and crime event determinants of online and offline fraud reporting, as well as the reasons for not reporting. Thus, the article contributes to the literature regarding cybercrime and traditional crime a/symmetries. The findings show that, surprisingly, online fraud is reported at a higher rate than offline fraud, mainly owing to the greater odds of being considered a crime by the victim. In general, fraud reporting appears to involve a rational component because financial and non-financial harms and the expected utility of reporting are more relevant to the decision than socio-demographic factors. In addition, the most common reasons for not reporting refer to the opportunity costs of doing so. The demographic predictors of online reporters show only slight differences from their offline counterparts, but more associations are found regarding the reasons for not reporting. Finally, the implications for policy are discussed.
Fairness and Diversity in Social-Based Recommender Systems

In social networks, the phenomena of homophily and influence explain the fact that friends tend to be similar. Social-based recommenders exploit this observation by incorporating the social structure in collaborative filtering techniques. In practice, these recommenders tend to make friends appear more similar compared to non-socially aware techniques. Various proposals have demonstrated the benefit of incorporating social connections. But at what cost? In this work, we show that there exist users that are mistreated in social recommenders. Specifically, their individual preferences are suppressed more compared to other users in their social circle. We seek to identify who they are and develop techniques that protect them, without severely affecting the effectiveness of the recommender.
Building Knowledge-Based Economies in Africa: A Systematic Review of Policies and Strategies

Compared to other regions of the world, Africa is lagging in its drive towards knowledge-based economies. This study systematically reviews the literature in order to highlight the policies and strategies with which African countries can accelerate their current drive towards building knowledge-based economies. These are discussed in terms of three pillars of the World Bank's knowledge economy framework. They are the indices for (i) education and skilled population, (ii) information and communication technology and (iii) economic incentives and institutional regime.
Surviving the Cryptojungle: Perception and Management of Risk Among North American Cryptocurrency (Non)Users

With the massive growth of cryptocurrency markets in recent years has come an influx of new users and investors, pushing the overall number of owners into the millions. At the same time, the number of distinct cryptocurrencies has exploded to over 4,900. In this burgeoning and chaotic "cryptojungle," new and unexplored incentives and risks drive the behavior of users and non-users of cryptocurrencies. While previous research has focused almost exclusively on Bitcoin, other cryptocurrencies and utility tokens have been ignored. This paper presents findings from an interview study of cryptocurrency users and non-users $$(N=20)$$ . We specifically focus on their perceptions and management of cryptocurrency risks as well as their reasons for or against involvement with cryptocurrencies. Our results suggest that associated risks and mitigation strategies (among other factors) might be specific to a particular crypto-asset and its application area. Further, we identify misunderstandings of both users and non-users that might lead to skewed risk perceptions or dangerous errors. Lastly, we discuss ways of aiding users with managing risks, as well as design implications for coin management tools.
An application and empirical test of the Capability Opportunity Motivation-Behaviour model to data leakage prevention in financial organizations

It is widely agreed that technology alone cannot prevent cyber incidents. Organizations often need to rely on the cooperation of employees, for instance to report cyber incidents and to follow security policies. This research article presents a model of how the psychological constructs capability, opportunity and motivation interact to produce employee security behaviours that are assumed to help prevent data leakage incidents. To validate this model we surveyed 384 bank employees about their data leakage prevention behaviour. Results generally show that capability (i.e., knowledge) is uniquely related to data leakage prevention behaviour, and that motivation and opportunity are uniquely related to capability. Our findings suggest that although knowledge is pivotal for achieving desired behaviour, increasing motivation and opportunity may be key to influence knowledge acquiring and consequently data leakage prevention behaviour. Implications for information security practice are discussed.
Analysis of singular phase based on double random phase encoding using phase retrieval algorithm

We analyze the properties of the singular phase based on phase retrieval. The phase can be estimated from the result of the known-plaintext attack on a typical optical encryption technique, namely double random phase encoding. The key generated by the singular phase for decryption can be used to decrypt only the known ciphertext image used for the attack. However, it is not obvious whether any unknown ciphertext image can be decrypted using the singular key (a phase key image that has the singular property). Therefore, we prepared various ciphertext images by changing two parameters, namely the plaintext image and a random phase mask in the spatial plane, and analyzed the properties of the singular key by decrypting these unknown ciphertext images. The results show that when the unknown ciphertext images generated by changing only the plaintext image are decrypted, the known plaintext pattern is obtained. In contrast, when the unknown ciphertext images generated by changing only the random phase mask are decrypted, no pattern is obtained. These cases both indicate that cryptanalysis failed. Nevertheless, when the known plaintext pattern is obtained, it indicates that the correlation between the known and unknown random phase masks is high, giving the attacker an important clue.
A Network Cost-aware Geo-distributed Data Analytics System

Many geo-distributed data analytics (GDA) systems have focused on the network performance-bottleneck: interdata center network bandwidth to improve performance. Unfortunately, these systems may encounter a cost-bottleneck ($) because they have not considered data transfer cost ($), one of the most expensive and heterogeneous resources in a multi-cloud environment. In this paper, we present Kimchi, a network cost-aware GDA system to meet the cost-performance tradeoff by exploiting data transfer cost heterogeneity to avoid the cost-bottleneck. Kimchi determines cost-aware task placement decisions for scheduling tasks given inputs including data transfer cost, network bandwidth, input data size and locations, and desired cost-performance tradeoff preference. In addition, Kim- chi is also mindful of data transfer cost in the presence of dynamics. A Kimchi prototype has been implemented on Spark and experiments show that it reduces cost by 14% ~ 24% without impacting performance and reduces query execution time by 45% ~ 70% without impacting cost compared to other baseline approaches centralized, vanilla Spark, and bandwidth-aware (e.g. Iridium). More importantly, Kimchi allows applications to explore a much richer cost-performance tradeoff space in a multi-cloud environment.
Don’t @ Me: Experimentally Reducing Partisan Incivility on Twitter

Abstract I conduct an experiment which examines the impact of moral suasion on partisans engaged in uncivil arguments. Partisans often respond in vitriolic ways to politicians they disagree with, and this can engender hateful responses from partisans from the other side. This phenomenon was especially common during the contentious 2016 US Presidential Election. Using Twitter accounts that I controlled, I sanctioned people engaged partisan incivility in October 2016. I found that messages containing moral suasion were more effective at reducing incivility than were messages with no moral content in the first week post-treatment. There were no significant treatment effects in the first day post-treatment, emphasizing the need for research designs that measure effect duration. The type of moral suasion employed, however, did not have the expected differential effect on either Republicans or Democrats. These effects were significantly moderated by the anonymity of the subjects.
Compact Leakage-Free Support for Integrity and Reliability

The memory system is vulnerable to a number of security breaches, e.g., an attacker can interfere with program execution by disrupting values stored in memory. Modern Intel® Software Guard Extension (SGX) systems already support integrity trees to detect such malicious behavior. However, in spite of recent innovations, the bandwidth overhead of integrity+replay protection is non-trivial; state-of-the-art solutions like Synergy introduce average slowdowns of 2.3× for memory-intensive benchmarks. Prior work also implements a tree that is shared by multiple applications, thus introducing a potential side channel. In this work, we build on the Synergy and SGX baselines, and introduce three new techniques. First, we isolate each application by implementing a separate integrity tree and metadata cache for each application; this improves metadata cache efficiency and improves performance by 39%, while eliminating the potential side channel. Second, we reduce the footprint of the metadata. Synergy uses a combination of integrity and error correction metadata to provide low-overhead support for both. We share error correction metadata across multiple blocks, thus lowering its footprint (by 16×) while preventing error correction only in rare corner cases. However, we discover that shared error correction metadata, even with caching, does not improve performance. Third, we observe that thanks to its lower footprint, the error correction metadata can be embedded into the integrity tree. This reduces the metadata blocks that must be accessed to support both integrity verification and chipkill reliability. The proposed Isolated Tree with Embedded Shared Parity (ITESP) yields an overall performance improvement of 64%, relative to baseline Synergy.
Towards cross-task universal perturbation against black-box object detectors in autonomous driving

Deep neural network is the main research branch in artificial intelligence and suitable for many decision-making fields. Autonomous driving and unmanned vehicle often depend on deep neural networks for accurate and reliable detection, classification, and ranging of surrounding objects in real on-road environments, either locally or by swarm intelligence among distributed nodes via 5G channel. But, it has been demonstrated that deep neural networks are vulnerable to well-designed adversarial examples that are imperceptible to human eyes in computer vision tasks. It is valuable to study the vulnerability for enhancing the robustness of neural networks. However, existing adversarial examples against object detection models are image-dependent, so in this paper, we implement adversarial attacks against object detection models using universal perturbations. We find the cross-task, cross-model, and cross-dataset transferability of universal perturbations, we train universal perturbations generator firstly and then add the universal perturbations to the target images in two ways: resizing and pile-up, in order to solve the problem that universal perturbations cannot be directly applied to attack object detection models. We use the transferability of universal perturbations to attack black-box object detection models. In this way, the time cost of generating adversarial examples is reduced. A series of experiments are conducted on PASCAL VOC and MS COCO datasets demonstrating the feasibility of cross-task attacks and proving the effectiveness of our attack on two representative object detectors: regression-based models like YOLOv3 and proposal-based models like Faster R-CNN.