Reviewerly/exHarmony huggingface dataset:
MeltdownDetector: A runtime approach for detecting meltdown attacks

In this work, we present a runtime approach, called MeltdownDetector, for detecting, isolating, and preventing ongoing Meltdown attacks that operate by causing segmentation faults. Meltdown exploits a hardware vulnerability that allows a malicious process to access memory locations, which do not belong to the process, including the physical and kernel memory. The proposed approach is based on a simple observation that in order for a Meltdown attack to be worthwhile, either a single byte of data located at a particular memory address or a sequence of consecutive memory addresses (i.e., sequence of bytes) need to be read, so that a meaningful piece of information can be extracted from the data leaked. MeltdownDetector, therefore, monitors segmentation faults occurring at memory addresses that are close to each other and issues a warning at runtime when these faults become "suspicious." Furthermore, MeltdownDetector flushes the cache hierarchy after every suspicious segmentation fault, which, in turn, prevents any information leakage. In the experiments, MeltdownDetector successfully detected all the attacks and correctly pinpointed all the malicious processes involved in these attacks and did so without issuing any false alarms and without leaking even a single byte of data. Furthermore, the runtime overhead of the fastest MeltdownDetector implementation was about 1%, on average.
Probabilistic Approach to Robot Motion Planning in Dynamic Environments

Four major approaches to robot motion planning in dynamic environments are discussed: probabilistic robot, probabilistic collision state (PCS), partially closed-loop receding horizon control (PCLRHC) and gross hidden Markov model (GHMM). A comparison of three mapping techniques, Kalman filter, expectation and maximization algorithm and Markov model, is presented. The PCS method is the probabilistic extension of inevitable collision state, which is found to be the safest motion planning method. The concept of open-loop and partially closed-loop receding horizon control (OLRHC and PCLRHC) is compared critically, and the algorithms are benchmarked. GHMM is the best suited method for environments with limited space and dynamic environment due to human interactions. GHMM parameters and structure are evaluated using an incremental “learn-and-predict” approach. For exploring GHMM, we simulated a cafeteria with eight tables to be served by a robot, considering three different arrangements of tables along with convex and concave obstacles, and obtained the path length and time taken for a Hamiltonian path. During the simulation, it was observed that for a given static or dynamic environment, the concavity of the obstacles is what makes the scenario a complex one.
Manifold learning for user profiling and identity verification using motion sensors

Mobile devices are becoming ubiquitous and being increasingly used for data-sensitive activities such as communication, personal media storage, and banking. The protection of such data commonly relies on passwords and biometric traits such as fingerprints. These methods perform the user authentication sporadically and often require action from the user, which may make them susceptible to spoofing attacks. This scenario can be mitigated if we bring to bear motion-sensing based methods for authentication, which operate continuously and without requiring user action, hence are harder to attack. Such methods could be used allied with traditional authentication methods or on their own. This paper explores this idea in a novel user-agnostic approach for identity verification based on motion traits acquired by mobile sensors. The proposed approach does not require user-specific training before deployment in mobile devices nor does it require any extra sensor in the device. This solution is capable of learning a user profiling manifold from a small user subset and extend it to unknown users. We validated the proposal on two public datasets. The reported experiments demonstrate remarkable results under a cross-dataset protocol and an open-set setup. Moreover, we performed several analyses aiming at answering critical questions of a biometric method and the presented solution.
Rapid stabilization of time delay stochastic quantum systems based on continuous measurement feedback

Based on continuous measurement feedback, this paper addresses the rapid stabilization of eigenstates and Bell states for stochastic quantum systems, in which the time of computing control input is considered by using time delay control. For the rapidity of quantum state stabilization, a Lyapunov-LaSalle-like theorem is proposed, based on which the state space is divided into two subspaces, and two Lyapunov functions are constructed to be applied in the corresponding subspace. The desired target state is located in one subspace, while other equilibria are contained in the other subspace. The controls of rapidly stabilizing target state are designed based on the two Lyapunov functions, which can make sure the system trajectories only transit through the boundary between the two subspaces no more than 2 times from any initial state so that the stabilization time is shortened. The rapidity is proved, and numerical simulations are also performed to verify the rapidity by comparing with the existing controls in related literature.
Quantum multipartite maskers vs. quantum error-correcting codes

Since masking of quantum information was introduced by Modi et al. in [PRL 120, 230501 (2018)], many discussions on this topic have been published. In this paper, we consider relationship between quantum multipartite maskers (QMMs) and quantum error-correcting codes (QECCs). We say that a subset $Q$ of pure states of a system $K$ can be masked by an operator $S$ into a multipartite system $\H^{(n)}$ if all of the image states $S|\psi\>$ of states $|\psi\>$ in $Q$ have the same marginal states on each subsystem. We call such an $S$ a QMM of $Q$. By establishing an expression of a QMM, we obtain a relationship between QMMs and QECCs, which reads that an isometry is a QMM of all pure states of a system if and only if its range is a QECC of any one-erasure channel. As an application, we prove that there is no an isometric universal masker from $\C^2$ into $\C^2\otimes\C^2\otimes\C^2$ and then the states of $\C^3$ can not be masked isometrically into $\C^2\otimes\C^2\otimes\C^2$. This gives a consummation to a main result and leads to a negative answer to an open question in [PRA 98, 062306 (2018)]. Another application is that arbitrary quantum states of $\C^d$ can be completely hidden in correlations between any two subsystems of the tripartite system $\C^{d+1}\otimes\C^{d+1}\otimes\C^{d+1}$, while arbitrary quantum states cannot be completely hidden in the correlations between subsystems of a bipartite system [PRL 98, 080502 (2007)].
Multi-Device Collaboration in Virtual Environments

We present a multi-device collaboration principle for virtual environments, using a combination of virtual and augmented reality (VR/AR) technology, used in the context of two educational applications, a virtual nasal cavity, and a visualisation of earthquake data. A head-mounted display (HMD) and a 3D-tracked tablet create two views of a shared virtual space. This allows two users to collaborate, utilising the strengths of each of the two technologies, e.g., intuitive spatial navigation and interaction in VR, and touch control of the visualisation parameters via the AR tablet. Touch gestures on the tablet are translated into a pointer ray in VR, so the users can easily indicate spatial features. The underlying networking infrastructure allows for an extension of this application to more than two users and across different rendering platforms.
Gender Preference and Difference in Behavior Modeling in Fitness Applications: A Mixed-Method Approach

In recent years, the employment of behavior models to motivate behavior change has become a global trend in fitness application design. However, there is hardly any large-scale study of these applications to understand users’ exercise-type preferences, their drivers and barriers, and the potential of employing them for gender-based tailoring. To bridge this gap, we conducted a mixed-method study among 669 participants to investigate users’ exercise-type preferences (their drivers and barriers) and how they and gender can impact users’ social-cognitive beliefs and projected performance of bodyweight exercises. Firstly, we presented to the study participants a behavior model performing push-up or squat bodyweight exercise in a fitness application and asked them to rate their perceived self-efficacy, self-regulation, outcome expectation, and projected (exercise) performance level as observers of the behavior model. Secondly, we presented the study participants with a preselected list of commonly employed exercise types in fitness applications and requested them to identify their most/least preferred, and the reasons behind their choices. Our results showed that there were differences between both genders in their exercise-type preferences, perceived self-efficacy and projected exercise performance level. Males prefer push-up, squat, crunch, plank, and chair dip the most, with effectiveness being the most important driver, followed by ease of performance and improvement of the physique, look, and appearance. On the other hand, females prefer squat, crunch, jumping jack, step up, and plank the most, with ease of performance being the most important driver, followed by improvement of the physique, look, appearance, and effectiveness. Moreover, males prefer running in place the least, while females prefer push-up the least, with perceived difficulty being the greatest barrier for both genders. Moreover, our analysis of variance supported the female’s least preference for a push-up. Females have a lower perceived self-efficacy and projected performance level for push-up than males. We discussed the implications of our findings and provided guidelines for tailoring fitness applications on the market to users’ preferences and gender.
An Eye Gaze Tracking Method of Virtual Reality Headset Using A Single Camera and Multi-light Source

Abstract For the problems the hardware configuration of multi-camera and multi-light source is complicated and the eye image of little auxiliary light source is too dark in the eye gaze tracking system, A new eye gaze tracking system based on two-dimensional mapping model is proposed, which achieved the transformation from two-dimensional coordinates to three-dimensional coordinates combined attitude angle of virtual reality headset. In this paper, the mapping relationship between visual features and eye gaze points is established by improving the method of pupil image recognition and eye gaze estimation based pupil-corneal, which the accuracy of eye gaze estimation is further improved. The experimental results show that the error of eye gaze tracking system is less than 1.1 °, which achieved the good effect of eye gaze tracking.
A Method of Pedestrians Counting Based on Deep Learning

In order to realize real-time and accurate population statistics of pedestrians in video, a counting method based on deep learning is proposed. Firstly, the anchor boxes of the detection model is optimized by k-means clustering method. Secondly, the deep learning model YOLO-v3 is trained by pedestrian images obtained in real scenes. Then Deep sort online multi-object tracking algorithm is used to track multiple pedestrians and obtain their trajectories respectively. Finally, the number of pedestrians is determined by the counting line. The counting accuracy of this method is up to 89.2%, and the detection time of each frame is up to 65ms. In addition, it has strong scene adaptability and good robustness, which can meet the requirements of real-time pedestrian counting.
Hyperparameter Optimization Using Sustainable Proof of Work in Blockchain

Hyperparameters are pivotal for machine learning models. The success of efficient calibration, often surpasses the results obtained by devising new approaches. Traditionally, human intervention is required to tune the models, however, this obtuse outlook restricts the proficiency and competence. Automating this crucial characteristic of learning sustainably, proffers a significant boost in performance and cost optimization. Blockchain technology has revolutionized industries utilizing its Proof-of-Work algorithms for consensus. This complicated solution generates a lot of useless computations across the nodes attached to the network and thus, fritters away a huge amount of precious energy. In this paper, we propose to exploit these inane computations for training deep learning models instead of calculating purposeless hash values, thus, suggesting a new consensus schema. This work distinguishes itself from other related works by capitalizing on the parallel processing prospects it generates for hyperparameter tuning of complex deep learning models. We address this aspect through the framework of Bayesian optimization which is an effective methodology for the global optimization of functions with expensive evaluations. We call our work, Proof of Deep Learning with Hyperparameter Optimization (PoDLwHO).
An application of coherence-based method for earthquake detection and microseismic monitoring (Irpinia fault system, Southern Italy)

A coherence-based earthquake detection technique was applied to continuous (1 year) waveform data recorded along the Irpinia fault system (Southern Italy). The earthquake detection was performed using coherent P- and S-wave arrivals recorded by the dense seismic network operating in Irpinia and assuming a local velocity model. We applied a strategy to simultaneously detect and locate earthquakes and to discriminate among true and false detections using an automated and fast procedure, able to process 1 year of data in ~ 1.75 days. The final catalogue of automatically retrieved earthquakes shows a performance improvement with respect to the standard monitoring practices, with an increase in the number of detected small events of about a factor three with respect to the automatic Earth-worm Binder implemented in ISNet and decreases in completeness magnitude of almost half unit magnitude.
Exact solution of the two-mode quantum Rabi model

An analytical method is developed to study the two-mode quantum Rabi model. For certain specific parameter conditions, especially for the resonant conditions, we obtain an infinite number of the exact solutions of the eigenfunctions and associated energies. It is shown that there exist new types of the exact energies which do not correspond to the level-crossings. Our analytical method may find applications in some related models.
Integrating ethnomathematics into augmented reality technology: exploration, design, and implementation in geometry learning

Abstract This study aims to integrate ethnomatematics into augmented reality technology in learning geometry. The research method used was ADDIE (Analysis, Design, Development, Implementation, and Evaluation) approach. The stages used in this study consisted of (1) exploration; (2) design, (3) development; and (4) implementation. This study used two data analysis techniques: (1) qualitative was conducted for exploration studies, describing the design and development process; (2) quantitative was conducted to investigate the result of the use of integrated ethnomathematics AR technology implementation to 56 Mathematics pre-service teachers’ at Indramayu, Indonesia. The results showed that (1) ethnomathematics values in the form of uma lengge traditional house, ethnic karo houses, wa rebo traditional house, demak grand mosque, kaghati kolape kite can be used as marker objects on augmented reality technology and to explain geometry concept;. (2) The design stages consist of designing integration concepts, AR interfaces, and learning system development;. (3) the results of implementation analysis can (a) help to increase learning interaction activities (b) improve the understanding of concepts and geometry visualization, (c) provide information to Mathematics pre-service teachers’related to ethnomathematics and local wisdom in Indonesia.
Law, Endowment and Inequality in Access to Finance

Theoretical work suggests an ambiguous relationship between the strength of institutions and the distribution of access to finance. Using a sample of listed firms from 70 countries, this study constructs country-level measures of inequality in access to external finance, and explores its relation to institutions. We show that inequality of access is positively related to financial development as well as inequality in the distribution of firm size, firm revenue, and residents’ incomes. Countries with stronger investor protection for equity and debt have higher inequality in equity and debt financing respectively, presumably as a result of higher efficiency in fund allocation. Finally, we find that the historical determinants of institutions, including the civil law tradition and the disease environment encountered by colonizers, are negatively related to inequality in access to external finance.
The Profile of Ponzi Scheme Victims in China and the Characteristics of Their Decision-making Process

Ponzi schemes have begun flourishing in China in recent years. However, there is a dearth of empirical research on the victims of Ponzi schemes. The current study investigated the demographic characteristics of the victims of a Ponzi scheme based on archived data of 698 people, 30 of whom were invited to participate in in-depth interviews. Based on the interview data, we investigated why the victims invested in the scheme the first time, why they invested in the same scheme repeatedly, and why many of them relentlessly participated in other similar schemes after the disclosure of the current one without effectively learning from their failures. Inspired by institutional anomie theory, the current study suggests that specific sociocultural contexts (i.e., high living expenses, a high inflation rate, fake advertisements from newspapers, an inadequate banking system, and an ambiguous legal system) play vital roles in shaping victims’ motivation, decision-making, and investment behaviors. The practical implications of the current study are also discussed.
Cascaded Global Context Convolutional Neural Network for Brain Tumor Segmentation

A cascade of global context convolutional neural networks is proposed to segment multi-modality MR images with brain tumor into three subregions: enhancing tumor, whole tumor and tumor core. Each network is a modification of the 3D U-Net consisting of residual connection, group normalization and deep supervision. In addition, we apply Global Context (GC) block to capture long-range dependency and inter-channel dependency. We use a combination of logarithmic Dice loss and weighted cross entropy loss to focus on less accurate voxels and improve the accuracy. Experiments with BraTS 2019 validation set show the proposed method achieved average Dice scores of 0.77338, 0.90712, 0.83911 for enhancing tumor, whole tumor and tumor core, respectively. The corresponding values for BraTS 2019 testing set were 0.79303, 0.87962, 0.82887 for enhancing tumor, whole tumor and tumor core, respectively.
Towards Improving the Efficacy of Code-Based Verification in Internet Voting

End-to-end verifiable Internet voting enables a high level of election integrity. Cast-as-intended verification, in particular, allows voters to verify that their vote has been correctly cast, even in the presence of malicious voting devices. One cast-as-intended verification approach is code-based verification, used since 2015 in legally-binding Swiss elections. We evaluated the Swiss paper-based polling sheet and voting interface, focusing on how well it supported voters in verifying their votes. We uncovered several potential issues related to manipulation detection. We improved the paper-based polling sheet and voting interface accordingly. Then, we carried out a between-subjects lab study with 128 participants to compare the original and improved sheet and interface wrt. usability and its effectiveness in supporting manipulation detection. Our improvements significantly enhanced detection. Our study delivered insights into participants’ somewhat ineffectual reactions to detected anomalies, i.e. starting over again and trying to cast the same vote again, or calling the telephone number provided by the interface. This problem is likely to manifest in any verifiable voting system and thus needs to be addressed as future work.
Generalized sequential state discrimination for multiparty QKD and its optical implementation

Abstract Sequential state discrimination is a strategy for N separated receivers. As sequential state discrimination can be applied to multiparty quantum key distribution (QKD), it has become one of the relevant research fields in quantum information theory. Up to now, the analysis of sequential state discrimination has been confined to special cases. In this report, we consider a generalization of sequential state discrimination. Here, we do not limit the prior probabilities and the number of quantum states and receivers. We show that the generalized sequential state discrimination can be expressed as an optimization problem. Moreover, we investigate a structure of generalized sequential state discrimination for two quantum states and apply it to multiparty QKD. We demonstrate that when the number of receivers is not too many, generalized sequential state discrimination for two pure states can be suitable for multiparty QKD. In addition, we show that generalized sequential state discrimination for two mixed states can be performed with high optimal success probability. This optimal success probability is even higher than those of quantum reproducing and quantum broadcasting strategy. Thus, generalized sequential state discrimination of mixed states is adequate for performing multiparty QKD. Furthermore, we prove that generalized sequential state discrimination can be implemented experimentally by using linear optics. Finally, we analyze the security of multiparty QKD provided by optimal sequential state discrimination. Our analysis shows that the multiparty QKD guarantees nonzero secret key rate even in low channel efficiency.
BI-Modal Ultrasound Breast Cancer Diagnosis Via Multi-View Deep Neural Network SVM

B-mode ultrasound and ultrasound elastography are two routine diagnostic modalities for breast cancer. Unfortunately, few efforts have paid attention to learn bi-modal ultrasound jointly. By combining multi-view deep mapping-based feature representation with SVM-based classification, we proposed a novel integrated deep learning model, multi-view deep neural network support vector machine (MDNNSVM), to achieve breast cancer diagnosis on bi-modal ultrasound. In particular, multi-view representation learning extracts and fuses the various ultrasound characteristics (also including hardness information of soft tissue) effectively to differentiate benign breast lesions from malignant. Further, the SVM-based objective function is used to learn a classifier jointly with DNN to improve diagnostic accuracy significantly. The experimental results on a real-world dataset of breast cancer verify the effectiveness of the MDNNSVM with the best value of classification accuracy (86.36%) and AUC (0.9079).