OpenRaiser/Intern-Atlas huggingface dataset:
Delayed approximate matrix assembly in multigrid with dynamic precisions
Venue: arXiv cs.MS 2020
Year: 2020

The accurate assembly of the system matrix is an important step in any code that solves partial differential equations on a mesh. We either explicitly set up a matrix, or we work in a matrix-free environment where we have to be able to quickly return matrix entries upon demand. Either way, the construction can become costly due to non-trivial material parameters entering the equations, multigrid codes requiring cascades of matrices that depend upon each other, or dynamic adaptive mesh refinement that necessitates the recomputation of matrix entries or the whole equation system throughout the solve. We propose that these constructions can be performed concurrently with the multigrid cycles. Initial geometric matrices and low accuracy integrations kickstart the multigrid, while improved assembly data is fed to the solver as and when it becomes available. The time to solution is improved as we eliminate an expensive preparation phase traditionally delaying the actual computation. We eliminate algorithmic latency. Furthermore, we desynchronise the assembly from the solution process. This anarchic increase of the concurrency level improves the scalability. Assembly routines are notoriously memory- and bandwidth-demanding. As we work with iteratively improving operator accuracies, we finally propose the use of a hierarchical, lossy compression scheme such that the memory footprint is brought down aggressively where the system matrix entries carry little information or are not yet available with high accuracy.
LIIR at SemEval-2020 Task 12: A Cross-Lingual Augmentation Approach for Multilingual Offensive Language Identification
Venue: arXiv cs.CL 2020
Year: 2020

This paper presents our system entitled `LIIR' for SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval 2). We have participated in sub-task A for English, Danish, Greek, Arabic, and Turkish languages. We adapt and fine-tune the BERT and Multilingual Bert models made available by Google AI for English and non-English languages respectively. For the English language, we use a combination of two fine-tuned BERT models. For other languages we propose a cross-lingual augmentation approach in order to enrich training data and we use Multilingual BERT to obtain sentence representations. LIIR achieved rank 14/38, 18/47, 24/86, 24/54, and 25/40 in Greek, Turkish, English, Arabic, and Danish languages, respectively.
Détermination Automatique des Fonctions d'Appartenance et Interrogation Flexible et Coopérative des Bases de Données
Venue: arXiv cs.DB 2020
Year: 2020

Flexible querying of DB allows to extend DBMS in order to support imprecision and flexibility in queries. Flexible queries use vague and imprecise terms which have been defined as fuzzy sets. However, there is no consensus on memberships functions generation. Most of the proposed methods require expert intervention. This thesis is devised in two parts. In the first part, we propose a clustering based approach for automatic and incremental membership functions generation. We have proposed the clustering method CLUSTERDB* which evaluates clustering quality underway clusters generation. Moreover, we propose incremental updates of partitions and membership functions after insertion or deletion of a new object. The second part of this thesis uses these functions and Formal Concepts Analysis in flexible and cooperative querying. In case of empty answers, we formally detect the failure reasons and we generate approximative queries with their answers. These queries help the user to formulate new queries having answers. The different proposed approaches are implemented and experimented with several databases. The experimentation results are encouraging.
A Monte Carlo Approach to Closing the Reality Gap
Venue: arXiv cs.RO 2020
Year: 2020

We propose a novel approach to the 'reality gap' problem, i.e., modifying a robot simulation so that its performance becomes more similar to observed real world phenomena. This problem arises whether the simulation is being used by human designers or in an automated policy development mechanism. We expect that the program/policy is developed using simulation, and subsequently deployed on a real system. We further assume that the program includes a monitor procedure with scalar output to determine when it is achieving its performance objectives. The proposed approach collects simulation and real world observations and builds conditional probability functions. These are used to generate paired roll-outs to identify points of divergence in behavior. These are used to generate {\it state-space kernels} that coerce the simulation into behaving more like observed reality. The method was evaluated using ROS/Gazebo for simulation and a heavily modified Traaxas platform in outdoor deployment. The results support not just that the kernel approach can force the simulation to behave more like reality, but that the modification is such that an improved control policy tested in the modified simulation also performs better in the real world.
Access Points in the Air: Modeling and Optimization of Fixed-Wing UAV Network
Venue: arXiv cs.IT 2020
Year: 2020

Fixed-wing unmanned aerial vehicles (UAVs) are of great potential to serve as aerial access points (APs) owing to better aerodynamic performance and longer flight endurance. However, the inherent hovering feature of fixed-wing UAVs may result in discontinuity of connections and frequent handover of ground users (GUs). In this work, we model and evaluate the performance of a fixed-wing UAV network, where UAV APs provide coverage to GUs with millimeter wave backhaul. Firstly, it reveals that network spatial throughput (ST) is independent of the hover radius under real-time closest-UAV association, while linearly decreases with the hover radius if GUs are associated with the UAVs, whose hover center is the closest. Secondly, network ST is shown to be greatly degraded with the over-deployment of UAV APs due to the growing air-to-ground interference under excessive overlap of UAV cells. Finally, aiming to alleviate the interference, a projection area equivalence (PAE) rule is designed to tune the UAV beamwidth. Especially, network ST can be sustainably increased with growing UAV density and independent of UAV flight altitude if UAV beamwidth inversely grows with the square of UAV density under PAE.
Learning Generalized Spoof Cues for Face Anti-spoofing
Venue: arXiv cs.CV 2020
Year: 2020

Many existing face anti-spoofing (FAS) methods focus on modeling the decision boundaries for some predefined spoof types. However, the diversity of the spoof samples including the unknown ones hinders the effective decision boundary modeling and leads to weak generalization capability. In this paper, we reformulate FAS in an anomaly detection perspective and propose a residual-learning framework to learn the discriminative live-spoof differences which are defined as the spoof cues. The proposed framework consists of a spoof cue generator and an auxiliary classifier. The generator minimizes the spoof cues of live samples while imposes no explicit constraint on those of spoof samples to generalize well to unseen attacks. In this way, anomaly detection is implicitly used to guide spoof cue generation, leading to discriminative feature learning. The auxiliary classifier serves as a spoof cue amplifier and makes the spoof cues more discriminative. We conduct extensive experiments and the experimental results show the proposed method consistently outperforms the state-of-the-art methods. The code will be publicly available at https://github.com/vis-var/lgsc-for-fas.
Context-Sensitive Generation Network for Handing Unknown Slot Values in Dialogue State Tracking
Venue: arXiv cs.CL 2020
Year: 2020

As a key component in a dialogue system, dialogue state tracking plays an important role. It is very important for dialogue state tracking to deal with the problem of unknown slot values. As far as we known, almost all existing approaches depend on pointer network to solve the unknown slot value problem. These pointer network-based methods usually have a hidden assumption that there is at most one out-of-vocabulary word in an unknown slot value because of the character of a pointer network. However, often, there are multiple out-of-vocabulary words in an unknown slot value, and it makes the existing methods perform bad. To tackle the problem, in this paper, we propose a novel Context-Sensitive Generation network (CSG) which can facilitate the representation of out-of-vocabulary words when generating the unknown slot value. Extensive experiments show that our proposed method performs better than the state-of-the-art baselines.
Learning to Detect Unacceptable Machine Translations for Downstream Tasks
Venue: arXiv cs.CL 2020
Year: 2020

The field of machine translation has progressed tremendously in recent years. Even though the translation quality has improved significantly, current systems are still unable to produce uniformly acceptable machine translations for the variety of possible use cases. In this work, we put machine translation in a cross-lingual pipeline and introduce downstream tasks to define task-specific acceptability of machine translations. This allows us to leverage parallel data to automatically generate acceptability annotations on a large scale, which in turn help to learn acceptability detectors for the downstream tasks. We conduct experiments to demonstrate the effectiveness of our framework for a range of downstream tasks and translation models.
Inferring the Spatial Distribution of Physical Activity in Children Population from Characteristics of the Environment
Venue: arXiv cs.CY 2020
Year: 2020

Obesity affects a rising percentage of the children and adolescent population, contributing to decreased quality of life and increased risk for comorbidities. Although the major causes of obesity are known, the obesogenic behaviors manifest as a result of complex interactions of the individual with the living environment. For this reason, addressing childhood obesity remains a challenging problem for public health authorities. The BigO project (https://bigoprogram.eu) relies on large-scale behavioral and environmental data collection to create tools that support policy making and intervention design. In this work, we propose a novel analysis approach for modeling the expected population behavior as a function of the local environment. We experimentally evaluate this approach in predicting the expected physical activity level in small geographic regions using urban environment characteristics. Experiments on data collected from 156 children and adolescents verify the potential of the proposed approach. Specifically, we train models that predict the physical activity level in a region, achieving 81% leave-one-out accuracy. In addition, we exploit the model predictions to automatically visualize heatmaps of the expected population behavior in areas of interest, from which we draw useful insights. Overall, the predictive models and the automatic heatmaps are promising tools in gaining direct perception for the spatial distribution of the population's behavior, with potential uses by public health authorities.
Detecting sudden and gradual drifts in business processes from execution traces
Venue: arXiv cs.AI 2020
Year: 2020

Business processes are prone to unexpected changes, as process workers may suddenly or gradually start executing a process differently in order to adjust to changes in workload, season, or other external factors. Early detection of business process changes enables managers to identify and act upon changes that may otherwise affect process performance. Business process drift detection refers to a family of methods to detect changes in a business process by analyzing event logs extracted from the systems that support the execution of the process. Existing methods for business process drift detection are based on an explorative analysis of a potentially large feature space and in some cases they require users to manually identify specific features that characterize the drift. Depending on the explored feature space, these methods miss various types of changes. Moreover, they are either designed to detect sudden drifts or gradual drifts but not both. This paper proposes an automated and statistically grounded method for detecting sudden and gradual business process drifts under a unified framework. An empirical evaluation shows that the method detects typical change patterns with significantly higher accuracy and lower detection delay than existing methods, while accurately distinguishing between sudden and gradual drifts.
Convergence of IT and SCADA: Associated Security Threats and Vulnerabilities
Venue: arXiv cs.CR 2020
Year: 2020

As many industries shift towards centralised controlled information systems for monitoring and control, more importance is being placed upon technologies such as Supervisory Control and Data Acquisitions industrial systems (SCADA). This focus on integration and interoperability presents numerous challenges for security personnel and organisational management alike. It becomes paramount therefore to reciprocate this new direction within an organisation with adequate plans and frameworks that ensure protection and security of its SCADA architecture. A clear understanding of the relevant threats and vulnerabilities is critical for adopting/developing appropriate policy and frameworks. To this end, in this research we identify and analyse relevant SCADA security threats and vulnerabilities and present a simple scheme to classify them for better understanding.
Fast Automatic Visibility Optimization for Thermal Synthetic Aperture Visualization
Venue: arXiv cs.CV 2020
Year: 2020

In this article, we describe and validate the first fully automatic parameter optimization for thermal synthetic aperture visualization. It replaces previous manual exploration of the parameter space, which is time consuming and error prone. We prove that the visibility of targets in thermal integral images is proportional to the variance of the targets' image. Since this is invariant to occlusion it represents a suitable objective function for optimization. Our findings have the potential to enable fully autonomous search and recuse operations with camera drones.
Inference Time Optimization Using BranchyNet Partitioning
Venue: arXiv cs.DC 2020
Year: 2020

Deep Neural Network (DNN) applications with edge computing presents a trade-off between responsiveness and computational resources. On one hand, edge computing can provide high responsiveness deploying computational resources close to end devices, which may be prohibitive for the majority of cloud computing services. On the other hand, DNN inference requires computational power to be executed, which may not be available on edge devices, but a cloud server can provide it. To solve this problem (trade-off), we partition a DNN between edge device and cloud server, which means the first DNN layers are processed at the edge and the other layers at the cloud. This paper proposes an optimal partition of DNN, according to network bandwidth, computational resources of edge and cloud, and parameter inherent to data. Our proposal aims to minimize the inference time, to allow high responsiveness applications. To this end, we show the equivalency between DNN partitioning problem and shortest path problem to find an optimal solution, using Dijkstra's algorithm.
Comparison of Evolving Granular Classifiers applied to Anomaly Detection for Predictive Maintenance in Computing Centers
Venue: arXiv cs.NE 2020
Year: 2020

Log-based predictive maintenance of computing centers is a main concern regarding the worldwide computing grid that supports the CERN (European Organization for Nuclear Research) physics experiments. A log, as event-oriented adhoc information, is quite often given as unstructured big data. Log data processing is a time-consuming computational task. The goal is to grab essential information from a continuously changeable grid environment to construct a classification model. Evolving granular classifiers are suited to learn from time-varying log streams and, therefore, perform online classification of the severity of anomalies. We formulated a 4-class online anomaly classification problem, and employed time windows between landmarks and two granular computing methods, namely, Fuzzy-set-Based evolving Modeling (FBeM) and evolving Granular Neural Network (eGNN), to model and monitor logging activity rate. The results of classification are of utmost importance for predictive maintenance because priority can be given to specific time intervals in which the classifier indicates the existence of high or medium severity anomalies.
On Designing Secure and Robust Scan Chain for Protecting Obfuscated Logic
Venue: arXiv cs.CR 2020
Year: 2020

In this paper, we assess the security and testability of the state-of-the-art design-for-security (DFS) architectures in the presence of scan-chain locking/obfuscation, a group of solution that has previously proposed to restrict unauthorized access to the scan chain. We discuss the key leakage vulnerability in the recently published prior-art DFS architectures. This leakage relies on the potential glitches in the DFS architecture that could lead the adversary to make a leakage condition in the circuit. Also, we demonstrate that the state-of-the-art DFS architectures impose some substantial architectural drawbacks that moderately affect both test flow and design constraints. We propose a new DFS architecture for building a secure scan chain architecture while addressing the potential of key leakage. The proposed architecture allows the designer to perform the structural test with no limitation, enabling an untrusted foundry to utilize the scan chain for manufacturing fault testing without needing to access the scan chain. Our proposed solution poses negligible limitation/overhead on the test flow, as well as the design criteria.
Knowledge Patterns
Venue: arXiv cs.AI 2020
Year: 2020

This paper describes a new technique, called "knowledge patterns", for helping construct axiom-rich, formal ontologies, based on identifying and explicitly representing recurring patterns of knowledge (theory schemata) in the ontology, and then stating how those patterns map onto domain-specific concepts in the ontology. From a modeling perspective, knowledge patterns provide an important insight into the structure of a formal ontology: rather than viewing a formal ontology simply as a list of terms and axioms, knowledge patterns views it as a collection of abstract, modular theories (the "knowledge patterns") plus a collection of modeling decisions stating how different aspects of the world can be modeled using those theories. Knowledge patterns make both those abstract theories and their mappings to the domain of interest explicit, thus making modeling decisions clear, and avoiding some of the ontological confusion that can otherwise arise. In addition, from a computational perspective, knowledge patterns provide a simple and computationally efficient mechanism for facilitating knowledge reuse. We describe the technique and an application built using them, and then critique its strengths and weaknesses. We conclude that this technique enables us to better explicate both the structure and modeling decisions made when constructing a formal axiom-rich ontology.
Probing Linguistic Systematicity
Venue: arXiv cs.CL 2020
Year: 2020

Recently, there has been much interest in the question of whether deep natural language understanding models exhibit systematicity; generalizing such that units like words make consistent contributions to the meaning of the sentences in which they appear. There is accumulating evidence that neural models often generalize non-systematically. We examined the notion of systematicity from a linguistic perspective, defining a set of probes and a set of metrics to measure systematic behaviour. We also identified ways in which network architectures can generalize non-systematically, and discuss why such forms of generalization may be unsatisfying. As a case study, we performed a series of experiments in the setting of natural language inference (NLI), demonstrating that some NLU systems achieve high overall performance despite being non-systematic.
Transforming task representations to perform novel tasks
Venue: arXiv cs.LG 2020
Year: 2020

An important aspect of intelligence is the ability to adapt to a novel task without any direct experience (zero-shot), based on its relationship to previous tasks. Humans can exhibit this cognitive flexibility. By contrast, models that achieve superhuman performance in specific tasks often fail to adapt to even slight task alterations. To address this, we propose a general computational framework for adapting to novel tasks based on their relationship to prior tasks. We begin by learning vector representations of tasks. To adapt to new tasks, we propose meta-mappings, higher-order tasks that transform basic task representations. We demonstrate the effectiveness of this framework across a wide variety of tasks and computational paradigms, ranging from regression to image classification and reinforcement learning. We compare to both human adaptability and language-based approaches to zero-shot learning. Across these domains, meta-mapping is successful, often achieving 80-90% performance, without any data, on a novel task, even when the new task directly contradicts prior experience. We further show that meta-mapping can not only generalize to new tasks via learned relationships, but can also generalize using novel relationships unseen during training. Finally, using meta-mapping as a starting point can dramatically accelerate later learning on a new task, and reduce learning time and cumulative error substantially. Our results provide insight into a possible computational basis of intelligent adaptability and offer a possible framework for modeling cognitive flexibility and building more flexible artificial intelligence systems.
A Social Search Model for Large Scale Social Networks
Venue: arXiv cs.IR 2020
Year: 2020

With the rise of social networks, information on the internet is no longer solely organized by web pages. Rather, content is generated and shared among users and organized around their social relations on social networks. This presents new challenges to information retrieval systems. On a social network search system, the generation of result sets not only needs to consider keyword matches, like a traditional web search engine does, but it also needs to take into account the searcher's social connections and the content's visibility settings. Besides, search ranking should be able to handle both textual relevance and the rich social interaction signals from the social network. In this paper, we present our solution to these two challenges by first introducing a social retrieval mechanism, and then investigate novel deep neural networks for the ranking problem. The retrieval system treats social connections as indexing terms, and generates meaningful results sets by biasing towards close social connections in a constrained optimization fashion. The result set is then ranked by a deep neural network that handles textual and social relevance in a two-tower approach, in which personalization and textual relevance are addressed jointly. The retrieval mechanism is deployed on Facebook and is helping billions of users finding postings from their connections efficiently. Based on the postings being retrieved, we evaluate our two-tower neutral network, and examine the importance of personalization and textual signals in the ranking problem.