Reviewerly/exHarmony huggingface dataset:
Evading Face Recognition via Partial Tampering of Faces

Advancements in machine learning and deep learning techniques have led to the development of sophisticated and accurate face recognition systems. However, for the past few years, researchers are exploring the vulnerabilities of these systems towards digital attacks. Creation of digitally altered images has become an easy task with the availability of various image editing tools and mobile application such as Snapchat. Morphing based digital attacks are used to elude and gain the identity of legitimate users by fooling the deep networks. In this research, partial face tampering attack is proposed, where facial regions are replaced or morphed to generate tampered samples. Face verification experiments performed using two state-of-the-art face recognition systems, VGG-Face and OpenFace on the CMU-MultiPIE dataset indicates the vulnerability of these systems towards the attack. Further, a Partial Face Tampering Detection (PFTD) network is proposed for the detection of the proposed attack. The network captures the inconsistencies among the original and tampered images by combining the raw and high-frequency information of the input images for the detection of tampered images. The proposed network surpasses the performance of the existing baseline deep neural networks for tampered image detection.
One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary Video

Due to burdensome data requirements, learning from demonstration often falls short of its promise to allow users to quickly and naturally program robots. Demonstrations are inherently ambiguous and incomplete, making correct generalization to unseen situations difficult without a large number of demonstrations in varying conditions. By contrast, humans are often able to learn complex tasks from a single demonstration (typically observations without action labels) by leveraging context learned over a lifetime. Inspired by this capability, our goal is to enable robots to perform one-shot learning of multi-step tasks from observation by leveraging auxiliary video data as context. Our primary contribution is a novel system that achieves this goal by: (1) using a single user-segmented demonstration to define the primitive actions that comprise a task, (2) localizing additional examples of these actions in unsegmented auxiliary videos via a metalearning-based approach, (3) using these additional examples to learn a reward function for each action, and (4) performing reinforcement learning on top of the inferred reward functions to learn action policies that can be combined to accomplish the task. We empirically demonstrate that a robot can learn multi-step tasks more effectively when provided auxiliary video, and that performance greatly improves when localizing individual actions, compared to learning from unsegmented videos.
Computer-Aided Tumor Segmentation from T2-Weighted MR Images of Patient-Derived Tumor Xenografts

Magnetic resonance imaging (MRI) is typically used to detect and assess therapeutic response in preclinical imaging of patient-derived tumor xenografts (PDX). The overarching objective of the work is to develop an automated methodology to detect and segment tumors in PDX for subsequent analyses. Automated segmentation also has the benefit that it will minimize user bias. A hybrid method combining fast k-means, morphology, and level set is used to localize and segment tumor volume from volumetric MR images. Initial centroids of k-means are selected by local density peak estimation method. A new variational model is implemented to exploit the region information by minimizing energy functional in level set. The mask specific initialization approach is used to create a genuine boundary of level set. Performance of tumor segmentation is compared with manually segmented image and to established algorithms. Segmentation results obtained from six metrics are Jaccard score (>80%), Dice score (>85%), F score (>85%), G-mean (>90%), volume similarity matrix (>95%) and relative volume error (<8%). The proposed method reliably localizes and segments PDX tumors and has the potential to facilitate high-throughput analysis of MR imaging in co-clinical trials involving PDX.
Multi-Object Search using Object-Oriented POMDPs

A core capability of robots is to reason about multiple objects under uncertainty. Partially Observable Markov Decision Processes (POMDPs) provide a means of reasoning under uncertainty for sequential decision making, but are computationally intractable in large domains. In this paper, we propose Object-Oriented POMDPs (OO-POMDPs), which represent the state and observation spaces in terms of classes and objects. The structure afforded by OO-POMDPs support a factorization of the agent's belief into independent object distributions, which enables the size of the belief to scale linearly versus exponentially in the number of objects. We formulate a novel Multi-Object Search (MOS) task as an OO-POMDP for mobile robotics domains in which the agent must find the locations of multiple objects. Our solution exploits the structure of OO-POMDPs by featuring human language to selectively update the belief at task onset. Using this structure, we develop a new algorithm for efficiently solving OO-POMDPs: Object-Oriented Partially Observable Monte-Carlo Planning (OOPOMCP). We show that OO-POMCP with grounded language commands is sufficient for solving challenging MOS tasks both in simulation and on a physical mobile robot.
Experts reviews of a cloud forensic readiness framework for organizations

Cloud computing has drastically altered the ways in which it is possible to deliver information technologies (ITs) to consumers as a service. In addition, the concept has given rise to multiple benefits for consumers and organizations. However, such a fast surge in the adoption of cloud computing has led to the emergence of the cloud as a new cybercrime environment, thus giving rise to fresh legal, technical and organizational challenges. In addition to the vast number of attacks that have had an impact on cloud computing and the fact that cloud-based data processing is carried out in a decentralized manner, many other concerns have been noted. Among these concerns are how to conduct a thorough digital investigation in cloud environments and how to be prepared to gather data ahead of time before the occurrence of an incident; indeed, this kind of preparation would reduce the amount of money, time and effort that is expended. As a number of cloud forensics challenges have not received enough attention, this study is motivated by a particular gap in research on the technical, legal and organizational factors that facilitate forensic readiness in organizations that utilize an Infrastructure as a Service (IaaS) model. This paper presents a framework with which to investigate the factors that facilitate the forensic readiness of organizations. This framework was identified by critically reviewing previous studies in the literature and by performing an in-depth examination of the relevant industrial standards. The factors were comprehensively studied and extracted from the literature; then, the factors were analysed, duplicates were removed, and the factors were categorized and synthesized to produce the framework. To obtain reliable results, the research method involved two steps: a literature review, followed by expert reviews. These techniques help us paint a comprehensive picture of the research topic and validate and confirm the results.
Detecting concurrency memory corruption vulnerabilities

Memory corruption vulnerabilities can occur in multithreaded executions, known as concurrency vulnerabilities in this paper. Due to non-deterministic multithreaded executions, they are extremely difficult to detect. Recently, researchers tried to apply data race detectors to detect concurrency vulnerabilities. Unfortunately, these detectors are ineffective on detecting concurrency vulnerabilities. For example, most (90%) of data races are benign. However, concurrency vulnerabilities are harmful and can usually be exploited to launch attacks. Techniques based on maximal causal model rely on constraints solvers to predict scheduling; they can miss concurrency vulnerabilities in practice. Our insight is, a concurrency vulnerability is more related to the orders of events that can be reversed in different executions, no matter whether the corresponding accesses can form data races. We then define exchangeable events to identify pairs of events such that their execution orders can be probably reversed in different executions. We further propose algorithms to detect three major kinds of concurrency vulnerabilities. To overcome potential imprecision of exchangeable events, we also adopt a validation to isolate real vulnerabilities. We implemented our algorithms as a tool ConVul and applied it on 10 known concurrency vulnerabilities and the MySQL database server. Compared with three widely-used race detectors and one detector based on maximal causal model, ConVul was significantly more effective by detecting 9 of 10 known vulnerabilities and 6 zero-day vulnerabilities on MySQL (four have been confirmed). However, other detectors only detected at most 3 out of the 16 known and zero-day vulnerabilities.
Faster First-Order Methods for Stochastic Non-Convex Optimization on Riemannian Manifolds

First-order non-convex Riemannian optimization algorithms have gained recent popularity in structured machine learning problems including principal component analysis and low-rank matrix completion. The current paper presents an efficient Riemannian Stochastic Path Integrated Differential EstimatoR (R-SPIDER) algorithm to solve the finite-sum and online Riemannian non-convex minimization problems. At the core of R-SPIDER is a recursive semi-stochastic gradient estimator that can accurately estimate Riemannian gradient under not only exponential mapping and parallel transport, but also general retraction and vector transport operations. Compared with prior Riemannian algorithms, such a recursive gradient estimation mechanism endows R-SPIDER with lower computational cost in first-order oracle complexity. Specifically, for finite-sum problems with n components, R-SPIDER is proved to converge to an 6-approximate stationary point within O(min(n/ε <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ,1/3)) stochastic gradient evaluations, beating the best-known complexity O(n+1/ ε <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ); for online optimization, R-SPIDER is shown to converge with O(1/ε <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) complexity which is, to the best of our knowledge, the first non-asymptotic result for online Riemannian optimization. For the special case of gradient dominated functions, we further develop a variant of R-SPIDER with improved linear rate of convergence. Extensive experimental results demonstrate the advantage of the proposed algorithms over the state-of-the-art Riemannian non-convex optimization methods.
The use of item scores and response times to detect examinees who may have benefited from item preknowledge

According to Wollack and Schoenig (2018, The Sage encyclopedia of educational research, measurement, and evaluation . Thousand Oaks, CA: Sage, 260), benefiting from item preknowledge is one of the three broad types of test fraud that occur in educational assessments. We use tools from constrained statistical inference to suggest a new statistic that is based on item scores and response times and can be used to detect examinees who may have benefited from item preknowledge for the case when the set of compromised items is known. The asymptotic distribution of the new statistic under no preknowledge is proved to be a simple mixture of two χ 2 distributions. We perform a detailed simulation study to show that the Type I error rate of the new statistic is very close to the nominal level and that the power of the new statistic is satisfactory in comparison to that of the existing statistics for detecting item preknowledge based on both item scores and response times. We also include a real data example to demonstrate the usefulness of the suggested statistic.
Robust Student Network Learning

Deep neural networks bring in impressive accuracy in various applications, but the success often relies on heavy network architectures. Taking well-trained heavy networks as teachers, classical teacher-student learning paradigm aims to learn a student network that is lightweight yet accurate. In this way, a portable student network with significantly fewer parameters can achieve considerable accuracy, which is comparable to that of a teacher network. However, beyond accuracy, the robustness of the learned student network against perturbation is also essential for practical uses. Existing teacher-student learning frameworks mainly focus on accuracy and compression ratios, but ignore the robustness. In this paper, we make the student network produce more confident predictions with the help of the teacher network, and analyze the lower bound of the perturbation that will destroy the confidence of the student network. Two important objectives regarding prediction scores and gradients of examples are developed to maximize this lower bound, to enhance the robustness of the student network without sacrificing the performance. Experiments on benchmark data sets demonstrate the efficiency of the proposed approach to learning robust student networks that have satisfying accuracy and compact sizes.
USETL

Growing popularity of serverless functions is driving the need to optimize the execution platform to reduce resource usage and increase the number of functions that can be executed concurrently. This reduces the provider's costs and increases profit. While current serverless solutions use containers and/or virtual machines, we propose a unikernel based design called USETL which is specialized for serverless extract, transform, load (ETL) workloads. Our design is motivated by a number of key observations: serverless functions are stateless, ephemeral and event-driven. Further, each function's specific purpose is known at invocation time. Unikernels are a natural fit for execution contexts with these properties: they are minimal kernels packaged with a single application in a single address space, which makes them incredibly lightweight. Our design removes network and storage components entirely, replacing them with high level APIs tailored to the needs of serverless ETL functions. Virtualizing I/O at the runtime library interface reduces memory and CPU overheads, yielding higher consolidation density.
On the investment credentials of Bitcoin: A cross-currency perspective

We examine diversification capabilities of Bitcoin for a global portfolio spread across six asset classes from the standpoint of investors dealing in five major fiat currencies namely US Dollar, Great Britain Pound, Euro, Japanese Yen and Chinese Yuan. Considering the period of prolonged decline in Bitcoin's value throughout 2018, we employ modified conditional value-at-risk and standard deviation as measures of risk to perform portfolio optimisations across three asset allocation strategies. Results show that portfolios denominated in Japanese Yen, Chinese Yuan and US Dollar account for greater proportion of optimal investment in Bitcoin and exhibit higher improvement in risk-adjusted returns due to investment in Bitcoin. We also perform a comprehensive risk-adjusted evaluation of portfolios with and without Bitcoin to reinforce striking variation in degree of diversification benefits of Bitcoin in a cross-currency context. Taken together, our findings provide insights into sharp disparity in Bitcoin trading volumes across national currencies from a portfolio theory perspective.
Cross-Level Parallel Network for Crowd Counting

Automated people counting in crowd scenes is challenging due to large variations in scale, density, and background clutter. To tackle them, we propose a novel cross-level parallel network (CLPNet) by extracting multiple low-level features from VGG16 and fusing them with specific scale aggregation modules in the high-level stage. To deal with scale variation, we design five different aggregation modules for multiscale fusion. Furthermore, the ground truth is processed skillfully to eliminate the mismatches caused by the scale variation between heads and density maps. To cope with background clutter, cross-level feature fusion is implemented. Higher-level semantic information could effectively separate head from background and regain the lost low-level detailed information. To address the variation of density, we design a parallel network, in which two separate channels focus on different density-level estimation, and attain more accurate counting results. Finally, we evaluate the proposed CLPNet on four representative crowd counting datasets, i.e., ShanghaiTech, UCF_CC_50, WorldExpo'10, and UCF_QNRF. The experimental results demonstrate that with the cross-level and multiscale structure CLPNet achieves superior performance compared with the state-of-the-art crowd counting methods.
Feature fusion by using LBP, HOG, GIST descriptors and Canonical Correlation Analysis for face recognition

Face recognition is the most active research topics in machine vision because of its highly secured demands. The fusion of multiple features can enhance the accuracy of face recognition systems instead of using only one type of feature. However, this leads to increase the storage and processing time. In this work, we apply feature fusion by using Canonical Correlation Analysis to concatenate two different feature sources for coding a facial image. Three popular descriptors (LBP, HOG, GIST) have been investigated for extracting facial features based on block division.
Invertible secret image sharing with steganography and authentication for AMBTC compressed images

Existing secret image sharing (SIS) schemes provided steganography and authentication abilities. But those schemes cannot deal with Absolute Moment Block Truncation Coding (AMBTC) compressed images. Moreover, if the cover image is significant, the distorted stego image cannot be reverted into the original form. In this paper, an invertible SIS scheme with steganography and authentication for AMBTC compressed images is proposed. The AMBTC compressed secret image is shared and embedded into the cover image to generate n AMBTC compressed stego images. To ensure the integrity of the stego image, parity bits are constructed and concealed into stego images. When revealing the secret image, the stego images are verified. By using those successfully verified stego images, distortion-free secret image and cover image are both obtained. Experimental results are demonstrated to show the effectiveness and advantages of the proposed scheme.
The Potential of Chatbots: Analysis of Chatbot Conversations

The idea of utilizing computers for question answering tasks has been around from the early beginning of these systems. First algorithms with the aim to accomplish this were already implemented in the early 1960s. In recent years, chatbots have been gaining enormous popularity in various fields. In the context of business applications, they are considered as useful tools for improving customer relationships. In this paper, chat conversations between customers and the chatbot of a telecommunication company are analysed to find out if these interactions can be used to determine a) users' topics of interests and b) user satisfaction. To reach this goal, chat conversations are interpreted as sequences of events and user inputs are analysed with the help of text mining techniques. The study shows that based on users' written conversational contributions, valuable insights on users' interests and satisfaction can be gained. The majority of users leave the chat conversation after a short period of time if the chatbot was not able to give the desired answer right away. Moreover, a huge number of conversations deal with similar topics. Our results imply that companies offering chatbots must thoroughly analyse the collected data to gain more insights into their customers' needs. Based on our findings, they can improve customers' satisfaction by offering personalized service and implementing real-time feedback.
Don't Punish all of us: Measuring User Attitudes about Two-Factor Authentication

Two-factor authentication (2FA) defends against password compromise by a remote attacker. We surveyed 4,275 students, faculty, and staff at Brigham Young University to measure user sentiment about Duo 2FA one year after the university adopted it. The results were mixed. A majority of the participants felt more secure using Duo and felt it was easy to use. About half of all participants reported at least one instance of being locked out of their university account because of an inability to authenticate with Duo. We found that students and faculty generally had more negative perceptions of Duo than staff. The survey responses reveal some pain points for Duo users. In response, we offer recommendations that reduce the frequency of 2FA for users. We also suggest UI changes that draw more attention to 2FA methods that do not require WiFi, the "Remember Me" setting, and the help utility.
Selective interactions in the quantum Rabi model

We demonstrate the emergence of selective $k$-photon interactions in the strong and ultrastrong coupling regimes of the quantum Rabi model with a Stark coupling term. In particular, we show that the interplay between the rotating and counter-rotating terms produces multi-photon interactions whose resonance frequencies depend, due to the Stark term, on the state of the bosonic mode. We develop an analytical framework to explain these $k$-photon interactions by using time-dependent perturbation theory. Finally, we propose a method to achieve the quantum simulation of the quantum Rabi model with a Stark term by using the internal and vibrational degrees of freedom of a trapped ion, and demonstrate its performance with numerical simulations considering realistic physical parameters.
Multi-Service Mobile Traffic Forecasting via Convolutional Long Short-Term Memories

Network slicing is increasingly used to partition network infrastructure between different mobile services. Precise service-wise mobile traffic forecasting becomes essential in this context, as mobile operators seek to pre-allocate resources to each slice in advance, to meet the distinct requirements of individual services. This paper attacks the problem of multi-service mobile traffic forecasting using a sequence-to-sequence (S2S) learning paradigm and convolutional long short-term memories (ConvL-STMs). The proposed architecture is designed so as to effectively extract complex spatiotemporal features of mobile network traffic and predict with high accuracy the future demands for individual services at city scale. We conduct experiments on a mobile traffic dataset collected in a large European metropolis, demonstrating that the proposed S2S-ConvLSTM can forecast the mobile traffic volume produced by tens of different services in advance of up to one hour, by just using measurements taken during the past hour. In particular, our solution achieves mean absolute errors (MAE) at antenna level that are below 13KBps, outperforming other deep learning approaches by up to 31.2%.