Reviewerly/exHarmony huggingface dataset:
Fast secure matrix multiplications over ring-based homomorphic encryption

As widespread development of biometrics, concerns about security and privacy are rapidly increasing. Secure matrix computation is one of the most fundamental and useful operations for statistical analysis and machine learning with protecting the confidentiality of input data. Secure computation can be achieved by homomorphic encryption, supporting meaningful operations over encrypted data. HElib is a software library that implements the Brakerski-Gentry-Vaikuntanathan (BGV) homomorphic scheme, in which secure matrix-vector multiplication is proposed for operating matrices. Recently, Duong et al. (Tatra Mt. Publ) proposed a new method for secure single matrix multiplication over a ring-LWE-based scheme. In this paper, we generalize Duong et al.’s method for secure multiple matrix multiplications over the BGV scheme. We also implement our method using HElib, and show that our method is much faster than the matrix-vector multiplication in HElib for secure matrix multiplications.
A Lower Bound for One-Round Oblivious RAM

We initiate a fine-grained study of the round complexity of Oblivious RAM (ORAM). We prove that any one-round balls-in-bins ORAM that does not duplicate balls must have either $$\varOmega (\sqrt{N})$$ bandwidth or $$\varOmega (\sqrt{N})$$ client memory, where N is the number of memory slots being simulated. This shows that such schemes are strictly weaker than general (multi-round) ORAMs or those with server computation, and in particular implies that a one-round version of the original square-root ORAM of Goldreich and Ostrovksy (J. ACM 1996) is optimal. We prove this bound via new techniques that differ from those of Goldreich and Ostrovksy, and of Larsen and Nielsen (CRYPTO 2018), which achieved an $$\varOmega (\log N)$$ bound for balls-in-bins and general multi-round ORAMs respectively. Finally we give a weaker extension of our bound that allows for limited duplication of balls, and also show that our bound extends to multiple-round ORAMs of a restricted form that include the best known constructions.
AdaPipe: A Recommender System for Adaptive Computation Pipelines in Cyber-Manufacturing Computation Services

The industrial cyber-physical systems (ICPS) will accelerate the transformation of offline data-driven modeling to fast computation services, such as computation pipelines for prediction, monitoring, prognosis, diagnosis, and control in factories. However, it is computationally intensive to adapt computation pipelines to heterogeneous contexts in ICPS in manufacturing. In this article, we propose to rank and select the best computation pipelines to match contexts and formulate the problem as a recommendation problem. The proposed method adaptive computation pipelines (AdaPipe) considers similarities of computation pipelines from word embedding, and features of contexts. Thus, without exploring all computation pipelines extensively in a trial-and-error manner, AdaPipe efficiently identifies top-ranked computation pipelines. We validated the proposed method with 60 bootstrapped datasets from three real manufacturing processes: thermal spray coating, printed electronics, and additive manufacturing. The results indicate that the proposed recommendation method outperforms the traditional matrix completion, tensor regression methods, and a state-of-the-art personalized recommendation model.
Tell Me More: Transparency and Time-Fillers to Optimize Chatbots’ Waiting Time Experience

In customer-chatbot conversations, the agents regularly come to their limit, which requires to hand over the request to a human service representative. In the case of unmanaged handovers, customers encounter huge uncertainty while waiting and are often left alone with unsatisfied service inquiries. The waiting time (and service experience) might result in frustration and potential loss of the customer. In this paper, we discuss results from a study aimed at turning chatbot handover routines into positive experiences. In a lab study with (n=28) participants, we compared two handover types that utilized the concepts of time fillers and time and queue information with a baseline handover without such countermeasures. Our results, based on subjective waiting experience, service quality, as well as user experience and affect confirm that digital enterprises have to provide maximum transparency to customers, e.g., details about waiting time and queue status. In addition, they might use "time-fillers'' in waiting situations to further improve customer satisfaction.
Federated Topic Discovery: A Semantic Consistent Approach

General-purpose topic models have widespread industrial applications. Yet high-quality topic modeling is becoming increasingly challenging because accurate models require large amounts of training data typically owned by multiple parties, who are often unwilling to share their sensitive data for collaborative training without guarantees on their data privacy. To enable effective privacy-preserving multiparty topic modeling, we propose a novel federated general-purpose topic model named private and consistent topic discovery (PC-TD). On the one hand, PC-TD seamlessly integrates differential privacy in topic modeling to provide privacy guarantees on sensitive data of different parties. On the other hand, PC-TD exploits multiple sources of semantic consistency information to retain the accuracy of topic modeling while protecting data privacy. We verify the effectiveness of PC-TD on real-life datasets. Experimental results demonstrate its superiority over the state-of-the-art general-purpose topic models.
Online Filtering Training Samples for Robust Visual Tracking

In recent years, discriminative trackers show its great tracking performance, that is mainly due to the online updating using samples collected during tracking. The model could adapt appearance changes of objects and the background well after updating. But these trackers have a serious disadvantage that wrong samples may cause severe model degradation. Most of the training samples in the tracking phase are obtained according to the tracking result of the current frame. Wrong training samples will be collected when the tracking result is inaccurate, seriously affecting the discrimination ability of the model. Besides, partial occlusion also leads to the same problem. In this paper, we propose an optimization module named MetricNet for online filtering training samples. It applies a matching network containing the classification and distance branches, and uses multiple metric methods for different type samples. MetricNet optimizes the training sample set by recognizing wrong and redundant samples, thereby improving the tracking performance. The proposed MetricNet can be regarded as an independent optimization module and integrated into all discriminative trackers updated online. Extensive experiments on three tracking datasets show its effectiveness and generalization ability. After applying MetricNet to MDNet, the tracking result is increased by 5.3% in terms of the success plot on the LaSOT dataset. Our project is available at https://github.com/zj5559/MetricNet.
Indexing of diffraction patterns for determination of crystal orientations

The task of determining the orientations of crystals is usually performed by indexing reflections detected on diffraction patterns. The well known underlying principle of indexing methods is universal: they are based on matching experimental scattering vectors to some vectors of the reciprocal lattice. Despite this, the standard attitude has been to devise algorithms applicable to patterns of a particular type. This paper provides a broader perspective. A general approach to indexing of diffraction patterns of various types is presented. References are made to formally similar problems in other research fields, e.g. in computational geometry, computer science, computer vision or star identification. Besides a general description of available methods, concrete algorithms are presented in detail and their applicability to patterns of various types is demonstrated; a program based on these algorithms is shown to index Kikuchi patterns, Kossel patterns and Laue patterns, among others.
Compatible priors for model selection of high-dimensional Gaussian DAGs

Graphical models represent a powerful framework to incorporate conditional independence structure for the statistical analysis of high-dimensional data.In this paper we focus on Directed Acyclic Graphs (DAGs).In the Gaussian setting, a prior recently introduced for the parameters associated to the (modified) Cholesky decomposition of the precision matrix is the DAG-Wishart.The flexibility introduced through a rich choice of shape hyperparameters coupled with conjugacy are two desirable assets of this prior which are especially welcome for estimation and prediction.In this paper we look at the DAG-Wishart prior from the perspective of model selection, with special reference to its consistency properties in high dimensional settings.We show that Bayes factor consistency only holds when comparing two DAGs which do not belong to the same Markov equivalence class, equivalently they encode distinct conditional independencies; a similar result holds for posterior ratio consistency.We also prove that DAG-Wishart distributions with arbitrarily chosen hyperparameters will lead to incompatible priors for model selection, because they assign different marginal likelihoods to Markov equivalent graphs.To overcome this difficulty, we propose a constructive method to specify DAG-Wishart priors whose suitably constrained shape hyperparameters ensure compatibility for DAG model selection.
Measuring the Importance of Temporal Features in Video Saliency

Where people look when watching videos is believed to be heavily influenced by temporal patterns. In this work, we test this assumption by quantifying to which extent gaze on recent video saliency benchmarks can be predicted by a static baseline model. On the recent LEDOV dataset, we find that at least 75% of the explainable information as defined by a gold standard model can be explained using static features. Our baseline model “DeepGaze MR” even outperforms state-of-the-art video saliency models, despite deliberately ignoring all temporal patterns. Visual inspection of our static baseline’s failure cases shows that clear temporal effects on human gaze placement exist, but are both rare in the dataset and not captured by any of the recent video saliency models. To focus the development of video saliency models on better capturing temporal effects we construct a meta-dataset consisting of those examples requiring temporal information.
Exploring the Potential of HySpex Hyperspectral Imagery for Extraction of Copper Content

Detritus geochemical information has been proven through research to be an effective prospecting method in mineral exploration. However, the traditional detritus metal content monitoring methods based on field sampling and laboratory chemical analysis are time-consuming and may not meet the requirements of large-scale metal content monitoring. In this study, we obtained 95 detritus samples and seven HySpex hyperspectral imagery scenes with a spatial resolution of 1 m from Karatag Gobi area, Xinjiang, China, and used partial least squares and wavebands selection methods to explore the usefulness of super-low-altitude HySpex hyperspectral images in estimating detritus feasibility and effectiveness of Cu element content. The results show that: (1) among all the inversion models of transformed spectra, power-logarithm transformation spectrum was the optimal prediction model (coefficient of determination(R2) = 0.586, mean absolute error(MAE) = 21.405); (2) compared to the genetic algorithm (GA) and continuous projection algorithm (SPA), the competitive weighted resampling algorithm (CARS) was the optimal feature band-screening method. The R2 of the inversion model was constructed based on the characteristic bands selected by CARS reaching 0.734, which was higher than that of GA (0.519) and SPA (0.691), and the MAE (19.926) was the lowest. Only 20 bands were used in the model construction, which is lower than that of GA (105) and SPA (42); (3) The power-logarithm transforms, and CARS combined with the model of HySpex hyperspectral images and the Cu content distribution in the study area were obtained, consistent with the actual survey results on the ground. Our results prove that the method incorporating the HySpex hyperspectral data to invert copper content in detritus is feasible and effective, and provides data and a reference method for obtaining geochemical element distribution in a large area and for reducing key areas of geological exploration in the future.
JCoffee: Using Compiler Feedback to Make Partial Code Snippets Compilable

Static program analysis tools are often required to work with only a small part of a program's source code, either due to the unavailability of the entire program or the lack of need to analyze the complete code. This makes it challenging to use static analysis tools that require a complete and typed intermediate representation (IR). We present JCoffee, a tool that leverages compiler feedback to convert partial Java programs into their compilable counterparts by simulating the presence of missing surrounding code. It works with any well-typed code snippet (class, function, or even an unenclosed group of statements) while making minimal changes to the input code fragment. A demo of the tool is available here: https://youtu.be/O4h2gn2Qls.
Teddy: Automatic Recommendation of Pythonic Idiom Usage For Pull-Based Software Projects

Pythonic code is idiomatic code that follows guiding principles and practices within the Python community. Offering performance and readability benefits, Pythonic code is claimed to be widely adopted by experienced Python developers, but can be a learning curve to novice programmers. To aid with Pythonic learning, we create an automated tool, called Teddy, that can help checking the Pythonic idiom usage. The tool offers a prevention mode with Just-In-Time analysis to recommend the use of Pythonic idiom during code review and a detection mode with historical analysis to run a thorough scan of idiomatic and non-idiomatic code. In this paper, we first describe our tool and an evaluation of its performance. Furthermore, we present a case study that demonstrates how to use Teddy in a real-life scenario on an Open Source project. An evaluation shows that Teddy has high precision for detecting Pythonic idiom and non-Pythonic code. Using interactive visualizations, we demonstrate how novice programmers can navigate and identify Pythonic idiom and non-Pythonic code in their projects. Our video demo with the full interactive visualizations is available at https://youtu.be/vOCQReSvBxA.
GANs enabled super-resolution reconstruction of wind field

Abstract Atmospheric flows are governed by a broad variety of spatio-temporal scales, thus making real-time numerical modeling of such turbulent flows in complex terrain at high resolution computationally unmanageable. In this paper, we demonstrate a novel approach to address this issue through a combination of fast coarse scale physics based simulator and a family of advanced machine learning algorithm called the Generative Adversarial Networks. The physics-based simulator generates a coarse wind field in a real wind farm and then ESRGANs enhance the result to a much finer resolution. The method outperforms state of the art bicubic interpolation methods commonly utilized for this purpose.
Semantic Segmentation of Self-Supervised Dataset and Medical Images Using Combination of U-Net and Neural Ordinary Differential Equations

An architecture is proposed in this paper, which combines both the U-net and Neural Ordinary Differential Equations for semantic segmentation. This method consumes very lower memory and at the same time in many cases outperforms some state-of-the-art methodologies in terms of very well known performance metrics for semantic segmentation. The proposed approach is tested on three datasets, two of them are medical images and another one is self-supervised dataset. For all the datasets, the proposed approach outperforms the state-of-the-art methods with the same environmental setup.
CounterFault: Value-Based Fault Localization by Modeling and Predicting Counterfactual Outcomes

This paper presents a new, flexible approach to automatically localizing faults in software, named CounterFault. It uses a form of causal inference called counterfactual prediction to predict the effect, on the success or failure of an execution Ex, of intervening at a statement s to set an assignment target A to a value a that is not actually assigned to A in Ex but that could be if s or Ex was modified. CounterFault generates this prediction without actually modifying s or Ex, by employing a very flexible non-parametric statistical or machine learning model (e.g., a random forest). CounterFault applies this basic idea to estimate, with minimal confounding bias, the average causal effects on program failures of different changes in the values assigned to program variables, and these estimates are then employed to derive suspiciousness scores, which are used to assist developers in localizing faults. This paper also reports on an empirical evaluation of CounterFault involving the widely used Defects4J evaluation framework, which contains real software faults, as well as several other Java numerical programs. CounterFault is compared empirically with two other value-based fault localization techniques and four of the best performing coverage-based techniques. The results indicate that CounterFault is more effective than the competing techniques.
Optimizing a standard preparation procedure for grain size analysis of marine sediments by laser diffraction (MS-PT4SD: Marine sediments-pretreatment for size distribution)

Grain size distributions are a key physical measurement in marine sediment studies. The introduction of laser diffraction spectrometry analyzers a few decades ago, allowed the differentiating of grain size in higher resolution than before, and led to increased volumes of samples analyzed overall. However, pretreatment of the sediment samples is required for obtaining high quality results by this method. Currently there are different pretreatment methods for different sediments, but no inclusive, standard method that is applicable across many types. Moreover, often the particulars of the methods are not fully described in the literature. A standard simple and effective pretreatment method which is applicable for a large variety of marine sediments is required as it can allow for intra and inter study comparisons. This study systematically tested and compared commonly applied digestion and dispersion pretreatment techniques for particle size analysis by laser diffraction spectrometry. The pretreatments were applied on a wide spectrum of marine sediments to distill a single protocol that produces precise, reliable, and consistent results across all samples. The recommended marine sediment pretreatment protocol (‘PT4SD’) reduces the needed amount of sediment for analysis, reduces handler bias and overall digestion time. We propose that using ‘PT4SD’ as a standard pretreatment protocol will greatly benefit sedimentological laboratories individually, as well as encourage and improve the depth of cross-study comparability, thereby improving the collective field of marine sedimentology and related research as a whole.
Duplex adversarial networks for multiple-source domain adaptation

Domain adaptation is a powerful tool for transferring the knowledge of the source domain with sufficient annotations for target tasks. However, most existing domain adaptation methods focus on the single-source–single-target adaptation setting, which ignore that existing different but related multiple source domains are available for target tasks. In this paper, we propose a novel theoretical generalization bound for multiple source domain adaptation, which averages the multiple source hypothesis risks by using HΔH-distance with an assumption that the target hypothesis is the combination of all the source hypotheses. Following this theoretical result, we propose a new algorithm called Duplex Adversarial Networks for Multi-source Adaptation (DAN_MA): one adversarial network is utilized for feature generator to learn domain invariant representations between source and target domains with a domain classifier for each pair of source and target; the other is utilized to eliminate the domain characteristics among different sources by making target samples close to each source task-specific decision boundaries. The shared feature generator aims to learn target representations which are invariant to the multiple domain shifts while still close to the source decision boundaries. Extensive experiments show that our method outperforms other domain adaptation methods on three public datasets.
Mixing user-centered and generalized models for Fall Detection

Fall Detection (FD) using wearable devices has been the focus of many research studies during the last years. Solutions are expected to work autonomously and securely; however, even the commercial products are still far from being reliable in elderly people autonomously everyday life. This research focuses on developing a FD method valid to be deployed on a wearable device with tri-axial accelerometer as sensing unit, and working autonomously without external services. Moreover, each fall type is considered independently and the solution adapts to its current user. The proposal includes three stages: a novel event detection stage followed by a one-class problem classification and a final classifier that labels the anomaly events as Fall or Normal. The process starts gathering data from the current user’s wearable provided the data does not include any fall; this data is used to tune the event detection and to train a one-class classifier. Two different methods are tested: a One-Class Support Vector Machine and a classifier based on the centroid of the normal activities’ instances. The detected anomaly events are then classified as Fall or Normal; this second classifier is previously obtained from data from other users’ past experiences. For this two-class model, two different options are tested as well: a Support Vector Machine and a feed-forward Neural Network. The experimentation stage includes two different published simulated falls data sets. The obtained results show that filtering the detected peaks corresponding to normal activities of the current user helps the two-class classifier for some types of fall events, suggesting to introduce specific one-class filtering per fall type. However, the number of false alarms is still high, and the two-class classifiers have a high variability in their performances according to the user, which still needs further research. The results suggest that it might be interesting to obtain a more accurate two-class classifier using data only from subjects with similar activity levels; the use of online learning might also improve the general performance in the classification stages.