OpenRaiser/Intern-Atlas huggingface dataset:
Explicit constructions of optimal linear codes with Hermitian hulls and their application to quantum codes
Venue: arXiv cs.IT 2021
Year: 2021

We prove that any Hermitian self-orthogonal $[n,k,d]_{q^2}$ code gives rise to an $[n,k,d]_{q^2}$ code with $\ell$ dimensional Hermitian hull for $0\le \ell \le k$. We present a new method to construct Hermitian self-orthogonal $[n,k]_{q^2}$ codes with large dimensions $k>\frac{n+q-1}{q+1}$. New families of Hermitian self-orthogonal codes with good parameters are obtained; more precisely those containing almost MDS codes. By applying a puncturing technique to Hermitian self-orthogonal codes, MDS $[n,k]_{q^2}$ linear codes with Hermitian hull having large dimensions $k>\frac{n+q-1}{q+1}$ are also derived. New families of MDS, almost MDS and optimal codes with arbitrary Hermitian hull dimensions are explicitly constructed from algebraic curves. As an application, we provide entanglement-assisted quantum error correcting codes with new parameters.
Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity
Venue: arXiv cs.LG 2021
Year: 2021

The traditional approach in FL tries to learn a single global model collaboratively with the help of many clients under the orchestration of a central server. However, learning a single global model might not work well for all clients participating in the FL under data heterogeneity. Therefore, the personalization of the global model becomes crucial in handling the challenges that arise with statistical heterogeneity and the non-IID distribution of data. Unlike prior works, in this work we propose a new approach for obtaining a personalized model from a client-level objective. This further motivates all clients to participate in federation even under statistical heterogeneity in order to improve their performance, instead of merely being a source of data and model training for the central server. To realize this personalization, we leverage finding a small subnetwork for each client by applying hybrid pruning (combination of structured and unstructured pruning), and unstructured pruning. Through a range of experiments on different benchmarks, we observed that the clients with similar data (labels) share similar personal parameters. By finding a subnetwork for each client ...
Sphynx: a parallel multi-GPU graph partitioner for distributed-memory systems
Venue: arXiv cs.DC 2021
Year: 2021

Graph partitioning has been an important tool to partition the work among several processors to minimize the communication cost and balance the workload. While accelerator-based supercomputers are emerging to be the standard, the use of graph partitioning becomes even more important as applications are rapidly moving to these architectures. However, there is no distributed-memory parallel, multi-GPU graph partitioner available for applications. We developed a spectral graph partitioner, Sphynx, using the portable, accelerator-friendly stack of the Trilinos framework. In Sphynx, we allow using different preconditioners and exploit their unique advantages. We use Sphynx to systematically evaluate the various algorithmic choices in spectral partitioning with a focus on the GPU performance. We perform those evaluations on two distinct classes of graphs: regular (such as meshes, matrices from finite element methods) and irregular (such as social networks and web graphs), and show that different settings and preconditioners are needed for these graph classes. The experimental results on the Summit supercomputer show that Sphynx is the fastest alternative on irregular graphs in an application-friendly setting and obtains a partitioning quality close to ParMETIS on regular graphs. When compared to nvGRAPH on a single GPU, Sphynx is faster and obtains better balance and better quality partitions. Sphynx provides a good and robust partitioning method across a wide range of graphs for applications looking for a GPU-based partitioner.
An End-to-End and Accurate PPG-based Respiratory Rate Estimation Approach Using Cycle Generative Adversarial Networks
Venue: arXiv cs.LG 2021
Year: 2021

Respiratory rate (RR) is a clinical sign representing ventilation. An abnormal change in RR is often the first sign of health deterioration as the body attempts to maintain oxygen delivery to its tissues. There has been a growing interest in remotely monitoring of RR in everyday settings which has made photoplethysmography (PPG) monitoring wearable devices an attractive choice. PPG signals are useful sources for RR extraction due to the presence of respiration-induced modulations in them. The existing PPG-based RR estimation methods mainly rely on hand-crafted rules and manual parameters tuning. An end-to-end deep learning approach was recently proposed, however, despite its automatic nature, the performance of this method is not ideal using the real world data. In this paper, we present an end-to-end and accurate pipeline for RR estimation using Cycle Generative Adversarial Networks (CycleGAN) to reconstruct respiratory signals from raw PPG signals. Our results demonstrate a higher RR estimation accuracy of up to 2$\times$ (mean absolute error of 1.9$\pm$0.3 using five fold cross validation) compared to the state-of-th-art using a identical publicly available dataset. Our results suggest that CycleGAN can be a valuable method for RR estimation from raw PPG signals.
Model Multiplicity (UML) Versus Model Singularity in System Requirements and Design
Venue: arXiv cs.SE 2021
Year: 2021

A conceptual model can be used to manage complexity in both the design and implementation phases of the system development life cycle. Such a model requires a firm grasp of the abstract principles on which a system is based, as well as an understanding of the high-level nature of the representation of entities and processes. In this context, models can have distinct architectural characteristics. This paper discusses model multiplicity (e.g., unified modeling language [UML]), model singularity (e.g., object-process methodology [OPM], thinging machine [TM]), and a heterogeneous model that involves multiplicity and singularity. The basic idea of model multiplicity is that it is not possible to present all views in a single representation, so a number of models are used, with each model representing a different view. The model singularity approach uses only a single unified model that assimilates its subsystems into one system. This paper is concerned with current approaches, especially in software engineering texts, where multimodal UML is introduced as the general-purpose modeling language (i.e., UML is modeling). In such a situation, we suggest raising the issue of multiplicity versus singularity in modeling. This would foster a basic appreciation of the UML advantages and difficulties that may be faced during modeling, especially in the educational setting. Furthermore, we advocate the claim that a multiplicity of views does not necessitate a multiplicity of models. The model singularity approach can represent multiple views (static, behavior) without resorting to a collection of multiple models with various notations. We present an example of such a model where the static representation is developed first. Then, the dynamic view and behavioral representations are built by incorporating a decomposition strategy interleaved with the notion of time.
Schema-Aware Deep Graph Convolutional Networks for Heterogeneous Graphs
Venue: arXiv cs.LG 2021
Year: 2021

Graph convolutional network (GCN) based approaches have achieved significant progress for solving complex, graph-structured problems. GCNs incorporate the graph structure information and the node (or edge) features through message passing and computes 'deep' node representations. Despite significant progress in the field, designing GCN architectures for heterogeneous graphs still remains an open challenge. Due to the schema of a heterogeneous graph, useful information may reside multiple hops away. A key question is how to perform message passing to incorporate information of neighbors multiple hops away while avoiding the well-known over-smoothing problem in GCNs. To address this question, we propose our GCN framework 'Deep Heterogeneous Graph Convolutional Network (DHGCN)', which takes advantage of the schema of a heterogeneous graph and uses a hierarchical approach to effectively utilize information many hops away. It first computes representations of the target nodes based on their 'schema-derived ego-network' (SEN). It then links the nodes of the same type with various pre-defined metapaths and performs message passing along these links to compute final node representations. Our design choices naturally capture the way a heterogeneous graph is generated from the schema. The experimental results on real and synthetic datasets corroborate the design choice and illustrate the performance gains relative to competing alternatives.
Bias in Knowledge Graphs -- an Empirical Study with Movie Recommendation and Different Language Editions of DBpedia
Venue: arXiv cs.IR 2021
Year: 2021

Public knowledge graphs such as DBpedia and Wikidata have been recognized as interesting sources of background knowledge to build content-based recommender systems. They can be used to add information about the items to be recommended and links between those. While quite a few approaches for exploiting knowledge graphs have been proposed, most of them aim at optimizing the recommendation strategy while using a fixed knowledge graph. In this paper, we take a different approach, i.e., we fix the recommendation strategy and observe changes when using different underlying knowledge graphs. Particularly, we use different language editions of DBpedia. We show that the usage of different knowledge graphs does not only lead to differently biased recommender systems, but also to recommender systems that differ in performance for particular fields of recommendations.
Ensemble Feature Extraction for Multi-Container Quality-Diversity Algorithms
Venue: arXiv cs.NE 2021
Year: 2021

Quality-Diversity algorithms search for large collections of diverse and high-performing solutions, rather than just for a single solution like typical optimisation methods. They are specially adapted for multi-modal problems that can be solved in many different ways, such as complex reinforcement learning or robotics tasks. However, these approaches are highly dependent on the choice of feature descriptors (FDs) quantifying the similarity in behaviour of the solutions. While FDs usually needs to be hand-designed, recent studies have proposed ways to define them automatically by using feature extraction techniques, such as PCA or Auto-Encoders, to learn a representation of the problem from previously explored solutions. Here, we extend these approaches to more complex problems which cannot be efficiently explored by relying only on a single representation but require instead a set of diverse and complementary representations. We describe MC-AURORA, a Quality-Diversity approach that optimises simultaneously several collections of solutions, each with a different set of FDs, which are, in turn, defined automatically by an ensemble of modular auto-encoders. We show that this approach produces solutions that are more diverse than those produced by single-representation approaches.
Multi-modal Bifurcated Network for Depth Guided Image Relighting
Venue: arXiv cs.CV 2021
Year: 2021

Image relighting aims to recalibrate the illumination setting in an image. In this paper, we propose a deep learning-based method called multi-modal bifurcated network (MBNet) for depth guided image relighting. That is, given an image and the corresponding depth maps, a new image with the given illuminant angle and color temperature is generated by our network. This model extracts the image and the depth features by the bifurcated network in the encoder. To use the two features effectively, we adopt the dynamic dilated pyramid modules in the decoder. Moreover, to increase the variety of training data, we propose a novel data process pipeline to increase the number of the training data. Experiments conducted on the VIDIT dataset show that the proposed solution obtains the \textbf{1}$^{st}$ place in terms of SSIM and PMS in the NTIRE 2021 Depth Guide One-to-one Relighting Challenge.
Comparison Analysis of Facebook's Prophet, Amazon's DeepAR+ and CNN-QR Algorithms for Successful Real-World Sales Forecasting
Venue: arXiv cs.AI 2021
Year: 2021

By successfully solving the problem of forecasting, the processes in the work of various companies are optimized and savings are achieved. In this process, the analysis of time series data is of particular importance. Since the creation of Facebook's Prophet, and Amazon's DeepAR+ and CNN-QR forecasting models, algorithms have attracted a great deal of attention. The paper presents the application and comparison of the above algorithms for sales forecasting in distribution companies. A detailed comparison of the performance of algorithms over real data with different lengths of sales history was made. The results show that Prophet gives better results for items with a longer history and frequent sales, while Amazon's algorithms show superiority for items without a long history and items that are rarely sold.
Synchronised Visualisation of Software Process and Product Artefacts: Concept, Design and Prototype Implementation
Venue: arXiv cs.SE 2021
Year: 2021

Context: Most prior software visualisation (SV) research has focused primarily on making aspects of intangible software product artefacts more evident. While undoubtedly useful, this focus has meant that software process visualisation has received far less attention. Objective: This paper presents Conceptual Visualisation, a novel SV approach that builds on the well-known CodeCity metaphor by situating software code artefacts alongside their software development processes, in order to link and synchronise these typically separate components. Method: While the majority of prior SV research has focused on representing what is already available in the code (i.e., the implementation) or information derived from it (i.e., various metrics), the presented approach instead makes the design concepts and original developers' intentions -- both significant sources of information in terms of software development and maintenance -- readily and contextually available in a visualisation environment that tightly integrates the code artefacts with their original functional requirements and development activity. Results: Our approach has been implemented in a prototype tool called ScrumCity with the proof of concept being demonstrated using six real-world open source systems. A preliminary case study has further been carried out with real world data. Conclusion: Conceptual Visualisation, as implemented in ScrumCity, shows early promise in enabling developers and managers (and potentially other stakeholders) to traverse and explore multiple aspects of software product and process artefacts in a synchronised manner, achieving traceability between the two.
Stream Compression of DLMS Smart Meter Readings
Venue: arXiv cs.NI 2021
Year: 2021

Smart electricity meters typically upload readings a few times a day. Utility providers aim to increase the upload frequency in order to access consumption information in near real time, but the legacy compressors fail to provide sufficient savings on the low-bandwidth, high-cost data connection. We propose a new compression method and data format for DLMS smart meter readings, which is significantly better with frequent uploads and enable reporting every reading in near real time with the same or lower data sizes than the currently available compressors in the DLMS protocol.
MLCheck- Property-Driven Testing of Machine Learning Models
Venue: arXiv cs.SE 2021
Year: 2021

In recent years, we observe an increasing amount of software with machine learning components being deployed. This poses the question of quality assurance for such components: how can we validate whether specified requirements are fulfilled by a machine learned software? Current testing and verification approaches either focus on a single requirement (e.g., fairness) or specialize on a single type of machine learning model (e.g., neural networks). In this paper, we propose property-driven testing of machine learning models. Our approach MLCheck encompasses (1) a language for property specification, and (2) a technique for systematic test case generation. The specification language is comparable to property-based testing languages. Test case generation employs advanced verification technology for a systematic, property-dependent construction of test suites, without additional user-supplied generator functions. We evaluate MLCheck using requirements and data sets from three different application areas (software discrimination, learning on knowledge graphs and security). Our evaluation shows that despite its generality MLCheck can even outperform specialised testing approaches while having a comparable runtime.
Computational Two-Party Correlation: A Dichotomy for Key-Agreement Protocols
Venue: arXiv cs.CR 2021
Year: 2021

Let $π$ be an efficient two-party protocol that given security parameter $κ$, both parties output single bits $X_κ$ and $Y_κ$, respectively. We are interested in how $(X_κ,Y_κ)$ "appears" to an efficient adversary that only views the transcript $T_κ$. We make the following contributions: $\bullet$ We develop new tools to argue about this loose notion and show (modulo some caveats) that for every such protocol $π$, there exists an efficient simulator such that the following holds: on input $T_κ$, the simulator outputs a pair $(X'_κ,Y'_κ)$ such that $(X'_κ,Y'_κ,T_κ)$ is (somewhat) computationally indistinguishable from $(X_κ,Y_κ,T_κ)$. $\bullet$ We use these tools to prove the following dichotomy theorem: every such protocol $π$ is: - either uncorrelated -- it is (somewhat) indistinguishable from an efficient protocol whose parties interact to produce $T_κ$, but then choose their outputs independently from some product distribution (that is determined in poly-time from $T_κ$), - or, the protocol implies a key-agreement protocol (for infinitely many $κ$'s). Uncorrelated protocols are uninteresting from a cryptographic viewpoint, as the correlation between outputs is (computationally) trivial. Our dichotomy shows that every protocol is either completely uninteresting or implies key-agreement. $\bullet$ We use the above dichotomy to make progress on open problems on minimal cryptographic assumptions required for differentially private mechanisms for the XOR function. $\bullet$ A subsequent work of Haitner et al. uses the above dichotomy to makes progress on a longstanding open question regarding the complexity of fair two-party coin-flipping protocols.
Channels of Small Log-Ratio Leakage and Characterization of Two-Party Differentially Private Computation
Venue: arXiv cs.CC 2021
Year: 2021

Consider a PPT two-party protocol $π=(A,B)$ in which the parties get no private inputs and obtain outputs $O^A,O^B\in \{0,1\}$, and let $V^A$ and $V^B$ denote the parties' individual views. Protocol $π$ has $α$-agreement if $Pr[O^A=O^B]=1/2+α$. The leakage of $π$ is the amount of information a party obtains about the event $\{O^A=O^B\}$; that is, the leakage $ε$ is the maximum, over $P\in\{A,B\}$, of the distance between $V^P|OA=OB$ and $V^P|OA\neq OB$. Typically, this distance is measured in statistical distance, or, in the computational setting, in computational indistinguishability. For this choice, Wullschleger [TCC 09] showed that if $α>>ε$ then the protocol can be transformed into an OT protocol. We consider measuring the protocol leakage by the log-ratio distance (which was popularized by its use in the differential privacy framework). The log-ratio distance between X,Y over domain Ωis the minimal $ε>0$ for which, for every $v\inΩ$, $log(Pr[X=v]/Pr[Y=v])\in [-ε,ε]$. In the computational setting, we use computational indistinguishability from having log-ratio distance $ε$. We show that a protocol with (noticeable) accuracy $α\inΩ(ε^2)$ can be transformed into an OT protocol (note that this allows $ε>>α$). We complete the picture, in this respect, showing that a protocol with $α\in o(ε^2)$ does not necessarily imply OT. Our results hold for both the information theoretic and the computational settings, and can be viewed as a "fine grained" approach to "weak OT amplification". We then use the above result to fully characterize the complexity of differentially private two-party computation for the XOR function, answering the open question put by Goyal, Khurana, Mironov, Pandey, and Sahai [ICALP 16] and Haitner, Nissim, Omri, Shaltiel, and Silbak [FOCS 18].
DeepMPCVS: Deep Model Predictive Control for Visual Servoing
Venue: arXiv cs.RO 2021
Year: 2021

The simplicity of the visual servoing approach makes it an attractive option for tasks dealing with vision-based control of robots in many real-world applications. However, attaining precise alignment for unseen environments pose a challenge to existing visual servoing approaches. While classical approaches assume a perfect world, the recent data-driven approaches face issues when generalizing to novel environments. In this paper, we aim to combine the best of both worlds. We present a deep model predictive visual servoing framework that can achieve precise alignment with optimal trajectories and can generalize to novel environments. Our framework consists of a deep network for optical flow predictions, which are used along with a predictive model to forecast future optical flow. For generating an optimal set of velocities we present a control network that can be trained on the fly without any supervision. Through extensive simulations on photo-realistic indoor settings of the popular Habitat framework, we show significant performance gain due to the proposed formulation vis-a-vis recent state-of-the-art methods. Specifically, we show a faster convergence and an improved performance in trajectory length over recent approaches.
WhatTheWikiFact: Fact-Checking Claims Against Wikipedia
Venue: arXiv cs.CL 2021
Year: 2021

The rise of Internet has made it a major source of information. Unfortunately, not all information online is true, and thus a number of fact-checking initiatives have been launched, both manual and automatic, to deal with the problem. Here, we present our contribution in this regard: \emph{WhatTheWikiFact}, a system for automatic claim verification using Wikipedia. The system can predict the veracity of an input claim, and it further shows the evidence it has retrieved as part of the verification process. It shows confidence scores and a list of relevant Wikipedia articles, together with detailed information about each article, including the phrase used to retrieve it, the most relevant sentences extracted from it and their stance with respect to the input claim, as well as the associated probabilities. The system supports several languages: Bulgarian, English, and Russian.
Wireless Federated Learning (WFL) for 6G Networks -- Part I: Research Challenges and Future Trends
Venue: arXiv cs.DC 2021
Year: 2021

Conventional machine learning techniques are conducted in a centralized manner. Recently, the massive volume of generated wireless data, the privacy concerns and the increasing computing capabilities of wireless end-devices have led to the emergence of a promising decentralized solution, termed as Wireless Federated Learning (WFL). In this first of the two parts paper, we present the application of WFL in the sixth generation of wireless networks (6G), which is envisioned to be an integrated communication and computing platform. After analyzing the key concepts of WFL, we discuss the core challenges of WFL imposed by the wireless (or mobile communication) environment. Finally, we shed light to the future directions of WFL, aiming to compose a constructive integration of FL into the future wireless networks.