Reviewerly/exHarmony huggingface dataset:
Deep mind in social responses to technologies: A new approach to explaining the Computers are Social Actors phenomena

The Computers are Social Actors (CASA) paradigm has been increasingly used as a major theoretical framework to explain users' social responses to emerging technologies such as chatbots, voice assistants, and social robots. However, the core explanatory mechanism of the CASA paradigm is still under debate. In past works, mixed findings have emerged to support the mindlessness explanation and the mindfulness explanation. Thus, to better understand which mechanism features more explanatory power, this study analyzed 834 participants' responses and adopted a new approach combining the experimental design and classical multidimensional scaling. An investigation into participants' cognitive maps of technology differences in evoking social presence and perceived trustworthiness suggested that compared to mindfulness, mindlessness had more power in accounting for users' social responses to technologies. The findings can serve as important evidence for the explanatory mechanism of the CASA paradigm, make methodological contributions, and have practical implications.
MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients

Existing model poisoning attacks to federated learning assume that an attacker has access to a large fraction of compromised genuine clients. However, such assumption is not realistic in production federated learning systems that involve millions of clients. In this work, we propose the first Model Poisoning Attack based on Fake clients called MPAF. Specifically, we assume the attacker injects fake clients to a federated learning system and sends carefully crafted fake local model updates to the cloud server during training, such that the learnt global model has low accuracy for many indiscriminate test inputs. Towards this goal, our attack drags the global model towards an attacker-chosen base model that has low accuracy. Specifically, in each round of federated learning, the fake clients craft fake local model updates that point to the base model and scale them up to amplify their impact before sending them to the cloud server. Our experiments show that MPAF can significantly decrease the test accuracy of the global model, even if classical defenses and norm clipping are adopted, highlighting the need for more advanced defenses.
Learning Frequency-aware Dynamic Network for Efficient Super-Resolution

Deep learning based methods, especially convolutional neural networks (CNNs) have been successfully applied in the field of single image super-resolution (SISR). To obtain better fidelity and visual quality, most of existing networks are of heavy design with massive computation. However, the computation resources of modern mobile devices are limited, which cannot easily support the expensive cost. To this end, this paper explores a novel frequency-aware dynamic network for dividing the input into multiple parts according to its coefficients in the discrete cosine transform (DCT) domain. In practice, the high-frequency part will be processed using expensive operations and the lower-frequency part is assigned with cheap operations to relieve the computation burden. Since pixels or image patches belong to low-frequency areas contain relatively few textural details, this dynamic network will not affect the quality of resulting super-resolution images. In addition, we embed predictors into the proposed dynamic network to end-to-end fine-tune the handcrafted frequency-aware masks. Extensive experiments conducted on benchmark SISR models and datasets show that the frequency-aware dynamic network can be employed for various SISR neural architectures to obtain the better tradeoff between visual quality and computational complexity. For instance, we can reduce the FLOPs of SR models by approximate 50% while preserving state-of-the-art SISR performance.
THUNDR: Transformer-based 3D HUmaN Reconstruction with Markers

We present THUNDR, a transformer-based deep neural network methodology to reconstruct the 3d pose and shape of people, given monocular RGB images. Key to our methodology is an intermediate 3d marker representation, where we aim to combine the predictive power of model-free-output architectures and the regularizing, anthropometrically-preserving properties of a statistical human surface model like GHUM—a recently introduced, expressive full body statistical 3d human model, trained end-to-end. Our novel transformer-based prediction pipeline can focus on image regions relevant to the task, supports self-supervised regimes, and ensures that solutions are consistent with human anthropometry. We show state-of-the-art results on Human3.6M and 3DPW, for both the fully-supervised and the self-supervised models, for the task of inferring 3d human shape, joint positions, and global translation. Moreover, we observe very solid 3d reconstruction performance for difficult human poses collected in the wild.
API Features Individualizing of Web Services: REST and SOAP

Web Services are combination of open protocols and standards to allow communication between client and server. It provides an interoperability between contrasting applications. Representational state Transfer (REST) and Simple Object Access Protocol (SOAP) are the two main popular used web services now-a-days. REST is an architectural style based, whereas SOAP is a underlying protocol. Both services are used to handle the communication on the world wide web (www). Both services have some advantages and drawbacks and it is the decision of web developer to decide which service is best to use according to its requirements. The aim of this research work is to design a REST API and SOAP API by JAX-RS and JAX-WS, respectively and gives a comparative analysis of Application Programming Interface (API) features (in terms of response time, memory usage, execution speed and so on) of these services by using API testing tool like Postman. This gives insight view of which service is better to use as per requirements. The result of experiments shows that the response time of SOAP is approximate takes 4ms to 7ms more than REST. It has been observed that as number of API increases, SOAP takes approximate 1MB to 2MB more memory usage than REST
Fault Detection and Classification in Industrial IoT in Case of Missing Sensor Data

This article addresses the issue of reliability in the Industrial Internet of Things (IIoT) in case of missing sensors measurements due to network or hardware problems.We propose to support the fault detection and classification modules, which are the two critical components of a monitoring system for IIoT, with a generative model.The latter is responsible for imputing missing sensor measurements so that the monitoring system performance is robust to missing data.In particular, we adopt generative adversarial networks (GANs) to generate missing sensor measurements and we propose to fine-tune the training of the GAN based on the impact that the generated data have on the fault detection and classification modules.We conduct a thorough evaluation of the proposed approach using the extended Tennessee Eastman Process data set.Results show that the GANimputed data mitigate the impact on the fault detection and classification even in the case of persistently missing measurements from sensors that are critical for the correct functioning of the monitoring system.
AlignMixup: Improving Representations By Interpolating Aligned Features

Mixup is a powerful data augmentation method that interpolates between two or more examples in the input or feature space and between the corresponding target labels. Many recent mixup methods focus on cutting and pasting two or more objects into one image, which is more about efficient processing than interpolation. However, how to best interpolate images is not well defined. In this sense, mixup has been connected to autoencoders, because often autoencoders "interpolate well", for instance generating an image that continuously deforms into another. In this work, we revisit mixup from the interpolation perspective and introduce AlignMix, where we geometrically align two images in the feature space. The correspondences allow us to interpolate between two sets of features, while keeping the locations of one set. Interestingly, this gives rise to a situation where mixup retains mostly the geometry or pose of one image and the texture of the other, connecting it to style transfer. More than that, we show that an autoencoder can still improve representation learning under mixup, without the classifier ever seeing decoded images. AlignMix outperforms state-of-the-art mixup methods on five different benchmarks.
A user-friendly tool for cloud-based whole slide image segmentation with examples from renal histopathology

Image-based machine learning tools hold great promise for clinical applications in pathology research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often lack the programming experience required for the setup and use of these tools which often rely on the use of command line interfaces.We have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis.By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in three murine models.Histo-Cloud is open source, accessible over the internet, and adaptable for segmentation of any histological structure regardless of stain.
Rhythmic Gesticulator

Automatic synthesis of realistic co-speech gestures is an increasingly important yet challenging task in artificial embodied agent creation. Previous systems mainly focus on generating gestures in an end-to-end manner, which leads to difficulties in mining the clear rhythm and semantics due to the complex yet subtle harmony between speech and gestures. We present a novel co-speech gesture synthesis method that achieves convincing results both on the rhythm and semantics. For the rhythm, our system contains a robust rhythm-based segmentation pipeline to ensure the temporal coherence between the vocalization and gestures explicitly. For the gesture semantics, we devise a mechanism to effectively disentangle both low- and high-level neural embeddings of speech and motion based on linguistic theory. The high-level embedding corresponds to semantics, while the low-level embedding relates to subtle variations. Lastly, we build correspondence between the hierarchical embeddings of the speech and the motion, resulting in rhythm- and semantics-aware gesture synthesis. Evaluations with existing objective metrics, a newly proposed rhythmic metric, and human feedback show that our method outperforms state-of-the-art systems by a clear margin.
Logical quantum processor based on reconfigurable atom arrays

Abstract Suppressing errors is the central challenge for useful quantum computing 1 , requiring quantum error correction (QEC) 2–6 for large-scale processing. However, the overhead in the realization of error-corrected ‘logical’ qubits, in which information is encoded across many physical qubits for redundancy 2–4 , poses substantial challenges to large-scale logical quantum computing. Here we report the realization of a programmable quantum processor based on encoded logical qubits operating with up to 280 physical qubits. Using logical-level control and a zoned architecture in reconfigurable neutral-atom arrays 7 , our system combines high two-qubit gate fidelities 8 , arbitrary connectivity 7,9 , as well as fully programmable single-qubit rotations and mid-circuit readout 10–15 . Operating this logical processor with various types of encoding, we demonstrate improvement of a two-qubit logic gate by scaling surface-code 6 distance from d = 3 to d = 7, preparation of colour-code qubits with break-even fidelities 5 , fault-tolerant creation of logical Greenberger–Horne–Zeilinger (GHZ) states and feedforward entanglement teleportation, as well as operation of 40 colour-code qubits. Finally, using 3D [[8,3,2]] code blocks 16,17 , we realize computationally complex sampling circuits 18 with up to 48 logical qubits entangled with hypercube connectivity 19 with 228 logical two-qubit gates and 48 logical CCZ gates 20 . We find that this logical encoding substantially improves algorithmic performance with error detection, outperforming physical-qubit fidelities at both cross-entropy benchmarking and quantum simulations of fast scrambling 21,22 . These results herald the advent of early error-corrected quantum computation and chart a path towards large-scale logical processors.
Group Signatures with Time-Bound Keys Revisited: A New Model, an Efficient Construction, and its Implementation

Chu et al. (ASIACCS 2012) proposed group signature with time-bound keys (GS-TBK), where each signing key is associated with expiry time τ. In addition, to prove membership of the group, a signer needs to prove that the expiry time has not passed, i.e., t <; τ, where t is the current time. A signer whose expiry time has passed is automatically revoked, and this revocation is called natural revocation. Signers can be revoked simultaneously before their expiry times if the credential is compromised. This revocation is called premature revocation. A nice property in the Chu et al. proposal is that the size of revocation lists can be reduced compared to those of Verifier-Local Revocation (VLR) group signature schemes by assuming that natural revocation accounts for most of the signer revocations in practice, and prematurely revoked signers are only a small fraction. In this paper, we point out that the definition of traceability of Chu et al. did not capture the unforgeability of expiry time for signing keys, which guarantees that no adversary who has a signing key associated with expiry time τ can compute a valid signature after τ has passed. This situation significantly reduces the dependability of the system since legitimate signing keys may be used for providing a forged signature. We introduce a security model that captures unforgeability, and propose a secure GS-TBK scheme in the new model. Our scheme also provides constant signing costs, whereas those of the previous schemes depended on the bit-length of the time representation. Finally, we provide the implementation results. We employ Barreto-Lynn-Scott (BLS) curves with 455-bit prime order and the RELIC library, and demonstrate that our scheme is feasible in practical settings.
Quantum Query Complexity of Entropy Estimation

Estimation of Shannon and Rényi entropies of unknown discrete distributions is a fundamental problem in statistical property testing. In this paper, we give the first quantum algorithms for estimating α-Rényi entropies (Shannon entropy being 1-Rényi entropy). In particular, we demonstrate a quadratic quantum speedup for Shannon entropy estimation and a generic quantum speedup for α-Rényi entropy estimation for all α ≥ 0 values, including tight bounds for the Shannon entropy, the Hartley entropy (α = 0), and the collision entropy (α = 2). We also provide quantum upper bounds for estimating min-entropy (α = +∞) as well as the Kullback-Leibler divergence. We complement our results with quantum lower bounds on α-Rényi entropy estimation for all α ≥ 0 values. Our approach is inspired by the pioneering work of Bravyi, Harrow, and Hassidim (BHH); however, with many new technical ingredients: 1) we improve the error dependence of the BHH framework by a fine-tuned error analysis together with Montanaro's approach to estimating the expected output of quantum subroutines for α = 0, 1; 2) we develop a procedure, similar to cooling schedules in simulated annealing, for general α ≥ 0, and 3) in the cases of integer α ≥ 2 and α = +∞, we reduce the entropy estimation problem to the α-distinctness and [log n]-distinctness problems, respectively.
Toward a Comprehensive Face Detector in the Wild

In this paper, we aim to build a comprehensive face detection system which provides a one-stop solution to various practical challenges for face detection in realistic scenarios, e.g., detecting faces from multiple-views, faces with occlusions, exaggerated expressions or blurred faces. Moreover, we introduce an automatic data harvest algorithm to effectively improve the generalization performance of the system even when collecting training faces containing various challenging patterns is difficult. In particular, we introduce three critical components to build the system, i.e., a recently widely used deep convolutional neural network (CNN), a novel blur-aware bi-channel network architecture, and a new self-learning mechanism capable of exploiting video contexts continuously. The aforementioned challenges except for detecting blurred faces can potentially be addressed by the CNN component owing its robustness to local deformation of target faces. The more challenging problem of detecting blurred faces is addressed by the bi-channel architecture component which processes blurred and clear faces adaptively. In addition, to address the difficulties in improving the generalization performance of the learning-based face detection system, we introduce a video-context-based self-learning mechanism into the system, which enables the system to continuously enhance its performance by harvesting faces with challenging training patterns automatically. To exploit video context, the detector is applied to massive unlabeled videos, and challenging faces are captured based on temporal inference. These recaptured faces, generally corresponding to one or multiple challenges mentioned above, are fed into the detection system to further improve its performance. Extensive experiments with the proposed detection system provide new state-of-the-art performance on FDDB data set, PASCAL face data set, AFW data set, and WIDER Face data set.
Fuzzy Rule-Based Design of Evolutionary Algorithm for Optimization

During the last two decades, many multioperator- and multimethod-based evolutionary algorithms for solving optimization problems have been proposed. Although, in general terms, they outperform single-operator-based traditional ones, they do not perform consistently for all the problems tested in the literature. The designs of such algorithms usually follow a trial and error approach that can be improved by using a rule-based approach. In this paper, we propose a new way for two algorithms to cooperate as an effective team, in which a heuristic is applied using fuzzy rules of two complementary characteristics, the quality of solutions and diversity in the population. In this process, two subpopulations are used, one for each algorithm, with greater emphasis placed on the better-performing one. Inferior algorithms learn from trusted ones and a fine-tuning procedure is applied in the later stages of the evolutionary process. The proposed algorithm was analyzed on the CEC2014 unconstrained problems and then tested on other three sets (CEC2013, CEC2005, and 12 classical problems), with its results showing a high success rate and that it outperformed both single-operator-based and different state-of-the-art algorithms.
Automatic Generation of Tests to Exploit XML Injection Vulnerabilities in Web Applications

Modern enterprise systems can be composed of many web services (e.g., SOAP and RESTful). Users of such systems might not have direct access to those services, and rather interact with them through a single-entry point which provides a GUI (e.g., a web page or a mobile app). Although the interactions with such entry point might be secure, a hacker could trick such systems to send malicious inputs to those internal web services. A typical example is XML injection targeting SOAP communications. Previous work has shown that it is possible to automatically generate such kind of attacks using search-based techniques. In this paper, we improve upon previous results by providing more efficient techniques to generate such attacks. In particular, we investigate four different algorithms and two different fitness functions. A large empirical study, involving also two industrial systems, shows that our technique is effective at automatically generating XML injection attacks.
Hybrid Elitist-Ant System for Nurse-Rostering Problem

The diversity and quality of high-quality and diverse-solution external memory of the hybrid Elitist-Ant System is examined in this study. The Elitist-Ant System incorporates an external memory for preserving search diversity while exploiting the solution space. Using this procedure, the effectiveness and efficiency of the search may be guaranteed which could consequently improve the performance of the algorithm and it could be well generalized across diverse problems of combinatorial optimization. The generality of this algorithm through its consistency and efficiency is tested using a Nurse-Rostering Problem. The outcomes demonstrate the competitiveness of the hybrid Elitist-Ant System's performance within numerous datasets as opposed to those by other systems. The effectiveness of the external memory usage in search diversification is evidenced in this work. Subsequently, such usage improves the performance of the hybrid Elitist-Ant System over diverse datasets and problems.
A visual programming framework for distributed Internet of Things centric complex event processing

Complex Event Processing (CEP) is a promising approach for real-time processing of big-data streams originating from Internet of Things (IoT) devices. Even though scalability and flexibility are key issues for IoT applications, current studies are mostly based on centralized solutions and restrictive query languages. Moreover, development, deployment and operation of big-data applications require significant amount of technical expertise. Hence, a framework that provides a higher abstraction level programming model and a streamlined execution environment is essential to facilitate widespread user adoption. In this paper, we propose a data-flow based visual programming model that enables development of big-data applications in IoT domain even by users who have limited or no experience in big-data technologies. A scalable CEP engine is also devised to execute the applications developed using this language. A prototype is implemented by using the state-of-the-art big-data and real-time stream processing technologies to evaluate the applicability and usability of the framework.
Extendibility Limits the Performance of Quantum Processors

Resource theories in quantum information science are helpful for the study and quantification of the performance of information-processing tasks that involve quantum systems. These resource theories also find applications in other areas of study; e.g., the resource theories of entanglement and coherence have found use and implications in the study of quantum thermodynamics and memory effects in quantum dynamics. In this paper, we introduce the resource theory of unextendibility, which is associated with the inability of extending quantum entanglement in a given quantum state to multiple parties. The free states in this resource theory are the k-extendible states, and the free channels are k-extendible channels, which preserve the class of k-extendible states. We make use of this resource theory to derive nonasymptotic, upper bounds on the rate at which quantum communication or entanglement preservation is possible by utilizing an arbitrary quantum channel a finite number of times, along with the assistance of k-extendible channels at no cost. We then show that the bounds obtained are significantly tighter than previously known bounds for quantum communication over both the depolarizing and erasure channels.