Miracle-dz/UniFAR-FTUs huggingface dataset:
A Least-Privilege Memory Protection Model for Modern Hardware

We present a new least-privilege-based model of addressing on which to base memory management functionality in an OS for modern computers like phones or server-based accelerators. Existing software assumptions do not account for heterogeneous cores with different views of the address space, leading to the related problems of numerous security bugs in memory management code (for example programming IOMMUs), and an inability of mainstream OSes to securely manage the complete set of hardware resources on, say, a phone System-on-Chip. Our new work is based on a recent formal model of address translation hardware which views the machine as a configurable network of address spaces. We refine this to capture existing address translation hardware from modern SoCs and accelerators at a sufficiently fine granularity to model minimal rights both to access memory and configure translation hardware. We then build an executable specification in Haskell, which expresses the model and metadata structures in terms of partitioned capabilities. Finally, we show a fully functional implementation of the model in C created by extending the capability system of the Barrelfish research OS. Our evaluation shows that our unoptimized implementation has comparable (and in some cases) better performance than the Linux virtual memory system, despite both capturing all the functionality of modern hardware addressing and enabling least-privilege, decentralized authority to access physical memory and devices.
Regression analysis of unmeasured confounding

Abstract When studying the causal effect of x on y, researchers may conduct regression and report a confidence interval for the slope coefficient β x ${\beta }_{x}$ . This common confidence interval provides an assessment of uncertainty from sampling error, but it does not assess uncertainty from confounding. An intervention on x may produce a response in y that is unexpected, and our misinterpretation of the slope happens when there are confounding factors w. When w are measured we may conduct multiple regression, but when w are unmeasured it is common practice to include a precautionary statement when reporting the confidence interval, warning against unwarranted causal interpretation. If the goal is robust causal interpretation then we can do something more informative. Uncertainty, in the specification of three confounding parameters can be propagated through an equation to produce a confounding interval. Here, we develop supporting mathematical theory and describe an example application. Our proposed methodology applies well to studies of a continuous response or rare outcome. It is a general method for quantifying error from model uncertainty. Whereas, confidence intervals are used to assess uncertainty from unmeasured individuals, confounding intervals can be used to assess uncertainty from unmeasured attributes.
Object-RPE: Dense 3D Reconstruction and Pose Estimation with Convolutional Neural Networks for Warehouse Robots

We present a system for accurate 3D instance-aware semantic reconstruction and 6D pose estimation, using an RGB-D camera. Our framework couples convolutional neural networks (CNNs) and a state-of-the-art dense Simultaneous Localisation and Mapping (SLAM) system, ElasticFusion, to achieve both high-quality semantic reconstruction as well as robust 6D pose estimation for relevant objects. The method presented in this paper extends a high-quality instance-aware semantic 3D Mapping system from previous work [1] by adding a 6D object pose estimator. While the main trend in CNN-based 6D pose estimation has been to infer object's position and orientation from single views of the scene, our approach explores performing pose estimation from multiple viewpoints, under the conjecture that combining multiple predictions can improve the robustness of an object detection system. The resulting system is capable of producing high-quality object-aware semantic reconstructions of room-sized environments, as well as accurately detecting objects and their 6D poses. The developed method has been verified through experimental validation on the YCB-Video dataset and a newly collected warehouse object dataset. Experimental results confirmed that the proposed system achieves improvements over state-of-the-art methods in terms of surface reconstruction and object pose prediction. Our code and video are available at https://sites.google. com/view/object-rpe.
Joint Extraction of Entities and Relations with a Hierarchical Multi-task Tagging Model

Entity extraction and relation extraction are two indispensable building blocks for knowledge graph construction. Recent works on entity and relation extraction have shown the superiority of solving the two problems in a joint manner, where entities and relations are extracted simultaneously to form relational triples in a knowledge graph. However, existing methods ignore the hierarchical semantic interdependency between entity extraction (EE) and joint extraction (JE), which leaves much to be desired in real applications. In this work, we propose a hierarchical multi-task tagging model, called HMT, which captures such interdependency and achieves better performance for joint extraction of entities and relations. Specifically, the EE task is organized at the bottom layer and JE task at the top layer in a hierarchical structure. Furthermore, the learned semantic representation at the lower level can be shared by the upper level via multi-task learning. Experimental results demonstrate the effectiveness of the proposed model for joint extraction in comparison with the state-of-the-art methods.
Machine Teaching by Domain Experts: Towards More Humane, Inclusive, and Intelligent Machine Learning Systems

This paper argues that a possible way to escape from the limitations of current machine learning (ML) systems is to allow their development directly by domain experts without the mediation of ML experts. This could be accomplished by making ML systems interactively teachable using concepts, definitions, and similar high level knowledge constructs. Pointing to the recent advances in machine teaching technology, we list key technical challenges specific for such expert-centric ML systems, and suggest that they are more humane and possibly more intelligent than traditional ML systems in many domains. We then argue that ML systems could also benefit greatly from being built by a community of experts as much as open source software did, creating more inclusive systems, in terms of enabling different points-of-view about the same corpus of knowledge. Advantages of the community approach over current ways to build ML systems, as well as specific challenges this approach raises, are also discussed in the paper.
Task-Based Language Teaching For Writing of the Students In Indonesian Tourism Polytechnics ( A Case Study of The Fourth Semester Students At Tourism ) Management of Polytechnic of Makassar Muh

This research aims to find the students respond to the implementation of task-based approach in improving students writing performance, students of tourism management of Polytechnic of Makassar who were registered of 2015/2016. The subject were 29 students. In collecting the data concerning the implementation of task-based language teaching, the researcher utilized three methods of data collection including observation, questionnaire and documentation. A case study approach utilized multiple methods either quantitative or qualitative (Beeton,2005; Hind, 2006, Yin, 1994)The result of this study showed that: The students‟ respond to the implementation of the task-based teaching in improving students‟ writing performance were very good respond based on the fourth aspects, they were introduction, pre-task, main-task, and post-task. Keyword: Task-based language teaching, Task, Language teaching approach, writing performance, Students. --------------------------------------------------------------------------------------------------------------------------------Date of Submission: 01-02-2018 Date of acceptance: 19-02-2018 ---------------------------------------------------------------------------------------------------------------------
ROBUST BAYESIAN ANALYSIS WITH DISTRIBUTION BANDS

Received: Revised Version: January 2, 1995 Abstract Given a random variable with distribution indexed by a one dimensional parameter Θ, robustness of Bayesian analysis is considered when the prior cdf of θ lies in the distribution band Γ = { F : FL < F < I'};}. Such a prior class includes as special cases well known metric neighborhoods of a fixed cdf such as Kolmogorov' and Levy neighborhoods. General methods are described for finding rangée of ratio-linear posterior quantities and posterior quantiles over Γ. These methods are used to compare the sensitivity of different Bayes estimates arising out of different loss functions. Several examples illustrate applications of these ideas.
Effect of a second compact object on stable circular orbits

We investigate how stable circular orbits around a main compact object appear depending on the presence of a second one by using the Majumudar-Papapetrou dihole spacetime with different masses. While the parameter range of the separation of the two objects is divided due to the appearance of stable circular orbits, this division depends on its mass ratio. We show that the mass ratio range separates into four parts, and find three critical values as the boundaries.
Revisiting the Spectral and Timing Properties of NGC 4151

NGC 4151 is the brightest Seyfert 1 nucleus in X-rays. It was the first object to show short time delays in the Fe K band, which were attributed to relativistic reverberation, providing a new tool for probing regions at the black hole scale. Here we report the results of a large XMM-Newton campaign in 2015 to study these short delays further. Analyzing high-quality data that span timescales between hours and decades, we find that neutral and ionized absorption contributes significantly to the spectral shape. Accounting for their effects, we find no evidence for a relativistic reflection component, contrary to early work. Energy-dependent lags are significantly measured in the new data, but with an energy profile that does not resemble a broad iron line, in contrast to the old data. The complex lag-energy spectra, along with the lack of strong evidence for a relativistic spectral component, suggest that the energy-dependent lags are produced by absorption effects. The long-term spectral variations provide new details on the variability of the narrow Fe Kα line. We find that its variations are correlated with, and delayed with respect to, the primary X-ray continuum. We measure a delay of days, implying an origin in the inner broad-line region (BLR). The delay is half the Hβ line delay, suggesting a geometry that differs slightly from the optical BLR.
Advanced Analytics and AI: Impact, Implementation, and the Future of Work

Once thought of as science fiction, major corporations are already beginning to use cognitive systems to assist in providing wealth advice and also in medication treatment. The use of Cognitive Analytics/Artificial Intelligence (AI) Systems is set to accelerate, with the expectation that it’ll be considered ‘mainstream’ in the next 5 – 10 years. It’ll change the way we as individuals interact with data and systems—and the way we run our businesses.
See More Than Once - Kernel-Sharing Atrous Convolution for Semantic Segmentation

The state-of-the-art semantic segmentation solutions usually leverage different receptive fields via multiple parallel branches to handle objects with different sizes. However, employing separate kernels for individual branches degrades the generalization and representation abilities of the network, and the amount of parameters increases by the times of the number of branches. To tackle this problem, we propose a novel network structure namely Kernel-Sharing Atrous Convolution (KSAC), where branches of different receptive fields share the same kernel, i.e., let a single kernel `see' the input feature maps more than once with different receptive fields, to facilitate communication among branches and perform `feature augmentation' inside the network. Experiments conducted on the benchmark VOC 2012 dataset show that the proposed sharing strategy can not only boost network's generalization and representation abilities but also reduce the model complexity significantly. Specifically, when compared with DeepLabV3+ equipped with MobileNetv2 backbone, 33% parameters are reduced together with an mIOU improvement of 0.6%. When Xception is used as the backbone, the mIOU is elevated from 83.34% to 85.96% with about 10M parameters saved. In addition, different from the widely used ASPP structure, our proposed KSAC is able to further improve the mIOU by taking benefit of wider context with larger atrous rates.
Generation and evaluation of hyperspectral 3D surface models based on a structured light system with hyperspectral snapshot mosaic sensors

Recently, a number of hyperspectral cameras with novel image sensors that are equipped with a monolithic pixel-by-pixel filter array have been introduced onto the market. In this contribution, we describe the design and implementation of a structured light 3D sensor consisting of two such snapshot mosaic sensors, each with 25 different spectral bands between 600 and 975 nm, and a broadband GOBO projector that images varying, aperiodic sinusoidal patterns into the measurement volume. We characterize the system with regard to accuracy, and we present measurements of different objects illustrating the benefits of hyperspectral 3D sensors.
TEST: an End-to-End Network Traffic Examination and Identification Framework Based on Spatio-Temporal Features Extraction

With more encrypted network traffic gets involved in the Internet, how to effectively identify network traffic has become a top priority in the field. Accurate identification of the network traffic is the footstone of basic network services, say QoE, bandwidth allocation, and IDS. Previous identification methods either cannot deal with encrypted traffics or require experts to select tons of features to attain a relatively decent accuracy.In this paper, we present a Deep Learning based end-to-end network traffic identification framework, termed TEST, to avoid the aforementioned problems. CNN and LSTM are combined and implemented to help the machine automatically extract features from both special and time-related features of the raw traffic. The presented framework has two layers of structure, which made it possible to attain a remarkable accuracy on both encrypted traffic classification and intrusion detection tasks. The experimental results demonstrate that our model can outperform previous methods with a state-of-the-art accuracy of 99.98%.
Personalized Hashtag Recommendation for Micro-videos

Personalized hashtag recommendation methods aim to suggest users hashtags to annotate, categorize, and describe their posts. The hashtags, that a user provides to a post (e.g., a micro-video), are the ones which in her mind can well describe the post content where she is interested in. It means that we should consider both users' preferences on the post contents and their personal understanding on the hashtags. Most existing methods rely on modeling either the interactions between hashtags and posts or the interactions between users and hashtags for hashtag recommendation. These methods have not well explored the complicated interactions among users, hashtags, and micro-videos. In this paper, towards the personalized micro-video hashtag recommendation, we propose a Graph Convolution Network based Personalized Hashtag Recommendation (GCN-PHR) model, which leverages recently advanced GCN techniques to model the complicate interactions among and learn their representations. In our model, the users, hashtags, and micro-videos are three types of nodes in a graph and they are linked based on their direct associations. In particular, the message-passing strategy is used to learn the representation of a node (e.g., user) by aggregating the message passed from the directly linked other types of nodes (e.g., hashtag and micro-video). Because a user is often only interested in certain parts of a micro-video and a hashtag is typically used to describe the part (of a micro-video) that the user is interested in, we leverage the attention mechanism to filter the message passed from micro-videos to users and hashtags, which can significantly improve the representation capability. Extensive experiments have been conducted on two real-world micro-video datasets and demonstrate that our model outperforms the state-of-the-art approaches by a large margin.
An Efficient Characterization of Complex-Balanced, Detailed-Balanced, and Weakly Reversible Systems

Very often, models in biology, chemistry, physics, and engineering are systems of polynomial or power-law ordinary differential equations, arising from a reaction network. Such dynamical systems can be generated by many different reaction networks. On the other hand, networks with special properties (such as reversibility or weak reversibility) are known or conjectured to give rise to dynamical systems that have special properties: existence of positive steady states, persistence, permanence, and (for well-chosen parameters) complex balancing or detailed balancing. These last two are related to thermodynamic equilibrium, and therefore the positive steady states are unique and stable. We describe a computationally efficient characterization of polynomial or power-law dynamical systems that can be obtained as complex-balanced, detailed-balanced, weakly reversible, and reversible mass-action systems.
Unequally Sub-Connected Architecture for Hybrid Beamforming in Massive MIMO Systems

A variety of hybrid analog-digital beamforming architectures have recently been proposed for massive multiple-input multiple-output (MIMO) systems to reduce energy consumption and the cost of implementation. In the analog processing network of these architectures, the practical sub-connected structure requires lower power consumption and hardware complexity than the fully connected structure but cannot fully exploit the beamforming gains, which leads to a loss in overall performance. In this work, we propose a novel unequal sub-connected architecture for hybrid combining at the receiver of a massive MIMO system that employs unequal numbers of antennas in sub-antenna arrays. The optimal design of the proposed architecture is analytically derived, and includes antenna allocation and channel ordering schemes. Simulation results show that an enhancement of up to 10% can be attained in the total achievable rate by unequally assigning antennas to sub-arrays in the sub-connected system at the cost of a marginal increase in power consumption. Furthermore, in order to reduce the computational complexity involved in finding the optimal number of antennas connected to each radio frequency (RF) chain, we propose three low-complexity antenna allocation algorithms. The simulation results show that they can yield a significant reduction in complexity while achieving near-optimal performance.
Multiple Purposes, Multiple Problems: A User Study of Consent Dialogs after GDPR

Abstract The European Union’s General Data Protection Regulation (GDPR) requires websites to ask for consent to the use of cookies for specific purposes. This enlarges the relevant design space for consent dialogs. Websites could try to maximize click-through rates and positive consent decision, even at the risk of users agreeing to more purposes than intended. We evaluate a practice observed on popular websites by conducting an experiment with one control and two treatment groups (N = 150 university students in two countries). We hypothesize that users’ consent decision is influenced by (1) the number of options, connecting to the theory of choice proliferation, and (2) the presence of a highlighted default button (“select all”), connecting to theories of social norms and deception in consumer research. The results show that participants who see a default button accept cookies for more purposes than the control group, while being less able to correctly recall their choice. After being reminded of their choice, they regret it more often and perceive the consent dialog as more deceptive than the control group. Whether users are presented one or three purposes has no significant effect on their decisions and perceptions. We discuss the results and outline policy implications.
Coherence of temperature and velocity superstructures in turbulent Rayleigh–Bénard flow

We investigate the interplay between large-scale patterns, so-called superstructures, in the fluctuation fields of temperature $\unicode[STIX]{x1D703}$ and vertical velocity $w$ in turbulent Rayleigh–Bénard convection at large aspect ratios. Earlier studies suggested that velocity superstructures were smaller than their thermal counterparts in the centre of the domain. However, a scale-by-scale analysis of the correlation between the two fields employing the linear coherence spectrum reveals that superstructures of the same size exist in both fields, which are almost perfectly correlated. The issue is further clarified by the observation that, in contrast to the temperature, and unlike assumed previously, superstructures in the vertical-velocity field do not result in a peak in the power spectrum of $w$. The origin of this difference is traced back to the production terms of the $\unicode[STIX]{x1D703}$ and $w$ variance. These results are confirmed for a range of Rayleigh numbers $Ra=10^{5}{-}10^{9}$; the superstructure size is seen to increase monotonically with $Ra$. Furthermore, the scale distribution of the temperature fluctuations in particular is pronouncedly bimodal. In addition to the large-scale peak caused by the superstructures, there exists a strong small-scale peak. This ‘inner peak’ is most intense at a distance of $\unicode[STIX]{x1D6FF}_{\unicode[STIX]{x1D703}}$ from the wall and is associated with structures of size ${\approx}10\unicode[STIX]{x1D6FF}_{\unicode[STIX]{x1D703}}$, where $\unicode[STIX]{x1D6FF}_{\unicode[STIX]{x1D703}}$ is the thermal boundary layer thickness. Finally, based on the vertical coherence relative to a reference height of $\unicode[STIX]{x1D6FF}_{\unicode[STIX]{x1D703}}$, a self-similar structure is identified in the velocity field (vertical and horizontal components) but not in the temperature.
Hardness Amplification of Optimization Problems

In this paper, we prove a general hardness amplification scheme for optimization problems based on the technique of direct products. We say that an optimization problem $\Pi$ is direct product feasible if it is possible to efficiently aggregate any $k$ instances of $\Pi$ and form one large instance of $\Pi$ such that given an optimal feasible solution to the larger instance, we can efficiently find optimal feasible solutions to all the $k$ smaller instances. Given a direct product feasible optimization problem $\Pi$, our hardness amplification theorem may be informally stated as follows: If there is a distribution $\mathcal{D}$ over instances of $\Pi$ of size $n$ such that every randomized algorithm running in time $t(n)$ fails to solve $\Pi$ on $\frac{1}{\alpha(n)}$ fraction of inputs sampled from $\mathcal{D}$, then, assuming some relationships on $\alpha(n)$ and $t(n)$, there is a distribution $\mathcal{D}'$ over instances of $\Pi$ of size $O(n\cdot \alpha(n))$ such that every randomized algorithm running in time $\frac{t(n)}{poly(\alpha(n))}$ fails to solve $\Pi$ on $\frac{99}{100}$ fraction of inputs sampled from $\mathcal{D}'$. As a consequence of the above theorem, we show hardness amplification of problems in various classes such as NP-hard problems like Max-Clique, Knapsack, and Max-SAT, problems in P such as Longest Common Subsequence, Edit Distance, Matrix Multiplication, and even problems in TFNP such as Factoring and computing Nash equilibrium.