Reviewerly/exHarmony huggingface dataset:
Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior

There has been significant interest of late in generating behavior of agents that is interpretable to the human (observer) in the loop. However, the work in this area has typically lacked coherence on the topic, with proposed solutions for “explicable”, “legible”, “predictable” and “transparent” planning with overlapping, and sometimes conflicting, semantics all aimed at some notion of understanding what intentions the observer will ascribe to an agent by observing its behavior. This is also true for the recent works on “security” and “privacy” of plans which are also trying to answer the same question, but from the opposite point of view – i.e. when the agent is trying to hide instead of reveal its intentions. This paper attempts to provide a workable taxonomy of relevant concepts in this exciting and emerging field of inquiry.
Raccoon Optimization Algorithm

Population-based meta-heuristic is a high-level method intended to provide sufficient solution for problems with incomplete information among a massive volume of solutions. However, it does not guarantee to attain global optimum in a reasonable time. To improve the time and accuracy of the coverage in the population-based meta-heuristic, this paper presents a novel algorithm called the raccoon optimization algorithm (ROA). The ROA is inspired by the rummaging behaviors of real raccoons for food. Raccoons are successful animals because of their extraordinarily sensitive and dexterous paws and their ability to find solutions for foods and remember them for up to three years. These capabilities make raccoons expert problem solvers and allow them to purposefully seek optimum solutions. These behaviors exploited in the ROA to search the solution spaces of nonlinear continuous problems to find the global optimum with higher accuracy and lower time coverage. To evaluate the ROA’s ability in addressing complicated problems, it has been tested on several benchmark functions. The ROA is then compared with nine other well-known optimization algorithms. These experiments show that the ROA achieves higher accuracy with lower coverage time.
Matrix Completion for Graph-Based Deep Semi-Supervised Learning

Convolutional Neural Networks (CNNs) have provided promising achievements for image classification problems. However, training a CNN model relies on a large number of labeled data. Considering the vast amount of unlabeled data available on the web, it is important to make use of these data in conjunction with a small set of labeled data to train a deep learning model. In this paper, we introduce a new iterative Graph-based Semi-Supervised Learning (GSSL) method to train a CNN-based classifier using a large amount of unlabeled data and a small amount of labeled data. In this method, we first construct a similarity graph in which the nodes represent the CNN features corresponding to data points (labeled and unlabeled) while the edges tend to connect the data points with the same class label. In this graph, the missing label of unsupervised nodes is predicted by using a matrix completion method based on rank minimization criterion. In the next step, we use the constructed graph to calculate triplet regularization loss which is added to the supervised loss obtained by initially labeled data to update the CNN network parameters.
Quantum Algorithms for Feedforward Neural Networks

Quantum machine learning has the potential for broad industrial applications, and the development of quantum algorithms for improving the performance of neural networks is of particular interest given the central role they play in machine learning today. In this paper we present quantum algorithms for training and evaluating feedforward neural networks based on the canonical classical feedforward and backpropagation algorithms. Our algorithms rely on an efficient quantum subroutine for approximating the inner products between vectors in a robust way, and on implicitly storing large intermediate values in quantum random access memory for fast retrieval at later stages. The running times of our algorithms can be quadratically faster in the size of the network than their standard classical counterparts since they depend linearly on the number of neurons in the network, as opposed to the number of connections between neurons as in the classical case. This makes our algorithms suited for large-scale, highly-connected networks where the number of edges in the network dominates the classical algorithmic running time. Furthermore, networks trained by our quantum algorithm may have an intrinsic resilience to overfitting, as the algorithm naturally mimics the effects of classical techniques such as drop-out used to regularize networks. Our algorithms can also be used as the basis for new quantum-inspired classical algorithms which have the same dependence on the network dimensions as their quantum counterparts, but with quadratic overhead in other parameters that makes them relatively impractical.
DTMT: A Novel Deep Transition Architecture for Neural Machine Translation

Past years have witnessed rapid developments in Neural Machine Translation (NMT). Most recently, with advanced modeling and training techniques, the RNN-based NMT (RNMT) has shown its potential strength, even compared with the well-known Transformer (self-attentional) model. Although the RNMT model can possess very deep architectures through stacking layers, the transition depth between consecutive hidden states along the sequential axis is still shallow. In this paper, we further enhance the RNN-based NMT through increasing the transition depth between consecutive hidden states and build a novel Deep Transition RNN-based Architecture for Neural Machine Translation, named DTMT. This model enhances the hidden-to-hidden transition with multiple non-linear transformations, as well as maintains a linear transformation path throughout this deep transition by the well-designed linear transformation mechanism to alleviate the gradient vanishing problem. Experiments show that with the specially designed deep transition modules, our DTMT can achieve remarkable improvements on translation quality. Experimental results on Chinese⇒English translation task show that DTMT can outperform the Transformer model by +2.09 BLEU points and achieve the best results ever reported in the same dataset. On WMT14 English⇒German and English⇒French translation tasks, DTMT shows superior quality to the state-of-the-art NMT systems, including the Transformer and the RNMT+.
Statistical analysis of randomized benchmarking

Randomized benchmarking and variants thereof, which we collectively call RB+, are widely used to characterize the performance of quantum computers because they are simple, scalable, and robust to state-preparation and measurement errors. However, experimental implementations of RB+ allocate resources suboptimally and make ad-hoc assumptions that undermine the reliability of the data analysis. In this paper, we propose a simple modification of RB+ which rigorously eliminates a nuisance parameter and simplifies the experimental design. We then show that, with this modification and specific experimental choices, RB+ efficiently provides estimates of error rates with multiplicative precision. Finally, we provide a simplified rigorous method for obtaining credible regions for parameters of interest and a heuristic approximation for these intervals that performs well in currently relevant regimes.
A Mobile Crowd Sensing Application for Hypertensive Patients

Mobile crowd sensing (MCS) is an application that collects data from a network of conscientious volunteers and implements it for the common or personal benefit. This contribution proposes an implementation that collects the data from hypertensive patients, thus creating an experimental database using the cloud service Platform as a Service (PaaS). The challenge is to perform the analysis without the main diagnostic feature for hypertension-the blood pressure. The other problems consider the data reliability in an environment full of artifacts and with limited bandwidth and battery resources. In order to motivate the MCS volunteers, a feedback about the patient's current status is created, provided by the means of machine-learning (ML) techniques. Two techniques are investigated and the Random Forest algorithm yielded the best results. The proposed platform, with slight modifications, can be adapted to the patients with other cardiovascular problems.
Optimizing Content with A/B Headline Testing: Changing Newsroom Practices

Audience analytics are an increasingly essential part of the modern newsroom as publishers seek to maximize the reach and commercial potential of their content. On top of a wealth of audience data collected, algorithmic approaches can then be applied with an eye towards predicting and optimizing the performance of content based on historical patterns. This work focuses specifically on content optimization practices surrounding the use of A/B headline testing in newsrooms. Using such approaches, digital newsrooms might audience-test as many as a dozen headlines per article, collecting data that allows an optimization algorithm to converge on the headline that is best with respect to some metric, such as the click-through rate. This article presents the results of an interview study which illuminate the ways in which A/B testing algorithms are changing workflow and headline writing practices, as well as the social dynamics shaping this process and its implementation within US newsrooms.
FPGA Modeling and Optimization of a SIMON Lightweight Block Cipher

Security of sensitive data exchanged between devices is essential. Low-resource devices (LRDs), designed for constrained environments, are increasingly becoming ubiquitous. Lightweight block ciphers provide confidentiality for LRDs by balancing the required security with minimal resource overhead. SIMON is a lightweight block cipher targeted for hardware implementations. The objective of this research is to implement, optimize, and model SIMON cipher design for LRDs, with an emphasis on energy and power, which are critical metrics for LRDs. Various implementations use field-programmable gate array (FPGA) technology. Two types of design implementations are examined: scalar and pipelined. Results show that scalar implementations require 39% less resources and 45% less power consumption. The pipelined implementations demonstrate 12 times the throughput and consume 31% less energy. Moreover, the most energy-efficient and optimum design is a two-round pipelined implementation, which consumes 31% of the best scalar's implementation energy. The scalar design that consumes the least energy is a four-round implementation. The scalar design that uses the least area and power is the one-round implementation. Balancing energy and area, the two-round pipelined implementation is optimal for a continuous stream of data. One-round and two-round scalar implementations are recommended for intermittent data applications.
Seeking, Reading, and Use of Scholarly Articles: An International Study of Perceptions and Behavior of Researchers

While journal articles are still considered the most important sources of scholarly reading, libraries may no longer have a monopoly on providing discovery and access. Many other sources of scholarly information are available to readers. This international study examines how researchers discover, read, and use scholarly literature for their work. Respondents in 2018 report an average of almost 20 article readings a month and there are still significant differences found in the reading and use of scholarly literature by discipline and geographical location, consistent with the earlier studies. Researchers show they are willing to change or adopt new strategies to discover and obtain articles.
Comparison of automatic methods for reducing the Pareto front to a single solution applied to multi-document text summarization

Nowadays, automatic text summarization methods are needed in many different contexts. Extractive multi-document summarization approaches are intended to, simultaneously, synthesize the main content of a document collection and reduce the redundant information. Multi-objective optimization seems to be the natural way to address this problem. The result of applying a multi-objective optimization approach is a set containing many non-dominated solutions or Pareto set. However, only one relevant summary is needed, so a post-Pareto analysis must be performed to reduce this set to a single solution. Several methods have been considered to address this task, including those related to the largest hypervolume, the consensus solution, the shortest distance to the ideal point, and the shortest distance to all points. The approaches have been tested and compared through different experiments, which have been performed using datasets from Document Understanding Conferences (DUC). The methods have been compared with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results show that consensus is the method providing the best average values. The results provided by the consensus method improve the ones obtained with the other methods between 10.68% and 27.32% in ROUGE scores.
Word-level neutrosophic sentiment similarity

In the specializedliterature, there are many approaches developed for capturing textual measures: textual similarity, textual readability and textual sentiment. This paper proposes a new sentiment similarity measures between pairs of words using a fuzzy-based approach in which words are considered single-valued neutrosophic sets. We build our study with the aid of the lexical resource SentiWordNet 3.0 as our intended scope is to design a new word-level similarity measure calculated by means of the sentiment scores of the involved words. Our study pays attention to the polysemous words because these words are a real challenge for any application that processes natural language data. After our knowledge, this approach is quite new in the literature and the obtained results give us hope for further investigations.
Protecting Page Tables from RowHammer Attacks using Monotonic Pointers in DRAM True-Cells

We identify an important asymmetry in physical DRAM cells that can be utilized to prevent RowHammer attacks by adding 18 lines of code to modify the OS memory allocator. Our small modification has a powerful impact on RowHammer's ability to bypass memory protection mechanisms and achieve a successful attack. Specifically, we identify two types of DRAM cells: true-cells and anti-cells. In a true-cell, a leaking capacitor will induce a '1'->'0' error, while in anti-cells, errors flow from '0'->'1'. We then create DRAM cell-type-aware memory allocation which enables a "monotonicity property" for a given data object. The monotonicity property is able to counter RowHammer attacks (and, to a broader extent, other memory attacks) by allocating only one type of cells for an object, thereby restricting error direction. We apply the monotonicity property to pointers in page tables by placing all page tables in true-cells that are above a "low water mark". We show that this approach successfully defends against page-table-based privilege escalation RowHammer attacks. Using established RowHammer-induced bit-flip error statistics, we provide proofs of the soundness and completeness of our technique and show that with our technique only one out of 2.04x10 5 systems is vulnerable to the attack, and the expected attack time on the vulnerable system is 231 days. We also provide application performance results from prototypes implemented through modifications to Linux kernels. Our cross-layer approach avoids undesirable energy cost, hardware changes, performance overhead, and high software complexity associated with prior countermeasures.
The Tularosa Study: An Experimental Design and Implementation to Quantify the Effectiveness of Cyber Deception

The Tularosa study was designed to understand how defensive deception-including both cyber and psychological-affects cyber attackers.Over 130 red teamers participated in a network penetration task over two days in which we controlled both the presence of and explicit mention of deceptive defensive techniques.To our knowledge, this represents the largest study of its kind ever conducted on a professional red team population.The design was conducted with a battery of questionnaires (e.g., experience, personality, etc.) and cognitive tasks (e.g., fluid intelligence, working memory, etc.), allowing for the characterization of a "typical" red teamer, as well as physiological measures (e.g., galvanic skin response, heart rate, etc.) to be correlated with the cyber events.This paper focuses on the design, implementation, data, population characteristics, and begins to examine preliminary results.
Cyclic and Bidirectional Quantum Teleportation via Pseudo Multi-Qubit States

A novel scheme is proposed in this paper, which realizes the cyclic and bidirectional teleportation via pseudo multi-qubit entangled states.To realize the teleportation completely, it utilizes controlled-NOT operation to obtain a quantum channel whose number of qubits reaches twelve.In this scheme, Alice can teleport her qubits to Bob and Charlie, Bob can teleport his qubits to Alice and Charlie while Charlie teleports his qubits to Alice and Bob, respectively, they reconstruct the original state and make the scheme perfectly by performing Bell-state measurement and appropriate unitary operation.
Heralded Bell State of Dissipative Qubits Using Classical Light in a Waveguide

Maximally entangled two-qubit states (Bell states) are of central importance in quantum technologies. We show that heralded generation of a maximally entangled state of two intrinsically open qubits can be realized in a one-dimensional (1D) system through strong coherent driving and continuous monitoring. In contrast to the natural idea that dissipation leads to decoherence and so destroys quantum effects, continuous measurement and strong interference in our 1D system generate a pure state with perfect quantum correlation between the two open qubits. Though the steady state is a trivial product state that has zero coherence or concurrence, we show that, with carefully tuned parameters, a Bell state can be generated in the system's quantum jump trajectories, heralded by a reflected photon. Surprisingly, this maximally entangled state survives the strong coherent state input-a classical state that overwhelms the system. This simple method to generate maximally entangled states using classical coherent light and photon detection may, since our qubits are in a 1D continuum, find application as a building block of quantum networks.
Clustering With Orthogonal AutoEncoder

Recently, clustering algorithms based on deep AutoEncoder attract lots of attention due to their excellent clustering performance.On the other hand, the success of PCA-Kmeans and spectral clustering corroborates that the orthogonality of embedding is beneficial to increase the clustering accuracy.In this paper, we propose a novel dimensional reduction model, called Orthogonal AutoEncoder (OAE), which encourages the orthogonality of the learned embedding.Furthermore, we propose a joint deep Clustering framework based on Orthogonal AutoEncoder (COAE), and this new framework is capable of extracting the latent embedding and predicting the clustering assignment simultaneously.The COAE stacks a fully connected clustering layer on top of the OAE, where the activation function of the clustering layer is the multinomial logistic regression function.The loss function of the COAE contains two terms: the reconstruction loss and the clustering-oriented loss.The first one is a data-dependent term in order to prevent overfitting.The other one is the cross entropy between the predicted assignment and the auxiliary target distribution.The network parameters of the COAE can be effectively updated by the mini-batch stochastic gradient descent algorithm and the back-propagation approach.The experiments on benchmark datasets empirically demonstrate that the COAE can achieve superior or competitive clustering performance as state-of-the-art deep clustering frameworks.The implementation of our algorithm is available at https://github.com/WangDavey/COAE
Large-scale offline signature recognition via deep neural networks and feature embedding

Although there have been several developments in offline signature recognition, there is still no much focus on the recognition problem using a small sample size for the training. In many studies, 10 or more signatures are used for training phase, which is mostly intractable in practice. In this study we propose a new convolutional neural network (CNN) structure named Large-Scale Signature Network (LS2Net) with batch normalization to deal with the large-scale training problem. Moreover, we present, Class Center based Classifier (C3) algorithm, which relies on 1-Nearest Neighbor (1-NN) classification task by using the class-centers of the feature embeddings obtained from fully-connected layers. In addition to these, by replacing the activation function rectifier linear unit (ReLU) with leaky ReLU, we create a new network structure called LS2Net_v2. 96k signatures obtained from 4k signers of GPDS-4000 dataset are used. For a realistic comparison, MCYT and CEDAR are chosen besides the GPDS dataset. The proposed networks are compared with Visual Geometry Group (VGG)-[S, M, 16], which are the frequently used networks in the literature. The networks are tested with two splitting ratios as 25% train - 75% test and 50% train - 50% test per signers. For each ratio, five train and test subsets are randomly generated. Performance metrics are obtained by averaging the results of these five subsets. LS2Net achieved 96.41% and 98.30% accuracy performance for the 25%–75% ratio in MCYT and CEDAR. Moreover, LS2Net_v2 achieves best results by getting 96.91% accuracy score for 25%–75% ratio for GPDS-4000. Batch normalization and C3 algorithm contribute to the performance significantly.
Mind Your Own Business: A Longitudinal Study of Threats and Vulnerabilities in Enterprises

Enterprises own a significant fraction of the hosts connected to the Internet and possess valuable assets, such as financial data and intellectual property, which may be targeted by attackers.They suffer attacks that exploit unpatched hosts and install malware, resulting in breaches that may cost millions in damages.Despite the scale of this phenomenon, the threat and vulnerability landscape of enterprises remains under-studied.The security posture of enterprises remains unclear, and it's unknown whether enterprises are indeed more secure than consumer hosts.To address these questions, we perform the largest and longest enterprise security study so far.Our data covers nearly 3 years and is collected from 28K enterprises, belonging to 67 industries, which own 82M client hosts and 73M public-facing servers.Our measurements comprise of two parts: an analysis of the threat landscape and an analysis of the enterprise vulnerability patching behavior.The threat landscape analysis studies the encounter rate of malware and PUP in enterprise client hosts.It measures, among others, that 91%-97% of the enterprises, 13%-41% of the hosts, encountered at least one malware or PUP file over the length of our study; that enterprises encounter malware much more often than PUP; and that some industries like banks and consumer finances achieve significantly lower malware and PUP encounter rates than the most-affected industries.The vulnerability analysis examines the patching of 12 client-side and 112 server-side applications in enterprise client hosts and servers.It measures, among others, that it takes over 6 months on average to patch 90% of the population across all vulnerabilities in the 12 client-side applications; that enterprise hosts are faster to patch vulnerabilities compared to consumer hosts; and that the patching of server applications is much worse than the patching of clientside applications.