Reviewerly/exHarmony huggingface dataset:
Curie

Data sharing among partners---users, companies, organizations---is crucial for the advancement of collaborative machine learning in many domains such as healthcare, finance, and security. Sharing through secure computation and other means allow these partners to perform privacy-preserving computations on their private data in controlled ways. However, in reality, there exist complex relationships among members (partners). Politics, regulations, interest, trust, data demands and needs prevent members from sharing their complete data. Thus, there is a need for a mechanism to meet these conflicting relationships on data sharing. This paper presents, an approach to exchange data among members who have complex relationships. A novel policy language, CPL, that allows members to define the specifications of data exchange requirements is introduced. With CPL, members can easily assert who and what to exchange through their local policies and negotiate a global sharing agreement. The agreement is implemented in a distributed privacy-preserving model that guarantees sharing among members will comply with the policy as negotiated. The use of Curie is validated through an example healthcare application built on recently introduced secure multi-party computation and differential privacy frameworks, and policy and performance trade-offs are explored.
Non-linear Causal Inference Using Gaussianity Measures

We provide theoretical and empirical evidence for a type of asymmetry between causes and effects that is present when these are related via linear models contaminated with additive non-Gaussian noise. Assuming that the causes and the effects have the same distribution, we show that the distribution of the residuals of a linear fit in the anti-causal direction is closer to a Gaussian than the distribution of the residuals in the causal direction. This Gaussianization effect is characterized by reduction of the magnitude of the high-order cumulants and by an increment of the differential entropy of the residuals. The problem of non-linear causal inference is addressed by performing an embedding in an expanded feature space, in which the relation between causes and effects can be assumed to be linear. The effectiveness of a method to discriminate between causes and effects based on this type of asymmetry is illustrated in a variety of experiments using different measures of Gaussianity. The proposed method is shown to be competitive with state-of-the-art techniques for causal inference.
Person Recognition in Personal Photo Collections

People nowadays share large parts of their personal lives through social media.Being able to automatically recognise people in personal photos may greatly enhance user convenience by easing photo album organisation.For human identification task, however, traditional focus of computer vision has been face recognition and pedestrian re-identification.Person recognition in social media photos sets new challenges for computer vision, including non-cooperative subjects (e.g.backward viewpoints, unusual poses) and great changes in appearance.To tackle this problem, we build a simple person recognition framework that leverages convnet features from multiple image regions (head, body, etc.).We propose new recognition scenarios that focus on the time and appearance gap between training and testing samples.We present an in-depth analysis of the importance of different features according to time and viewpoint generalisability.In the process, we verify that our simple approach achieves the state of the art result on the PIPA [1] benchmark, arguably the largest social media based benchmark for person recognition to date with diverse poses, viewpoints, social groups, and events.Compared the conference version of the paper [2], this paper additionally presents (1) analysis of a face recogniser (DeepID2+ [3]), ( 2) new method naeil2 that combines the conference version method naeil and DeepID2+ to achieve state of the art results even compared to post-conference works, (3) discussion of related work since the conference version, (4) additional analysis including the head viewpoint-wise breakdown of performance, and (5) results on the open-world setup.
Learning Moore machines from input–output traces

The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper, we study this problem for finite-state machines with inputs and outputs, and in particular for Moore machines. We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input–output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI has the fundamental identification in the limit property. We compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. We also carry out a performance comparison against two existing tools (LearnLib and flexfringe). Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers and find that OSTIA generally does not learn a Moore machine, even when fed with a characteristic sample.
Fairness-Efficiency Allocation of CPU-GPU Heterogeneous Resources

Considering the performance improvement the cloud technology provides by processing workloads in parallel, applications and services are now migrating to online clouds. In a cloud platform, workloads can be executed in a virtualized environment to have a great improvement of the resource utilization. However, there is a new challenge in the allocation problem, which is quantifying and optimizing the fairness and efficiency of heterogeneous resources (CPUs and GPUs) required by applications such as cloud gaming. The solving approach needs scalarization methods of the requirement vector, relevant functions for fairness metrics, and an acceptable algorithm to solve that, where the difficulties mainly locate. We design an iterative, dynamic-adaptive heuristic solving algorithm Fairness-Efficiency Allocation (FEA) and optimize the implementation on a virtualized platform, which collects runtime data, allocates resources and reports differences. Data are recorded and analyzed to discover the effect of the allocation in different situations, including the promotion of fairness and the effect on the frame rate of the workloads. The result indicates that there is a considerable fairness improvement after the resource allocation, especially in situations that many virtual machines are executing simultaneously. Compared with the VGASA strategy, the fairness metric value improved 45 percent in three virtual machines' situation.
Linking indexing data structures to de Bruijn graphs: Construction and update

DNA sequencing technologies have tremendously increased their throughput, and hence complicated DNA assembly. Numerous assembly programs use de Bruijn graphs (dBG) built from short reads to merge these into contigs, which represent putative DNA segments. In a dBG of order k, nodes are substrings of length k of reads (or k-mers), while arcs are their k+1-mers. As analysing reads often require to index all their substrings, it is interesting to exhibit algorithms that directly build a dBG from a pre-existing index, and especially a contracted dBG, where non-branching paths are condensed into single nodes. Here, we exhibit linear time algorithms for constructing the full or contracted dBGs from suffix trees, suffix arrays, and truncated suffix trees. With the latter the construction uses a space that is linear in the size of the dBG. Finally, we also provide algorithms to dynamically update the order of the graph without reconstructing it.
Streaming k-mismatch with error correcting and applications

We present a new streaming algorithm for the k-Mismatch problem, one of the most basic problems in pattern matching. Given a pattern and a text, the task is to find all substrings of the text that are at the Hamming distance at most k from the pattern. Our algorithm is enhanced with an important new feature called Error Correcting, and its complexities for k=1 and for a general k are comparable to those of the solutions for the k-Mismatch problem by Porat and Porat (FOCS 2009) and Clifford et al. (SODA 2016). In parallel to our research, a yet more efficient algorithm for the k-Mismatch problem with the Error Correcting feature was developed by Clifford et al. (SODA 2019). Using the new feature and recent work on streaming Multiple Pattern Matching we develop a series of streaming algorithms for pattern matching on weighted strings, which are a commonly used representation of uncertain sequences in molecular biology. We also show that these algorithms are space-optimal up to polylog factors. A preliminary version of this work was published at DCC 2017 conference [24].
A model for discovering ‘containment’ relations

Rapid developments in the fields of learning and object recognition have been obtained by successfully developing and using methods for learning from a large number of labeled image examples. However, such current methods cannot explain infants' learning of new concepts based on their visual experience, in particular, the ability to learn complex concepts without external guidance, as well as the natural order in which related concepts are acquired. A remarkable example of early visual learning is the category of 'containers' and the notion of 'containment'. Surprisingly, this is one of the earliest spatial relations to be learned, starting already around 3 month of age, and preceding other common relations (e.g., 'support', 'in-between'). In this work we present a model, which explains infants' capacity of learning 'containment' and related concepts by 'just looking', together with their empirical development trajectory. Learning occurs in the model fast and without external guidance, relying only on perceptual processes that are present in the first months of life. Instead of labeled training examples, the system provides its own internal supervision to guide the learning process. We show how the detection of so-called 'paradoxical occlusion' provides natural internal supervision, which guides the system to gradually acquire a range of useful containment-related concepts. Similar mechanisms of using implicit internal supervision can have broad application in other cognitive domains as well as artificial intelligent systems, because they alleviate the need for supplying extensive external supervision, and because they can guide the learning process to extract concepts that are meaningful to the observer, even if they are not by themselves obvious, or salient in the input.
MUIT: A Domain-Specific Language and its Middleware for Adaptive Mobile Web-Based User Interfaces in WS-BPEL

In enterprise organizations, the Bring-Your-Own-Device (BYOD) requirement has become prevalent as employees use their own mobile devices to process the workflow-oriented tasks. Consequently, it calls for approaches that can quickly develop and integrate mobile user interactions into existing business processes, and adapt to various contexts. However, designing, developing, and deploying adaptive and mobile-oriented user interfaces for existing process engines are non-trivial, and require significant systematic efforts. To address this issue, we present a novel middleware-based approach, called MUIT, to developing and deploying the Mobility, User Interactions and Tasks into WS-BPEL engines. MUIT provides a Domain-Specific Language (DSL) that provides some intuitive facilities to support the declarative development of adaptive, mobile-oriented, and Web-based user interfaces in WS-BPEL. The DSL can significantly reduce developers' manual efforts of developing user interactions by preventing arbitrarily mixed code, and its runtime supports satisfactory user experiences. Additionally, MUIT can be seamlessly integrated into WS-BPEL without intrusions of existing process instances. We implement a proof-of-concept prototype by integrating MUIT into the commodity WS-BPEL-based Apusic Platform, and evaluate the performance and usability of MUIT platform.
On (α + uβ)-constacyclic codes of length 4ps over 𝔽pm + u𝔽pm∗

For any odd prime [Formula: see text] such that [Formula: see text], the structures of all [Formula: see text]-constacyclic codes of length [Formula: see text] over the finite commutative chain ring [Formula: see text] [Formula: see text] are established in term of their generator polynomials. When the unit [Formula: see text] is a square, each [Formula: see text]-constacyclic code of length [Formula: see text] is expressed as a direct sum of two constacyclic codes of length [Formula: see text]. In the main case that the unit [Formula: see text] is not a square, it is shown that the ambient ring [Formula: see text] is a principal ideal ring. From that, the structure, number of codewords, duals of all such [Formula: see text]-constacyclic codes are obtained. As an application, we identify all self-orthogonal, dual-containing, and the unique self-dual [Formula: see text]-constacyclic codes of length [Formula: see text] over [Formula: see text].
A Triangular NodeTrix Visualization Interface for Overlapping Social Community Structures of Cyber-Physical-Social Systems in Smart Factories

This work proposes a cyber-physical-social system (CPSS) framework for smart factories which associates the social-network-based physical factory circumference with the cyber control through a visualization interface based on NodeTrix. The NodeTrix representation consisting of adjacency matrices and node-link diagrams has been popularly used in social network analysis. Different from previous works on NodeTrix based on square-shaped matrices, the proposed NodeTrix applies triangle-shaped matrices for visualizing overlapping communities to save the displaying space, and a simulated annealing algorithm is used to reduce the crossings among links and communities. In the CPSS for smart factories, working-in-process (WIP) products are categorized according to their attributes, and are placed at different storage locations for later different production processes. By the NodeTrix-based interface for CPSS, when users with different domains and experiences work together to make instant decisions on production and logistics, the physical production environment will be adjusted accordingly and immediately through technological support of Industry 4.0. Through the interface, the information on locations, parameters, and interoperability of WIP products can be visualized and adjusted in time. By comparison with other works, the proposed representation looks promising in displaying social networks for the CPSS with fewer crossings within a smaller screen space.
Faster MapReduce Computation on Clouds Through Better Performance Estimation

Processing Big Data in cloud is on the increase. An important issue for efficient execution of Big Data processing jobs on a cloud platform is selecting the best fitting virtual machine (VM) configuration(s) among the miscellany of choices that cloud providers offer. Wise selection of VM configurations can lead to better performance, cost and energy consumption. Therefore, it is crucial to explore the available configurations and opt for the best ones that well suit each MapReduce application. Profiling the given application on all the configurations is costly, time and energy consuming. An alternative is to run the application on a subset of configurations (sample configurations) and estimate its performance on other configurations based on the obtained values by sample configurations. We show that the choice of these sample configurations highly affects accuracy of later estimations. Our Smart Configuration Selection (SCS) scheme chooses better representatives from among all configurations by once-off analysis of given performance figures of the benchmarks so as to increase the accuracy of estimations of missing values, and consequently, to more accurately choose the configuration providing the highest performance. The results show that the SCS choice of sample configurations is very close to the best choice, and can reduce estimation error to 11.58 percent from the original 19.72 percent of random configuration selection. More importantly, using SCS estimations in a makespan minimization algorithm improves the execution time by up to 36.03 percent compared with random sample selection.
A Novel Methodology to Acquire Live Big Data Evidence from the Cloud

In the last decade Digital Forensics has experienced several issues when dealing with network evidence. Collecting network evidence is difficult due to its volatility. In fact, such information may change overtime, may be stored on a server out jurisdiction or geographically far from the crime scene. On the other hand, the explosion of the Cloud Computing as the implementation of the Software as a Service (SaaS) paradigm is pushing users toward remote data repositories such as Dropbox, Amazon Cloud Drive, Apple iCloud, Google Drive, Microsoft OneDrive. In this paper is proposed a novel methodology for the collection of network evidence. In particular, it is focused on the collection of information from online services, such as web pages, chats, documents, photos and videos. The methodology is suitable for both expert and non-expert analysts as it “drives” the user through the whole acquisition process. During the acquisition, the information received from the remote source is automatically collected. It includes not only network packets, but also any information produced by the client upon its interpretation (such as video and audio output). A trusted-third-party, acting as a digital notary, is introduced in order to certify both the acquired evidence (i.e., the information obtained from the remote service) and the acquisition process (i.e., all the activities performed by the analysts to retrieve it). A proof-of-concept prototype, called LINEA, has been implemented to perform an experimental evaluation of the methodology.
Multi-modal egocentric activity recognition using multi-kernel learning

Egocentric activity recognition in first-person videos has an increasing importance with a variety of applications such as lifelogging, summarization, assisted-living and activity tracking. Existing methods for this task are based on interpretation of various sensor information using pre-determined weights for each feature. In this work, we propose a new framework for egocentric activity recognition problem based on combining audio-visual features with multi-kernel learning (MKL) and multi-kernel boosting (MKBoost). For that purpose, firstly grid optical-flow, virtual-inertia feature, log-covariance, cuboid are extracted from the video. The audio signal is characterized using a "supervector", obtained based on Gaussian mixture modelling of frame-level features, followed by a maximum a-posteriori adaptation. Then, the extracted multi-modal features are adaptively fused by MKL classifiers in which both the feature and kernel selection/weighing and recognition tasks are performed together. The proposed framework was evaluated on a number of egocentric datasets. The results showed that using multi-modal features with MKL outperforms the existing methods.
An algorithm for learning shape and appearance models without annotations

This paper presents a framework for automatically learning shape and appearance models for medical (and certain other) images. The algorithm was developed with the aim of eventually enabling distributed privacy-preserving analysis of brain image data, such that shared information (shape and appearance basis functions) may be passed across sites, whereas latent variables that encode individual images remain secure within each site. These latent variables are proposed as features for privacy-preserving data mining applications. The approach is demonstrated qualitatively on the KDEF dataset of 2D face images, showing that it can align images that traditionally require shape and appearance models trained using manually annotated data (manually defined landmarks etc.). It is applied to the MNIST dataset of handwritten digits to show its potential for machine learning applications, particularly when training data is limited. The model is able to handle "missing data", which allows it to be cross-validated according to how well it can predict left-out voxels. The suitability of the derived features for classifying individuals into patient groups was assessed by applying it to a dataset of over 1900 segmented T1-weighted MR images, which included images from the COBRE and ABIDE datasets.
Boosting linear-optical Bell measurement success probability with predetection squeezing and imperfect photon-number-resolving detectors

Linear-optical realizations of Bell state measurement (BSM) on two single-photon qubits succeed with probability ${p}_{s}$ no higher than 0.5. However, predetection quadrature squeezing, i.e., quantum noise limited phase sensitive amplification, in the usual linear-optical BSM circuit, can yield ${p}_{s}\ensuremath{\approx}0.643$. The ability to achieve ${p}_{s}&gt;0.5$ has been found to be critical in resource-efficient realizations of linear-optical quantum computing and all-photonic quantum repeaters. Yet, the aforesaid value of ${p}_{s}&gt;0.5$ is not known to be the maximum achievable using squeezing, thereby leaving it open whether close-to-$100%$ efficient BSM might be achievable using squeezing as a resource. In this paper, we report insights on why squeezing-enhanced BSM achieves ${p}_{s}&gt;0.5$. Using this, we show that the previously reported ${p}_{s}\ensuremath{\approx}0.643$ at single-mode squeezing strength $r=0.6585$---for unambiguous state discrimination (USD) of all four Bell states---is an experimentally unachievable point result, which drops to ${p}_{s}\ensuremath{\approx}0.59$ with the slightest change in $r$. We, however, show that squeezing-induced boosting of ${p}_{s}$ with USD operation is still possible over a continuous range of $r$, with an experimentally achievable maximum occurring at $r=0.5774$, achieving ${p}_{s}\ensuremath{\approx}0.596$. Finally, deviating from USD operation, we explore a trade space between ${p}_{s}$, the probability with which the BSM circuit declares a ``success,'' versus the probability of error ${p}_{e}$, the probability of an input Bell state being erroneously identified given the circuit declares a success. Since quantum error correction could correct for some ${p}_{e}&gt;0$, this tradeoff may enable better quantum repeater designs by potentially increasing the entanglement generation rates with ${p}_{s}$ exceeding what is possible with traditionally studied USD operation of BSMs.