Reviewerly/exHarmony huggingface dataset:
Adaptive Cyber Defense Against Multi-Stage Attacks Using Learning-Based POMDP

Growing multi-stage attacks in computer networks impose significant security risks and necessitate the development of effective defense schemes that are able to autonomously respond to intrusions during vulnerability windows. However, the defender faces several real-world challenges, e.g., unknown likelihoods and unknown impacts of successful exploits. In this article, we leverage reinforcement learning to develop an innovative adaptive cyber defense to maximize the cost-effectiveness subject to the aforementioned challenges. In particular, we use Bayesian attack graphs to model the interactions between the attacker and networks. Then we formulate the defense problem of interest as a partially observable Markov decision process problem where the defender maintains belief states to estimate system states, leverages Thompson sampling to estimate transition probabilities, and utilizes reinforcement learning to choose optimal defense actions using measured utility values. The algorithm performance is verified via numerical simulations based on real-world attacks.
LOCC Distinguishable Orthogonal Product States with Least Entanglement Resource

In this paper, we construct 2n − 1 locally indistinguishable orthogonal product states in $$ {\mathbb{C}}^n\otimes {\mathbb{C}}^4\kern1em \left(n>4\right) $$ and $$ {\mathbb{C}}^n\otimes {\mathbb{C}}^5\kern1em \left(n\ge 5\right) $$ respectively. Moreover, a set of locally indistinguishable orthogonal product states with 2(n + 2l) − 8 elements in $$ {\mathbb{C}}^n\otimes {\mathbb{C}}^{2l}\kern1em \left(n\ge 2l>4\right) $$ and a class of locally indistinguishable orthogonal product states with 2(n + 2k + 1) − 7 elements in $$ {\mathbb{C}}^n\otimes {\mathbb{C}}^{2k+1}\kern1em \left(n\ge 2k+1>5\right) $$ are also constructed respectively. These classes of quantum states are then shown to be distinguishable by local operation and classical communication (LOCC) using a suitable $$ {\mathbb{C}}^2\otimes {\mathbb{C}}^2 $$ maximally entangled state respectively.
Parametric down-conversion beyond the semiclassical approximation

Using a perturbative approach, we investigate the parametric down-conversion process without the semi-classical approximation. A Wigner functional formalism, which incorporates both the spatiotemproal degrees of freedom and the particle-number degrees of freedom is used to perform the analysis. First, we derive an evolution equation for the down-conversion process in the nonlinear medium. Then we use the perturbative approach to solve the equation. The leading order contribution is equivalent to the semi-classical solution. The next-to-leading order contribution provides a solution that includes the evolution of the pump field as a quantum field.
Model Dispersion with PRISM: An Alternative to MCMC for Rapid Analysis of Models

We have built PRISM, a "Probabilistic Regression Instrument for Simulating Models". PRISM uses the Bayes linear approach and history matching to construct an approximation ('emulator') of any given model, by combining limited model evaluations with advanced regression techniques, covariances and probability calculations. It is designed to easily facilitate and enhance existing Markov chain Monte Carlo (MCMC) methods by restricting plausible regions and exploring parameter space efficiently. However, PRISM can additionally be used as a standalone alternative to MCMC for model analysis, providing insight into the behavior of complex scientific models. With PRISM, the time spent on evaluating a model is minimized, providing developers with an advanced model analysis for a fraction of the time required by more traditional methods. This paper provides an overview of the different techniques and algorithms that are used within PRISM. We demonstrate the advantage of using the Bayes linear approach over a full Bayesian analysis when analyzing complex models. Our results show how much information can be captured by PRISM and how one can combine it with MCMC methods to significantly speed up calibration processes (>15 times faster). PRISM is an open-source Python package that is available under the BSD 3-Clause License (BSD-3) at https://github.com/1313e/PRISM and hosted at https://prism-tool.readthedocs.io. PRISM has also been reviewed by "The Journal of Open Source Software" (https://doi.org/10.21105/joss.01229).
Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning

Existing semi-supervised learning (SSL) algorithms use a single weight to balance the loss of labeled and unlabeled examples, i.e., all unlabeled examples are equally weighted. But not all unlabeled data are equal. In this paper we study how to use a different weight for every unlabeled example. Manual tuning of all those weights -- as done in prior work -- is no longer possible. Instead, we adjust those weights via an algorithm based on the influence function, a measure of a model's dependency on one training example. To make the approach efficient, we propose a fast and effective approximation of the influence function. We demonstrate that this technique outperforms state-of-the-art methods on semi-supervised image and language classification tasks.
Sun Position Identification in Sky Images for Nowcasting Application

Very-short-term photovoltaic power forecast, namely nowcasting, is gaining increasing attention to face grid stability issues and to optimize microgrid energy management systems in the presence of large penetration of renewable energy sources. In order to identify local phenomena as sharp ramps in photovoltaic production, whole sky images can be used effectively. The first step in the implementation of new and effective nowcasting algorithms is the identification of Sun positions. In this paper, three different techniques (solar angle-based, image processing-based, and neural network-based techniques) are proposed, described, and compared. These techniques are tested on real images obtained with a camera installed at SolarTechLab at Politecnico di Milano, Milan, Italy. Finally, the three techniques are compared by introducing some performance parameters aiming to evaluate of their reliability, accuracy, and computational effort. The neural network-based technique obtains the best performance: in fact, this method is able to identify accurately the Sun position and to estimate it when the Sun is covered by clouds.
Experimental Analysis of Image Dehazing Algorithms for Pelletization Process Images

The product quality of pelletization process in steel industry is usually monitored by machine vision system. However, the image quality deteriorates significantly by haze generated during pelletization. Current image dehazing algorithms mainly concentrate on natural haze in outdoor or synthetic hazy images. Whether these algorithms can be directly adopted in solving haze removal problem in industrial process images, needs to be studied. In the present work, experiments are performed to compare the performance of five state-of-the-art image dehazing algorithms, using the image dataset PELLET that consists of real hazy images captured from pelletization process in a local steel company. For a comprehensive comparative study of the image dehazing algorithms, both qualitative and quantitative evaluation criteria are adopted, including visual perceptual evaluation, no-reference image quality assessment, and task-driven comparison. Our experimental analysis demonstrates that Boundary Constrained Context Regularization (BCCR) and Non-local (NLD) image dehazing algorithms generally achieve better quality of restored image than the other three algorithms (Dark-Channel Prior, Optimized Contrast Enhancement, and AOD deep learning network) in dealing with pelletization process images with different haze levels. The computing time needed by BCCR algorithm is only half of that by NLD (2.5 vs. 5 seconds) in processing a hazy image of size 656 × 490. Nevertheless, the performance of these algorithms needs to be improved in the future to deal with pelletization process images with dense haze, as well as to meet with the real-time requirement of pelletization process monitoring.
A review of algorithms and techniques for image-based recognition and inference in mobile robotic systems

Autonomous vehicles include driverless, self-driving and robotic cars, and other platforms capable of sensing and interacting with its environment and navigating without human help. On the other hand, semiautonomous vehicles achieve partial realization of autonomy with human intervention, for example, in driver-assisted vehicles. Autonomous vehicles first interact with their surrounding using mounted sensors. Typically, visual sensors are used to acquire images, and computer vision techniques, signal processing, machine learning, and other techniques are applied to acquire, process, and extract information. The control subsystem interprets sensory information to identify appropriate navigation path to its destination and action plan to carry out tasks. Feedbacks are also elicited from the environment to improve upon its behavior. To increase sensing accuracy, autonomous vehicles are equipped with many sensors [light detection and ranging (LiDARs), infrared, sonar, inertial measurement units, etc.], as well as communication subsystem. Autonomous vehicles face several challenges such as unknown environments, blind spots (unseen views), non-line-of-sight scenarios, poor performance of sensors due to weather conditions, sensor errors, false alarms, limited energy, limited computational resources, algorithmic complexity, human–machine communications, size, and weight constraints. To tackle these problems, several algorithmic approaches have been implemented covering design of sensors, processing, control, and navigation. The review seeks to provide up-to-date information on the requirements, algorithms, and main challenges in the use of machine vision–based techniques for navigation and control in autonomous vehicles. An application using land-based vehicle as an Internet of Thing-enabled platform for pedestrian detection and tracking is also presented.
What-if I ask you to explain: Explaining the effects of perturbations in procedural text

Our goal is to explain the effects of perturbations in procedural text, e.g., given a passage describing a rabbit's life cycle, explain why illness (the perturbation) may reduce the rabbit population (the effect).Although modern systems are able to solve the original prediction task well (e.g., illness results in less rabbits), the explanation task -identifying the causal chain of events from perturbation to effect -remains largely unaddressed, and is the goal of this research.We present QUARTET, a system that constructs such explanations from paragraphs, by modeling the explanation task as a multitask learning problem.QUARTET constructs explanations from the sentences in the procedural text, achieving ∼ 18 points better on explanation accuracy compared to several strong baselines on a recent process comprehension benchmark.On an end task on this benchmark, we show a surprising finding that good explanations do not have to come at the expense of end task performance, in fact leading to a 7% F1 improvement over SOTA.
Service Level Driven Job Scheduling in Multi-Tier Cloud Computing: A Biologically Inspired Approach

Cloud computing environments often have to deal with random-arrival computational workloads that vary in resource requirements and demand high Quality of Service (QoS) obligations.It is typical that a Service-Level-Agreement (SLA) is employed to govern the QoS obligations of the cloud computing service provider to the client.A typical challenge service-providers face every day is maintaining a balance between the limited resources available for computing and the high QoS requirements of varying random demands.Any imbalance in managing these conflicting objectives may result in either dissatisfied clients and potentially significant commercial penalties, or an over-resourced cloud computing environment that can be significantly costly to acquire and operate.Thus, scheduling the clients' workloads as they arrive at the environment to ensure their timely execution has been a central issue in cloud computing.Various approaches have been reported in the literature to address this problem: Shortest-Queue, Join-Idle-Queue, Round Robin, MinMin, MaxMin, and Least Connection, to name a few.However, optimization strategies of such approaches fail to capture QoS obligations and their associated commercial penalties.This paper presents an approach for service-level driven load scheduling and balancing in multi-tier environments.Joint scheduling and balancing operations are employed to distribute and schedule jobs among the resources, such that the total waiting time of client jobs is minimized, and thus the potential of a penalty to be incurred by the service provider is mitigated.A penalty model is used to quantify the penalty the service provider incurs as a function of the jobs' total waiting time.A Virtual-Queue abstraction is proposed to facilitate optimal job scheduling at the tier level.This problem is NP-complete, thus, a genetic algorithm is proposed as a tool for computing job schedules that minimize potential QoS penalties and hence minimize the likelihood of dissatisfied clients.
Low-supervision urgency detection and transfer in short crisis messages

Humanitarian disasters have been on the rise in recent years due to the effects of climate change and socio-political situations such as the refugee crisis. Technology can be used to best mobilize resources such as food and water in the event of a natural disaster, by semi-automatically flagging tweets and short messages as indicating an urgent need. The problem is challenging not just because of the sparseness of data in the immediate aftermath of a disaster, but because of the varying characteristics of disasters in developing countries (making it difficult to train just one system) and the noise and quirks in social media. In this paper, we present a robust, low-supervision social media urgency system that adapts to arbitrary crises by leveraging both labeled and unlabeled data in an ensemble setting. The system is also able to adapt to new crises where an unlabeled background corpus may not be available yet by utilizing a simple and effective transfer learning methodology. Experimentally, our transfer learning and low-supervision approaches are found to outperform viable baselines with high significance on myriad disaster datasets.
Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching

Deep text matching approaches have been widely studied for many applications including question answering and information retrieval systems. To deal with a domain that has insufficient labeled data, these approaches can be used in a Transfer Learning (TL) setting to leverage labeled data from a resource-rich source domain. To achieve better performance, source domain data selection is essential in this process to prevent the negative problem. However, the emerging deep transfer models do not fit well with most existing data selection methods, because the data selection policy and the transfer learning model are not jointly trained, leading to sub-optimal training efficiency. In this paper, we propose a novel reinforced data selector to select high-quality source domain data to help the TL model. Specifically, the data selector acts on the source domain data to find a subset for optimization of the TL model, and the performance of the TL model can provide rewards in turn to update the selector. We build the reinforced data selector based on the actor-critic framework and integrate it to a DNN based transfer learning model, resulting in a Reinforced Transfer Learning (RTL) method. We perform a thorough experimental evaluation on two major tasks for text matching, namely, paraphrase identification and natural language inference. Experimental results show the proposed RTL can significantly improve the performance of the TL model. We further investigate different settings of states, rewards, and policy optimization methods to examine the robustness of our method. Last, we conduct a case study on the selected data and find our method is able to select source domain data whose Wasserstein distance is close to the target domain data. This is reasonable and intuitive as such source domain data can provide more transferability power to the model.
A decision tree for building IT applications

Blockchain technology has gained increasing attention from research and industry over the recent years. It allows implementing in its environment the smart-contracts technology which is used to automate and execute agreements between users. The blockchain is proposed today as a new technical infrastructure for several types of IT applications. This interest is mainly due to its core property that allows two users to perform transactions without going through a Trusted Third Party, while offering a transparent and fully protected data storage. However, a blockchain comes along a number of other intrinsic properties, which may not be suitable or beneficial in all the envisaged application cases. Consequently, we propose in this paper to design a new tool which is “a decision tree” that allows identifying when a blockchain may be the appropriate technical infrastructure for a given IT application, and when another classical system (centralized or distributed peer-to-peer) is more adapted. The proposed decision tree allows also identifying whether or not it is necessary to use the smart-contracts technology.
A Linear Threshold for Uniqueness of Solutions to Random Jigsaw Puzzles

We consider a problem introduced by Mossel and Ross (‘Shotgun assembly of labeled graphs’, arXiv:1504.07682 ). Suppose a random n × n jigsaw puzzle is constructed by independently and uniformly choosing the shape of each ‘jig’ from q possibilities. We are given the shuffled pieces. Then, depending on q , what is the probability that we can reassemble the puzzle uniquely? We say that two solutions of a puzzle are similar if they only differ by a global rotation of the puzzle, permutation of duplicate pieces, and rotation of rotationally symmetric pieces. In this paper, we show that, with high probability, such a puzzle has at least two non-similar solutions when 2 ⩽ q ⩽ 2 e −1/2 n , all solutions are similar when q ⩾ (2+ϵ) n , and the solution is unique when q = ω( n ).
Augmenting Parallel Coordinates Plots With Color-Coded Stacked Histograms

We introduce Parallel Histogram Plot (PHP), a technique that overcomes the innate limitations of parallel coordinates plot (PCP) by attaching stacked-bar histograms with discrete color schemes to PCP. The color-coded histograms enable users to see an overview of the whole data without cluttering or scalability issues. Each rectangle in the PHP histograms is color coded according to the data ranking by a selected attribute. This color-coding scheme allows users to visually examine relationships between attributes, even between those that are displayed far apart, without repositioning or reordering axes. We adopt the Visual Information Seeking Mantra so that the polylines of the original PCP can be used to show details of a small number of selected items when the cluttering problem subsides. We also design interactions, such as a focus+context technique, to help users investigate small regions of interest in a space-efficient manner. We provide a real-world example in which PHP is effectively utilized compared with other visualizations, and we perform a controlled user study to evaluate the performance of PHP in helping users estimate the correlation between attributes. The results demonstrate that the performance of PHP was consistent in the estimation of correlations between two attributes regardless of the distance between them.
SklCoin: Toward a Scalable Proof-of-Stake and Collective Signature Based Consensus Protocol for Strong Consistency in Blockchain

The proof-of-work consensus protocol suffers from two main limitations: waste of energy and offering only probabilistic guarantees about the status of the blockchain.This paper introduces SklCoin, a new Byzantine consensus protocol and its corresponding software architecture.This protocol leverages two ideas: 1) the proof-of-stake concept to dynamically form stakeproportionate consensus groups that represent block miners (stakeholders), and 2) scalable collective signing to efficiently commit transactions irreversibly.SklCoin has immediate finality characteristic where all miners instantly agree on the validity of blocks.In addition, SklCoin supports high transaction rate because of its fast miner election mechanism.
Acrostic Poem Generation

We propose a new task in the area of computational creativity: acrostic poem generation in English.Acrostic poems are poems that contain a hidden message; typically, the first letter of each line spells out a word or short phrase.We define the task as a generation task with multiple constraints: given an input word, 1) the initial letters of each line should spell out the provided word, 2) the poem's semantics should also relate to it, and 3) the poem should conform to a rhyming scheme.We further provide a baseline model for the task, which consists of a conditional neural language model in combination with a neural rhyming model.Since no dedicated datasets for acrostic poem generation exist, we create training data for our task by first training a separate topic prediction model on a small set of topic-annotated poems and then predicting topics for additional poems.Our experiments show that the acrostic poems generated by our baseline are received well by humans and do not lose much quality due to the additional constraints.Last, we confirm that poems generated by our model are indeed closely related to the provided prompts, and that pretraining on Wikipedia can boost performance.
Necessary conditions for classifying $$\mathbf {m}$$-separability of multipartite entanglements

We study the norms of the Bloch vectors for arbitrary n-partite quantum states. A tight upper bound of the norms is derived for n-partite systems with different individual dimensions. These upper bounds are used to deal with the separability problems. Necessary conditions are presented for $$\mathbf {m}$$-separable states in n-partite quantum systems. Based on the upper bounds, classification of multipartite entanglement is illustrated with detailed examples.
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning

We consider a collaborative learning scenario in which multiple data-owners wish to jointly train a logistic regression model, while keeping their individual datasets private from the other parties. We propose COPML, a fully-decentralized training framework that achieves scalability and privacy-protection simultaneously. The key idea of COPML is to securely encode the individual datasets to distribute the computation load effectively across many parties and to perform the training computations as well as the model updates in a distributed manner on the securely encoded data. We provide the privacy analysis of COPML and prove its convergence. Furthermore, we experimentally demonstrate that COPML can achieve significant speedup in training over the benchmark protocols. Our protocol provides strong statistical privacy guarantees against colluding parties (adversaries) with unbounded computational power, while achieving up to $16\times$ speedup in the training time against the benchmark protocols.