Reviewerly/exHarmony huggingface dataset:
Red Teaming Language Models with Language Models

Language Models (LMs) often cannot be deployed because of their potential to harm users in ways that are hard to predict in advance.Prior work identifies harmful behaviors before deployment by using human annotators to handwrite test cases.However, human annotation is expensive, limiting the number and diversity of test cases.In this work, we automatically find cases where a target LM behaves in a harmful way, by generating test cases ("red teaming") using another LM.We evaluate the target LM's replies to generated test questions using a classifier trained to detect offensive content, uncovering tens of thousands of offensive replies in a 280B parameter LM chatbot.We explore several methods, from zero-shot generation to reinforcement learning, for generating test cases with varying levels of diversity and difficulty.Furthermore, we use prompt engineering to control LM-generated test cases to uncover a variety of other harms, automatically finding groups of people that the chatbot discusses in offensive ways, personal and hospital phone numbers generated as the chatbot's own contact info, leakage of private training data in generated text, and harms that occur over the course of a conversation.Overall, LM-based red teaming is a promising tool for finding and fixing diverse, undesirable LM behaviors before impacting users.classifiers.
Personalized Landmark Recommendation Based on Geotags from Photo Sharing Sites

Geotagged photos of users on social media sites provide abundant location-based data, which can be exploited for various location-based services, such as travel recommendation. In this paper, we propose a novel approach to a new application, i.e., personalized landmark recommendation based on users’ geotagged photos. We formulate the landmark recommendation task as a collaborative filtering problem, for which we propose a category-regularized matrix factorization approach that integrates both user-landmark preference and category-based landmark similarity. We collected geotagged photos from Flickr and landmark categories from Wikipedia for our experiments. Our experimental results demonstrate that the proposed approach outperforms popularity-based landmark recommendation and a basic matrix factorization approach in recommending personalized landmarks that are less visited by the population as a whole.
Separators and adjustment sets in causal graphs: Complete criteria and an algorithmic framework

Principled reasoning about the identifiability of causal effects from non-experimental data is an important application of graphical causal models. This paper focuses on effects that are identifiable by covariate adjustment, a commonly used estimation approach. We present an algorithmic framework for efficiently testing, constructing, and enumerating m-separators in ancestral graphs (AGs), a class of graphical causal models that can represent uncertainty about the presence of latent confounders. Furthermore, we prove a reduction from causal effect identification by covariate adjustment to m-separation in a subgraph for directed acyclic graphs (DAGs) and maximal ancestral graphs (MAGs). Jointly, these results yield constructive criteria that characterize all adjustment sets as well as all minimal and minimum adjustment sets for identification of a desired causal effect with multiple exposures and outcomes in the presence of latent confounding. Our results extend several existing solutions for special cases of these problems. Our efficient algorithms allowed us to empirically quantify the identifiability gap between covariate adjustment and the do-calculus in random DAGs and MAGs, covering a wide range of scenarios. Implementations of our algorithms are provided in the R package dagitty.
Efficient Learning-Free Keyword Spotting

In this article, a method for segmentation-based learning-free Query by Example (QbE) keyword spotting on handwritten documents is proposed. The method consists of three steps, namely preprocessing, feature extraction and matching, which address critical variations of text images (e.g., skew, translation, different writing styles). During the feature extraction step, a sequence of descriptors is generated using a combination of a zoning scheme and a novel appearance descriptor, referred as modified Projections of Oriented Gradients. The preprocessing step, which includes contrast normalization and main-zone detection, aims to overcome the shortcomings of the appearance descriptor. Moreover, an uneven zoning scheme is introduced by applying a denser zoning only on query images for a more detailed representation. This leads to a significant reduction in storage requirements of a document collection. The distance between the query and word sequences is efficiently computed by the proposed Selective Matching algorithm. This algorithm is further extended to handle an augmented set of images originating from a single query image. The efficiency of the proposed method is demonstrated by experimentation conducted on seven publicly available datasets. In these experiments, the proposed method significantly outperforms all state-of-the-art learning-free techniques.
Revisiting Projective Structure from Motion: A Robust and Efficient Incremental Solution

This paper presents a solution to the Projective Structure from Motion (PSfM) problem able to deal efficiently with missing data, outliers and, for the first time, large scale 3D reconstruction scenarios. By embedding the projective depths into the projective parameters of the points and views, we decrease the number of unknowns to estimate and improve computational speed by optimizing standard linear Least Squares systems instead of homogeneous ones. In order to do so, we show that an extension of the linear constraints from the Generalized Projective Reconstruction Theorem can be transferred to the projective parameters, ensuring also a valid projective reconstruction in the process. We use an incremental approach that, starting from a solvable sub-problem, incrementally adds views and points until completion with a robust, outliers free, procedure. To prevent error accumulation, a refinement based on alternation between new estimations of views and points is used. This can also be done with constrained non-linear optimization. Experiments with simulated data shows that our approach is performing well, both in term of the quality of the reconstruction and the capacity to handle missing data and outliers with a reduced computational time. Finally, results on real datasets shows the ability of the method to be used in medium and large scale 3D reconstruction scenarios with high ratios of missing data (up to 98 percent).
Robust and Unobtrusive Display-to-Camera Communications via Blue Channel Embedding

Due to the rapid advancement in processing power and camera quality of mobile devices, research on the display-to-camera (D2C) communication channel has recently received increasing attention. Unlike the traditional QR Code, the unobtrusive D2C communication scheme normally serves both the human eyes and the mobile camera in commercial advertisements. Thus, attention should be paid to both unobtrusiveness and reliability of the design of a D2C communication scheme. In this paper, 2D barcodes with unobtrusive embedding in the blue channel are proposed in image and video formats to yield the robust and unobtrusive (RU) code and video-based RU (vRU) code, respectively. The proposed RU code is featured with a modulation scheme in blue channel that leverages several important properties of the human visual system; such as insensitivity toward the yellow-blue chrominance component, the proximity principle, and the oblique effect. Both RU code and vRU code employ a low-density-parity-check code for intra-frame channel coding. In addition, vRU code adopts an erasure-correcting Reed–Solomon code for inter-frame channel coding. Under a high perceptual quality constraint (multiscale structural similarity (MS-SSIM) ≈ 0.95), RU code achieves a demodulation bit error probability of 3.84%, which is an order of magnitude smaller than that of the existing picture-embedding 2D barcodes. Meanwhile, under a similar perceptual quality requirement (MS-SSIM ≈ 0.95 for each video frame), the goodput of vRU code is reported to be as high as 34.33 kbps under practical settings, e.g., a display frame rate of 30 fps and a capture frame rate of 42 fps.
Adaptive monitoring: A systematic mapping

Context: Adaptive monitoring is a method used in a variety of domains for responding to changing conditions.It has been applied in different ways, from monitoring systems' customization to re-composition, in different application domains.However, to the best of our knowledge, there are no studies analyzing how adaptive monitoring differs or resembles among the existing approaches.Objective: To characterize the current state of the art on adaptive monitoring, specifically to: a) identify the main concepts in the adaptive monitoring topic; b) determine the demographic characteristics of the studies published in this topic; c) identify how adaptive monitoring is conducted and evaluated by the different approaches; d) identify patterns in the approaches supporting adaptive monitoring. Method:We have conducted a systematic mapping study of adaptive monitoring approaches following recommended practices.We have applied automatic search and snowballing sampling on different sources and used rigorous selection criteria to retrieve the final set of papers.Moreover, we have used an existing qualitative analysis method for extracting relevant data from studies.Finally, we have applied data mining techniques for identifying patterns in the solutions. Results:We have evaluated 110 studies organized in 81 approaches that support adaptive monitoring.By analyzing them, we have: (1) surveyed related terms and definitions of adaptive monitoring and proposed a generic one; (2) visualized studies' demographic data and arranged the studies into approaches; (3) characterized the main approaches' contributions; (4) determined how approaches conduct the adaptation process and evaluate their solutions. Conclusions:This cross-domain overview of the current state of the art on adaptive monitoring may be a solid and comprehensive baseline for researchers and practitioners in the field.Especially, it may help in identifying opportunities of research; for instance, the need of proposing generic and flexible software engineering solutions for supporting adaptive monitoring in a variety of systems.
Quantum Zeno Dynamics from General Quantum Operations

We consider the evolution of an arbitrary quantum dynamical semigroup of a finite-dimensional quantum system under frequent kicks, where each kick is a generic quantum operation. We develop a generalization of the Baker-Campbell-Hausdorff formula allowing to reformulate such pulsed dynamics as a continuous one. This reveals an adiabatic evolution. We obtain a general type of quantum Zeno dynamics, which unifies all known manifestations in the literature as well as describing new types.
Evaluation of Causes of Protected Health Information Breaches

This study evaluates the internal and external sources of and preventive steps for data breaches in health care organizations from 2009 to 2017.
Accrediting outputs of noisy intermediate-scale quantum computing devices

We present an accreditation protocol for the outputs of noisy intermediate-scale quantum devices. By testing entire circuits rather than individual gates, our accreditation protocol can provide an upper-bound on the variation distance between noisy and noiseless probability distribution of the outputs of the target circuit of interest. Our accreditation protocol requires implementing quantum circuits no larger than the target circuit, therefore it is practical in the near term and scalable in the long term. Inspired by trap-based protocols for the verification of quantum computations, our accreditation protocol assumes that single-qubit gates have bounded probability of error. We allow for arbitrary spatial and temporal correlations in the noise affecting state preparation, measurements, single-qubit and two-qubit gates. We describe how to implement our protocol on real-world devices, and we also present a novel cryptographic protocol (which we call 'mesothetic' protocol) inspired by our accreditation protocol.
Representing and Learning Grammars in Answer Set Programming

In this paper we introduce an extension of context-free grammars called answer set grammars (ASGs). These grammars allow annotations on production rules, written in the language of Answer Set Programming (ASP), which can express context-sensitive constraints. We investigate the complexity of various classes of ASG with respect to two decision problems: deciding whether a given string belongs to the language of an ASG and deciding whether the language of an ASG is non-empty. Specifically, we show that the complexity of these decision problems can be lowered by restricting the subset of the ASP language used in the annotations. To aid the applicability of these grammars to computational problems that require context-sensitive parsers for partially known languages, we propose a learning task for inducing the annotations of an ASG. We characterise the complexity of this task and present an algorithm for solving it. An evaluation of a (prototype) implementation is also discussed.
Ladder to success – eliciting project managers’ perceptions of IS project success criteria

The traditional approach to assess information system (IS) project success is adherence to planning (ATP) – meeting budget, schedule, and requirements targets. Today, scholars agree that ATP is insufficient to adequately assess IS project success, but an agreed-on set of success criteria is still missing. Many works on this topic are based on theoretical considerations rather than empirical inquiries. We analyze practitioners’ subjective perspectives by investigating what criteria IS project managers consider relevant for IS project success assessment. We interview eleven experienced project managers in Germany, applying Repertory Grid and Laddering to minimize potential biases. Our results yield eight success criteria, indicating that criteria like process efficiency and stakeholder satisfaction must be considered in addition to ATP. Scholars can use our findings to apply the identified success criteria in future studies. Practitioners gain insights into the expert perspective on project success and might rethink the way of assessing success in their projects.
Standard Quantum Limit and Heisenberg Limit in Function Estimation

Unlike well-established parameter estimation, function estimation faces conceptual and mathematical difficulties despite its enormous potential utility. We establish the fundamental error bounds on function estimation in quantum metrology for a spatially varying phase operator, where various degrees of smooth functions are considered. The error bounds are identified in the cases of the absence and the presence of interparticle entanglement, which correspond to the standard quantum limit and the Heisenberg limit, respectively. Notably, these error bounds can be reached by either position-localized states or wave-number-localized ones. In fact, we show that these error bounds are theoretically optimal for any type of probe states, indicating that quantum metrology on functions is also subject to the Nyquist-Shannon sampling theorem, even if classical detection is replaced by quantum measurement.
A Survey of Super-Resolution in Iris Biometrics With Evaluation of Dictionary-Learning

The lack of resolution has a negative impact on the performance of image-based biometrics. While many generic super-resolution methods have been proposed to restore low-resolution images, they usually aim to enhance their visual appearance. However, an overall visual enhancement of biometric images does not necessarily correlate with a better recognition performance. Reconstruction approaches thus need to incorporate the specific information from the target biometric modality to effectively improve recognition performance. This paper presents a comprehensive survey of iris super-resolution approaches proposed in the literature. We have also adapted an eigen-patches’ reconstruction method based on the principal component analysis eigen-transformation of local image patches. The structure of the iris is exploited by building a patch-position-dependent dictionary. In addition, image patches are restored separately, having their own reconstruction weights. This allows the solution to be locally optimized, helping to preserve local information. To evaluate the algorithm, we degraded the high-resolution images from the CASIA Interval V3 database. Different restorations were considered, with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15\times 15$ </tex-math></inline-formula> pixels being the smallest resolution evaluated. To the best of our knowledge, this is the smallest resolutions employed in the literature. The experimental framework is complemented with six publicly available iris comparators that were used to carry out biometric verification and identification experiments. The experimental results show that the proposed method significantly outperforms both the bilinear and bicubic interpolations at a very low resolution. The performance of a number of comparators attains an impressive equal error rate as low as 5% and a Top-1 accuracy of 77%–84% when considering the iris images of only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15 \times 15$ </tex-math></inline-formula> pixels. These results clearly demonstrate the benefit of using trained super-resolution techniques to improve the quality of iris images prior to matching.
Research on a hole filling algorithm of a point cloud based on structure from motion

A point cloud can be obtained from three-dimensional measurement reconstruction based on fringe projection. However, there are holes in a point cloud due to objects with complicated shapes and the defect of the method. The holes have a profound impact on the subsequent data processing. A fitting approach to fill the holes based on structure from motion (SFM) is proposed in this paper. First, fringe projection with a two-dimensional phase is used to extract the hole boundary. Second, the registration of the SFM point cloud and the fringe projection point cloud is carried out. Then supplementary points are extracted. Third, the holes are filled based on a radial basis function on the point cloud added with the supplementary points. This method has been proven to be robust by experiments, and information of complex surface holes can be restored sufficiently.
Composable security of two-way continuous-variable quantum key distribution without active symmetrization

We present a general framework encompassing a number of continuous-variable quantum key distribution protocols, including standard one-way protocols, measurement-device-independent protocols, as well as some two-way protocols, or any other continuous-variable protocol involving only a Gaussian modulation of coherent states and heterodyne detection. The main interest of this framework is that the corresponding protocols are all covariant with respect to the action of the unitary group $\text{U}(n)$, implying that their security can be established thanks to a Gaussian de Finetti reduction. In particular, we give a composable security proof of two-way continuous-variable quantum key distribution against general attacks. We also prove that no active symmetrization procedure is required for these protocols, which would otherwise make them prohibitively costly to implement.
Hourly Urban Water Demand Forecasting Using the Continuous Deep Belief Echo State Network

Effective and accurate water demand prediction is an important part of the optimal scheduling of a city water supply system. A novel deep architecture model called the continuous deep belief echo state network (CDBESN) is proposed in this study for the prediction of hourly urban water demand. The CDBESN model uses a continuous deep belief network (CDBN) as the feature extraction algorithm and an echo state network (ESN) as the regression algorithm. The new architecture can model actual water demand data with fast convergence and global optimization ability. The prediction capacity of the CDBESN model is tested using historical hourly water demand data obtained from an urban waterworks in Zhuzhou, China. The performance of the proposed model is compared with those of ESN, continuous deep belief neural network, and support vector regression models. The correlation coefficient (r2), normalized root-mean-square error (NRMSE), and mean absolute percentage error (MAPE) are adopted as assessment criteria. Forecasting results obtained in the testing stage indicate that the CDBESN model has the largest r2 value of 0.995912 and the smallest NRMSE and MAPE values of 0.027163 and 2.469419, respectively. The prediction accuracy of the proposed model clearly outperforms those of the models it is compared with due to the good feature extraction ability of CDBN and the excellent feature learning ability of ESN.
Prediction intervals with random forests

The classical and most commonly used approach to building prediction intervals is the parametric approach. However, its main drawback is that its validity and performance highly depend on the assumed functional link between the covariates and the response. This research investigates new methods that improve the performance of prediction intervals with random forests. Two aspects are explored: The method used to build the forest and the method used to build the prediction interval. Four methods to build the forest are investigated, three from the classification and regression tree (CART) paradigm and the transformation forest method. For CART forests, in addition to the default least-squares splitting rule, two alternative splitting criteria are investigated. We also present and evaluate the performance of five flexible methods for constructing prediction intervals. This yields 20 distinct method variations. To reliably attain the desired confidence level, we include a calibration procedure performed on the out-of-bag information provided by the forest. The 20 method variations are thoroughly investigated, and compared to five alternative methods through simulation studies and in real data settings. The results show that the proposed methods are very competitive. They outperform commonly used methods in both in simulation settings and with real data.
An Overview of Restricted Boltzmann Machines

The restricted Boltzmann machine (RBM) is a two-layered network of stochastic units with undirected connections between pairs of units in the two layers. The two layers of nodes are called visible and hidden nodes. In an RBM, there are no connections from visible to visible or hidden to hidden nodes. RBMs are used mainly as a generative model. They can be suitably modified to perform classification tasks also. They are among the basic building blocks of other deep learning models such as deep Boltzmann machine and deep belief networks. The aim of this article is to give a tutorial introduction to the restricted Boltzmann machines and to review the evolution of this model.