Reviewerly/exHarmony huggingface dataset:
Multi-Client Functional Encryption for Linear Functions in the Standard Model from LWE

Multi-client functional encryption (MCFE) allows $$\ell $$ clients to encrypt ciphertexts $$(\mathbf {C}_{t,1},\mathbf {C}_{t,2},\ldots ,\mathbf {C}_{t,\ell })$$ under some label. Each client can encrypt his own data $$X_i$$ for a label t using a private encryption key $$\mathsf {ek}_i$$ issued by a trusted authority in such a way that, as long as all $$\mathbf {C}_{t,i}$$ share the same label t, an evaluator endowed with a functional key $$\mathsf {dk}_f$$ can evaluate $$f(X_1,X_2,\ldots ,X_\ell )$$ without learning anything else on the underlying plaintexts $$X_i$$. Functional decryption keys can be derived by the central authority using the master secret key. Under the Decision Diffie-Hellman assumption, Chotard et al. (Asiacrypt 2018) recently described an adaptively secure MCFE scheme for the evaluation of linear functions over the integers. They also gave a decentralized variant (DMCFE) of their scheme which does not rely on a centralized authority, but rather allows encryptors to issue functional secret keys in a distributed manner. While efficient, their constructions both rely on random oracles in their security analysis. In this paper, we build a standard-model MCFE scheme for the same functionality and prove it fully secure under adaptive corruptions. Our proof relies on the Learning-With-Errors ($$\mathsf {LWE}$$) assumption and does not require the random oracle model. We also provide a decentralized variant of our scheme, which we prove secure in the static corruption setting (but for adaptively chosen messages) under the $$\mathsf {LWE}$$ assumption.
Deep Constrained Dominant Sets for Person Re-Identification

In this work, we propose an end-to-end constrained clustering scheme to tackle the person re-identification (reid) problem.Deep neural networks (DNN) have recently proven to be effective on person re-identification task.In particular, rather than leveraging solely a probe-gallery similarity, diffusing the similarities among the gallery images in an end-to-end manner has proven to be effective in yielding a robust probe-gallery affinity.However, existing methods do not apply probe image as a constraint, and are prone to noise propagation during the similarity diffusion process.To overcome this, we propose an intriguing scheme which treats person-image retrieval problem as a constrained clustering optimization problem, called deep constrained dominant sets (DCDS).Given a probe and gallery images, we re-formulate person re-id problem as finding a constrained cluster, where the probe image is taken as a constraint (seed) and each cluster corresponds to a set of images corresponding to the same person.By optimizing the constrained clustering in an end-to-end manner, we naturally leverage the contextual knowledge of a set of images corresponding to the given person-images.We further enhance the performance by integrating an auxiliary net alongside DCDS, which employs a multi-scale ResNet.To validate the effectiveness of our method we present experiments on several benchmark datasets and show that the proposed method can outperform state-of-the-art methods.
Path-Independent Quantum Gates with Noisy Ancilla

Ancilla systems are often indispensable to universal control of a nearly isolated quantum system. However, ancilla systems are typically more vulnerable to environmental noise, which limits the performance of such ancilla-assisted quantum control. To address this challenge of ancilla-induced decoherence, we propose a general framework that integrates quantum control and quantum error correction, so that we can achieve robust quantum gates resilient to ancilla noise. We introduce the path independence criterion for fault-tolerant quantum gates against ancilla errors. As an example, a path-independent gate is provided for superconducting circuits with a hardware-efficient design.
MAVA: Multi-Level Adaptive Visual-Textual Alignment by Cross-Media Bi-Attention Mechanism

The rapidly developing information technology leads to a fast growth of visual and textual contents, and it comes with huge challenges to make correlation and perform crossmedia retrieval between images and sentences. Existing methods mainly explore cross-media correlation from either global-level instances as the whole images and sentences, or local-level fine-grained patches as the discriminative image regions and key words, which ignore the complementary information from the relation between local-level fine-grained patches. Naturally, relation understanding is highly important for learning crossmedia correlation. People focus on not only the alignment between discriminative image regions and key words, but also their relations lying in the visual and textual context. Therefore, in this paper, we propose Multi-level Adaptive Visual-textual Alignment (MAVA) approach with the following contributions. First, we propose cross-media multi-pathway fine-grained network to extract not only the local fine-grained patches as discriminative image regions and key words, but also visual relations between image regions as well as textual relations from the context of sentences, which contain complementary information to exploit fine-grained characteristics within different media types. Second, we propose visual-textual bi-attention mechanism to distinguish the fine-grained information with different saliency from both local and relation levels, which can provide more discriminative hints for correlation learning. Third, we propose cross-media multi-level adaptive alignment to explore global, local and relation alignments. An adaptive alignment strategy is further proposed to enhance the matched pairs of different media types, and discard those misalignments adaptively to learn more precise cross-media correlation. Extensive experiments are conducted to perform image-sentence matching on 2 widely-used cross-media datasets, namely Flickr-30K and MS-COCO, comparing with 10 state-of-the-art methods, which can fully verify the effectiveness of our proposed MAVA approach.
AccTEE

Remote computation has numerous use cases such as cloud computing, client-side web applications or volunteer computing. Typically, these computations are executed inside a sandboxed environment for two reasons: first, to isolate the execution in order to protect the host environment from unauthorised access, and second to control and restrict resource usage. Often, there is mutual distrust between entities providing the code and the ones executing it, owing to concerns over three potential problems: (i) loss of control over code and data by the providing entity, (ii) uncertainty of the integrity of the execution environment for customers, and (iii) a missing mutually trusted accounting of resource usage.
Interface evolution patterns

Remote Application Programming Interfaces (APIs) are technology enablers for distributed systems and cloud-native application development. API providers find it hard to design their remote APIs so that they can be evolved easily; refactoring and extending an API while preserving backward compatibility is particularly challenging. If APIs are evolved poorly, clients are critically impacted; high costs to adapt and compensate for downtimes may result. For instance, if an API provider publishes a new incompatible API version, existing clients might break and not function properly until they are upgraded to support the new version. Hence, applying adequate strategies for evolving service APIs is one of the core problems in API governance, which in turn is a prerequisite for successfully integrating service providers with their clients in the long run. Although many patterns and pattern languages are concerned with API, service design, and related integration technologies, patterns guiding the evolution of APIs are missing to date. Extending our emerging pattern language on Microservice API Patterns (MAP), we introduce a set of patterns focusing on API evolution strategies in this paper: API Description, Version Identifier, Semantic Versioning, Eternal Lifetime Guarantee, Limited Lifetime Guarantee, Two in Production, Aggressive Obsolescence, and Experimental Preview. The patterns were mined from public Web APIs and industry projects the authors had been involved in.
Fusion of 2D CNN and 3D DenseNet for Dynamic Gesture Recognition

Gesture recognition has been applied in many fields as it is a natural human–computer communication method. However, recognition of dynamic gesture is still a challenging topic because of complex disturbance information and motion information. In this paper, we propose an effective dynamic gesture recognition method by fusing the prediction results of a two-dimensional (2D) motion representation convolution neural network (CNN) model and three-dimensional (3D) dense convolutional network (DenseNet) model. Firstly, to obtain a compact and discriminative gesture motion representation, the motion history image (MHI) and pseudo-coloring technique were employed to integrate the spatiotemporal motion sequences into a frame image, before being fed into a 2D CNN model for gesture classification. Next, the proposed 3D DenseNet model was used to extract spatiotemporal features directly from Red, Green, Blue (RGB) gesture videos. Finally, the prediction results of the proposed 2D and 3D deep models were blended together to boost recognition performance. The experimental results on two public datasets demonstrate the effectiveness of our proposed method.
Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?

We present an analysis of predictive uncertainty based out-of-distribution detection for different approaches to estimate various models' epistemic uncertainty and contrast it with extreme value theory based open set recognition. While the former alone does not seem to be enough to overcome this challenge, we demonstrate that uncertainty goes hand in hand with the latter method. This seems to be particularly reflected in a generative model approach, where we show that posterior based open set recognition outperforms discriminative models and predictive uncertainty based outlier rejection, raising the question of whether classifiers need to be generative in order to know what they have not seen.
Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes

In this paper, we theoretically prove that gradient descent can find a global minimum for nonlinear deep neural networks of sizes commonly encountered in practice. The theory developed in this paper only requires the practical degrees of over-parameterization unlike previous theories. Our theory only requires the number of trainable parameters to increase linearly as the number of training samples increases. This allows the size of the deep neural networks to be consistent with practice and to be several orders of magnitude smaller than that required by the previous theories. Moreover, we prove that the linear increase of the size of the network is the optimal rate and that it cannot be improved, except by a logarithmic factor. Furthermore, deep neural networks with the trainability guarantee are shown to generalize well to unseen test samples with a natural dataset but not a random dataset.
Deep Clustering Based On A Mixture Of Autoencoders

In this paper we propose a Deep Autoencoder Mixture Clustering (DAMIC) algorithm based on a mixture of deep autoencoders where each cluster is represented by an autoencoder. A clustering network transforms the data into another space and then selects one of the clusters. Next, the autoencoder associated with this cluster is used to reconstruct the data-point. The clustering algorithm jointly learns the nonlinear data representation and the set of autoencoders. The optimal clustering is found by minimizing the reconstruction loss of the mixture of autoencoder network. Unlike other deep clustering algorithms, no regularization term is needed to avoid data collapsing to a single point. Our experimental evaluations on image and text corpora show significant improvement over state-of-the-art methods.
Deep-Learning based Lossless Image Coding

This paper proposes a novel approach for lossless image compression. The proposed coding approach employs a deep-learning-based method to compute the prediction for each pixel, and a context-tree-based bit-plane codec to encode the prediction errors. First, a novel deep learning-based predictor is proposed to estimate the residuals produced by traditional prediction methods. It is shown that the use of a deep-learning paradigm substantially boosts the prediction accuracy compared with the traditional prediction methods. Second, the prediction error is modeled by a context modeling method and encoded using a novel context-tree-based bit-plane codec. Codec profiles performing either one or two coding passes are proposed, trading off complexity for compression performance. The experimental evaluation is carried out on three different types of data: photographic images, lenslet images, and video sequences. The experimental results show that the proposed lossless coding approach systematically and substantially outperforms the state-of-the-art methods for each type of data.
Human–machine dialogue modelling with the fusion of word- and sentence-level emotions

Emotion intelligence plays an important role in building a successful human–machine dialogue system. However, the extreme difficulty of capturing emotional information of social text, as well as the weakness of generative models for learning emotional expression, limits the performance of existing dialogue system. Combining dictionary matching and machine learning, this paper proposes a generative model, which fuses the word- and sentence-level emotions, to model the dialogue text and learn emotional expression. The model first obtains the emotional embedding of each word through dictionary matching, then concatenates the emotional word embedding with its traditional word embedding, and the finally formed vector is taken as the input of the encoder. In order to control the emotional feature of the generated response, our model employs a BernoulliNB-based classifier to extract the emotional feature of the post, which is used as the attribute of the original text, and subsequently adds it to the decoder. For further significantly improving the emotional expression of the response, the model leverages a discriminator to constrain the latent variable, which enables the latent variable to encode better the information of emotional feature of the post. With this model, we can generate the dialogue text that is consistent with the original emotion. Experimental results on our Twitter dataset demonstrate that our model outperforms several state-of-the-art methods in the emotion accuracy and quality of generated texts.
A Short-List of Pairing-Friendly Curves Resistant to Special TNFS at the 128-Bit Security Level

There have been notable improvements in discrete logarithm computations in finite fields since 2015 and the introduction of the Tower Number Field Sieve algorithm (TNFS) for extension fields. The Special TNFS is very efficient in finite fields that are target groups of pairings on elliptic curves, where the characteristic is special (e.g. sparse). The key sizes for pairings should be increased, and alternative pairing-friendly curves can be considered. We revisit the Special variant of TNFS for pairing-friendly curves. In this case the characteristic is given by a polynomial of moderate degree (between 4 and 38) and tiny coefficients, evaluated at an integer (a seed). We present a polynomial selection with a new practical trade-off between degree and coefficient size. As a consequence, the security of curves computed by Barbulescu, El Mrabet and Ghammam in 2019 should be revised: we obtain a smaller estimated cost of STNFS for all curves except BLS12 and BN. To obtain TNFS-secure curves, we reconsider the Brezing–Weng generic construction of families of pairing-friendly curves and estimate the cost of our new Special TNFS algorithm for these curves. This improves on the work of Fotiadis and Konstantinou, Fotiadis and Martindale, and Barbulescu, El Mrabet and Ghammam. We obtain a short-list of interesting families of curves that are resistant to the Special TNFS algorithm, of embedding degrees 10 to 16 for the 128-bit security level. We conclude that at the 128-bit security level, BLS-12 and Fotiadis–Konstantinou–Martindale curves with $$k=12$$ over a 440 to 448-bit prime field seem to be the best choice for pairing efficiency. We also give hints at the 192-bit security level.
Improving Bug Triaging with High Confidence Predictions at Ericsson

Correctly assigning bugs to the right developer or team, i.e. bug triaging, is a costly activity. A concerted effort at Ericsson has been done to adopt automated bug triaging to reduce development costs. In this work, we replicate the research approaches that have been widely used in the literature. We apply them on over 10k bug reports for 9 large products at Ericsson. We find that a logistic regression classifier including the simple textual and categorical attributes of the bug reports has the highest precision and recall of 78.09% and 79.00%, respectively. Ericsson's bug reports often contain logs that have crash dumps and alarms. We add this information to the bug triage models. We find that this information does not improve the precision and recall of bug triaging in Ericsson's context. Although our models perform as well as the best ones reported in the literature, a criticism of bug triaging at Ericsson is that the accuracy is not sufficient for regular use. We develop a novel approach where we only triage bugs when the model has high confidence in the triage prediction. We find that we improve the accuracy to 90%, but we can make predictions for 62% of the bug reports.
Unsupervised Adversarial Domain Adaptation Network for Semantic Segmentation

With the rapid development of deep learning technology, semantic segmentation methods have been widely used in remote sensing data. A pretrained semantic segmentation model usually cannot perform well when the testing images (target domain) have an obvious difference from the training data set (source domain), while a large enough labeled data set is almost impossible to be acquired for each scenario. Unsupervised domain adaptation (DA) techniques aim to transfer knowledge learned from the source domain to a totally unlabeled target domain. By reducing the domain shift, DA methods have shown the ability to improve the classification accuracy for the target domain. Hence, in this letter, we propose an unsupervised adversarial DA network that converts deep features into 2-D feature curves and reduces the discrepancy between curves from the source domain and curves from the target domain based on a conditional generative adversarial networks (cGANs) model. Our proposed DA network is able to improve the semantic labeling accuracy when we apply a pretrained semantic segmentation model to the target domain. To test the effectiveness of the proposed method, experiments are conducted on the International Society for Photogrammetry and Remote Sensing (ISPRS) 2-D Semantic Labeling data set. Results show that our proposed network is able to stably improve overall accuracy not only when the source and target domains are from the same city but with different building styles but also when the source and target domains are from different cities and acquired by different sensors. By comparing with a few state-of-the-art DA methods, we demonstrate that our proposed method achieves the best cross-domain semantic segmentation performance.
Toward a Design Space for Mitigating Cognitive Bias in Vis

The use of cognitive heuristics often leads to fast and effective decisions. However, they can also systematically and predictably lead to errors known as cognitive biases. Strategies for minimizing or mitigating these biases, however, remain largely non-technological (e.g., training courses). The growing use of visual analytic (VA) tools for analysis and decision making enables a new class of bias mitigation strategies. In this work, we explore the ways in which the design of visualizations (vis) may be used to mitigate cognitive biases. We derive a design space comprised of 8 dimensions that can be manipulated to impact a user’s cognitive and analytic processes and describe them through an example hiring scenario. This design space can be used to guide and inform future vis systems that may integrate cognitive processes more closely.
Practical Lattice-Based Multisignature Schemes for Blockchains

Compact multisignature is vital for shrinking the signature size of decentralized blockchain.All practical compact multisignature schemes have been constructed from the discrete logarithm problem which is potentially vulnerable to quantum computing attacks.Lattice-based multisignature schemes are potential candidates for resisting quantum attacks.However, the existing lattice-based multisignature schemes suffer either loose signatures or large public key and signature sizes after compressing, which makes them unsuitable for blockchains.In this paper, we first present a practical lattice-based multisignature scheme with much smaller signature sizes than previous lattice-based multisignature schemes.Then, we extend our scheme to support public key aggregation with almost the same performance.Both of our multisignature schemes are provably secure in the random oracle model under the ring version of the short integer solution (Ring-SIS) assumption.They outperform the recent lattice-based multisignature scheme proposed by Bansarkhani and Sturm (BS) in terms of both signature size and communication overhead.
A privacy-preserving approach to prevent feature disclosure in an IoT scenario

In this paper, we propose a privacy-preserving approach to prevent feature disclosure in a multiple IoT scenario, i.e., a scenario where objects can be organized in (partially overlapped) networks interacting with each other. Our approach is based on two notions derived from database theory, namely k-anonymity and t-closeness. They are applied to cluster the involved objects in order to provide a unitary view of them and of their features. Indeed, the use of k-anonymity and t-closeness makes derived groups robust from a privacy perspective. In this way, not only information disclosure, but also feature disclosure, is prevented. This is an important strength of our approach because the malicious analysis of objects’ features can have disruptive effects on the privacy (and, ultimately, on the life) of people.
Functional Regularisation for Continual Learning with Gaussian Processes

We introduce a framework for Continual Learning (CL) based on Bayesian inference over the function space rather than the parameters of a deep neural network. This method, referred to as functional regularisation for Continual Learning, avoids forgetting a previous task by constructing and memorising an approximate posterior belief over the underlying task-specific function. To achieve this we rely on a Gaussian process obtained by treating the weights of the last layer of a neural network as random and Gaussian distributed. Then, the training algorithm sequentially encounters tasks and constructs posterior beliefs over the task-specific functions by using inducing point sparse Gaussian process methods. At each step a new task is first learnt and then a summary is constructed consisting of (i) inducing inputs – a fixed-size subset of the task inputs selected such that it optimally represents the task – and (ii) a posterior distribution over the function values at these inputs. This summary then regularises learning of future tasks, through Kullback-Leibler regularisation terms. Our method thus unites approaches focused on (pseudo-)rehearsal with those derived from a sequential Bayesian inference perspective in a principled way, leading to strong results on accepted benchmarks.