OpenRaiser/Intern-Atlas huggingface dataset:
Robot Motion Risk Reasoning Framework
Venue: arXiv cs.RO 2019
Year: 2019

This paper presents a formal and comprehensive reasoning framework for robot motion risk, with a focus on locomotion in challenging unstructured or confined environments. Risk which locomoting robots face in physical spaces was not formally defined in the robotics literature. Safety or risk concerns were addressed in an ad hoc fashion, depending only on the specific application of interest. Without a formal definition, certain properties of risk were simply assumed but ill-supported, such as additivity or being Markovian. The only contributing adverse effect being considered is related with obstacles. This work proposes a formal definition of robot motion risk using propositional logic and probability theory. It presents a universe of risk elements within three major risk categories and unifies them into one single metric. True properties of risk are revealed with formal reasoning, such as non-additivity or history-dependency. Risk representation which encompasses risk effects from both temporal and spatial domain is presented. The resulted risk framework provides a formal approach to reason about robot motion risk. Safety of robot locomotion could be explicitly reasoned, quantified, and compared. It could be used for risk-aware planning and reasoning by both human and robotic agents.
Harnessing the Power of Deep Learning Methods in Healthcare: Neonatal Pain Assessment from Crying Sound
Venue: arXiv cs.CV 2019
Year: 2019

Neonatal pain assessment in clinical environments is challenging as it is discontinuous and biased. Facial/body occlusion can occur in such settings due to clinical condition, developmental delays, prone position, or other external factors. In such cases, crying sound can be used to effectively assess neonatal pain. In this paper, we investigate the use of a novel CNN architecture (N-CNN) along with other CNN architectures (VGG16 and ResNet50) for assessing pain from crying sounds of neonates. The experimental results demonstrate that using our novel N-CNN for assessing pain from the sounds of neonates has a strong clinical potential and provides a viable alternative to the current assessment practice.
DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

The increasing inclusion of Deep Learning (DL) models in safety-critical systems such as autonomous vehicles have led to the development of multiple model-based DL testing techniques. One common denominator of these testing techniques is the automated generation of test cases, e.g., new inputs transformed from the original training data with the aim to optimize some test adequacy criteria. So far, the effectiveness of these approaches has been hindered by their reliance on random fuzzing or transformations that do not always produce test cases with a good diversity. To overcome these limitations, we propose, DeepEvolution, a novel search-based approach for testing DL models that relies on metaheuristics to ensure a maximum diversity in generated test cases. We assess the effectiveness of DeepEvolution in testing computer-vision DL models and found that it significantly increases the neuronal coverage of generated test cases. Moreover, using DeepEvolution, we could successfully find several corner-case behaviors. Finally, DeepEvolution outperformed Tensorfuzz (a coverage-guided fuzzing tool developed at Google Brain) in detecting latent defects introduced during the quantization of the models. These results suggest that search-based approaches can help build effective testing tools for DL systems.
Classification with Costly Features as a Sequential Decision-Making Problem
Venue: arXiv cs.LG 2019
Year: 2019

This work focuses on a specific classification problem, where the information about a sample is not readily available, but has to be acquired for a cost, and there is a per-sample budget. Inspired by real-world use-cases, we analyze average and hard variations of a directly specified budget. We postulate the problem in its explicit formulation and then convert it into an equivalent MDP, that can be solved with deep reinforcement learning. Also, we evaluate a real-world inspired setting with sparse training dataset with missing features. The presented method performs robustly well in all settings across several distinct datasets, outperforming other prior-art algorithms. The method is flexible, as showcased with all mentioned modifications and can be improved with any domain independent advancement in RL.
Deep Visual Template-Free Form Parsing
Venue: arXiv cs.CV 2019
Year: 2019

Automatic, template-free extraction of information from form images is challenging due to the variety of form layouts. This is even more challenging for historical forms due to noise and degradation. A crucial part of the extraction process is associating input text with pre-printed labels. We present a learned, template-free solution to detecting pre-printed text and input text/handwriting and predicting pair-wise relationships between them. While previous approaches to this problem have been focused on clean images and clear layouts, we show our approach is effective in the domain of noisy, degraded, and varied form images. We introduce a new dataset of historical form images (late 1800s, early 1900s) for training and validating our approach. Our method uses a convolutional network to detect pre-printed text and input text lines. We pool features from the detection network to classify possible relationships in a language-agnostic way. We show that our proposed pairing method outperforms heuristic rules and that visual features are critical to obtaining high accuracy.
Asymptotic Optimality in Byzantine Distributed Quickest Change Detection
Venue: arXiv cs.IT 2019
Year: 2019

The Byzantine distributed quickest change detection (BDQCD) is studied, where a fusion center monitors the occurrence of an abrupt event through a bunch of distributed sensors that may be compromised. We first consider the binary hypothesis case where there is only one post-change hypothesis and prove a novel converse to the first-order asymptotic detection delay in the large mean time to a false alarm regime. This converse is tight in that it coincides with the currently best achievability shown by Fellouris et al.; hence, the optimal asymptotic performance of binary BDQCD is characterized. An important implication of this result is that, even with compromised sensors, a 1-bit link between each sensor and the fusion center suffices to achieve asymptotic optimality. To accommodate multiple post-change hypotheses, we then formulate the multi-hypothesis BDQCD problem and again investigate the optimal first-order performance under different bandwidth constraints. A converse is first obtained by extending our converse from binary to multi-hypothesis BDQCD. Two families of stopping rules, namely the simultaneous $d$-th alarm and the multi-shot $d$-th alarm, are then proposed. Under sufficient link bandwidth, the simultaneous $d$-th alarm, with $d$ being set to the number of honest sensors, can achieve the asymptotic performance that coincides with the derived converse bound; hence, the asymptotically optimal performance of multi-hypothesis BDQCD is again characterized. Moreover, although being shown to be asymptotically optimal only for some special cases, the multi-shot $d$-th alarm is much more bandwidth-efficient and energy-efficient than the simultaneous $d$-th alarm. Built upon the above success in characterizing the asymptotic optimality of the BDQCD, a corresponding leader-follower Stackelberg game is formulated and its solution is found.
Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance
Venue: arXiv cs.IR 2019
Year: 2019

We present a pairwise learning to rank approach based on a neural net, called DirectRanker, that generalizes the RankNet architecture. We show mathematically that our model is reflexive, antisymmetric, and transitive allowing for simplified training and improved performance. Experimental results on the LETOR MSLR-WEB10K, MQ2007 and MQ2008 datasets show that our model outperforms numerous state-of-the-art methods, while being inherently simpler in structure and using a pairwise approach only.
Automatic Failure Recovery for End-User Programs on Service Mobile Robots
Venue: arXiv cs.RO 2019
Year: 2019

For service mobile robots to be most effective, it must be possible for non-experts and even end-users to program them to do new tasks. Regardless of the programming method (e.g., by demonstration or traditional programming), robot task programs are challenging to write, because they rely on multiple actions to succeed, including human-robot interactions. Unfortunately, interactions are prone to fail, because a human may perform the wrong action (e.g., if the robot's request is not clear). Moreover, when the robot cannot directly observe the human action, it may not detect the failure until several steps after it occurs. Therefore, writing fault-tolerant robot tasks is beyond the ability of non-experts. This paper presents a principled approach to detect and recover from a broad class of failures that occur in end-user programs on service mobile robots. We present a two-tiered Robot Task Programming Language (RTPL): 1) an expert roboticist uses a specification language to write a probabilistic model of the robot's actions and interactions, and 2) a non-expert then writes an ordinary sequential program for a particular task. The RTPL runtime system executes the task program sequentially, while using the probabilistic model to build a Bayesian network that tracks possible, unobserved failures. If an error is observed, RTPL uses Bayesian inference to find the likely root cause of the error, and then attempts to re-execute a portion of the program for recovery. Our empirical results show that RTPL 1) allows complex tasks to be written concisely, 2) correctly identifies the root cause of failure, and 3) allows multiple tasks to recover from a variety of errors, without task-specific error-recovery code.
An Automatic Debugging Tool of Instruction-Driven Multicore Systems with Synchronization Points
Venue: arXiv cs.DC 2019
Year: 2019

Tracing back the instruction execution sequence to debug a multicore system can be very time-consuming because the relationships of the instructions can be very complex. For instructions that cannot be checked by the environment immediately after their executions, the errors they triggered can propagate through the instruction execution sequence. Our task is to find the error-triggered instructions automatically. This paper presents an automatic debugging tool that can leverage the synchronization points in the instruction execution sequences of the multicore system being verified to locate the instruction which results in simulation error automatically. To evaluate the performance of the debugging tool, we analyze the complexity of the algorithms and count the number of instructions executed to locate the aimed instruction.
Big Data Intelligence Using Distributed Deep Neural Networks
Venue: arXiv cs.DC 2019
Year: 2019

Large amount of data is often required to train and deploy useful machine learning models in industry. Smaller enterprises do not have the luxury of accessing enough data for machine learning, For privacy sensitive fields such as banking, insurance and healthcare, aggregating data to a data warehouse poses a challenge of data security and limited computational resources. These challenges are critical when developing machine learning algorithms in industry. Several attempts have been made to address the above challenges by using distributed learning techniques such as federated learning over disparate data stores in order to circumvent the need for centralised data aggregation. This paper proposes an improved algorithm to securely train deep neural networks over several data sources in a distributed way, in order to eliminate the need to centrally aggregate the data and the need to share the data thus preserving privacy. The proposed method allows training of deep neural networks using data from multiple de-linked nodes in a distributed environment and to secure the representation shared during training. Only a representation of the trained models (network architecture and weights) are shared. The algorithm was evaluated on existing healthcare patients data and the performance of this implementation was compared to that of a regular deep neural network trained on a single centralised architecture. This algorithm will pave a way for distributed training of neural networks on privacy sensitive applications where raw data may not be shared directly or centrally aggregating this data in a data warehouse is not feasible.
Mapping finite state machines to zk-SNARKS Using Category Theory
Venue: arXiv cs.CR 2019
Year: 2019

We provide a categorical procedure to turn graphs corresponding to state spaces of finite state machines into boolean circuits, leveraging on the fact that boolean circuits can be easily turned into zk-SNARKS. Our circuits verify that a given sequence of edges and nodes is indeed a path in the graph they represent. We then generalize to circuits verifying paths in arbitrary graphs. We prove that all of our correspondences are pseudofunctorial, and behave nicely with respect to each other.
An Edge Computing Robot Experience for Automatic Elderly Mental Health Care Based on Voice
Venue: arXiv cs.HC 2019
Year: 2019

We need open platforms driven by specialists, in which queries can be created and collected for long periods and the diagnosis made, based on a rigorous clinical follow-up. In this work, we developed a multi-language robot interface helping to evaluate the mental health of seniors by interacting through questions. The specialist can propose questions, as well as to receive users' answers, in text form. The robot can automatically interact with the user using the appropriate language. It can process the answers and under the guidance of a specialist, questions and answers can be oriented towards the desired therapy direction. The prototype, was implemented on an embedded device meant for edge computing, thus it is able to filter environmental noise and can be placed anywhere at home. The experience is now available for specialists to create queries and answers through a Web-based interface.
Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey
Venue: arXiv cs.MA 2019
Year: 2019

The majority of multi-agent system (MAS) implementations aim to optimise agents' policies with respect to a single objective, despite the fact that many real-world problem domains are inherently multi-objective in nature. Multi-objective multi-agent systems (MOMAS) explicitly consider the possible trade-offs between conflicting objective functions. We argue that, in MOMAS, such compromises should be analysed on the basis of the utility that these compromises have for the users of a system. As is standard in multi-objective optimisation, we model the user utility using utility functions that map value or return vectors to scalar values. This approach naturally leads to two different optimisation criteria: expected scalarised returns (ESR) and scalarised expected returns (SER). We develop a new taxonomy which classifies multi-objective multi-agent decision making settings, on the basis of the reward structures, and which and how utility functions are applied. This allows us to offer a structured view of the field, to clearly delineate the current state-of-the-art in multi-objective multi-agent decision making approaches and to identify promising directions for future research. Starting from the execution phase, in which the selected policies are applied and the utility for the users is attained, we analyse which solution concepts apply to the different settings in our taxonomy. Furthermore, we define and discuss these solution concepts under both ESR and SER optimisation criteria. We conclude with a summary of our main findings and a discussion of many promising future research directions in multi-objective multi-agent systems.
A Proposed Framework for Interactive Virtual Reality In Situ Visualization of Parallel Numerical Simulations
Venue: arXiv cs.DC 2019
Year: 2019

As computer simulations progress to increasingly complex, non-linear, and three-dimensional systems and phenomena, intuitive and immediate visualization of their results is becoming crucial. While Virtual Reality (VR) and Natural User Interfaces (NUIs) have been shown to improve understanding of complex 3D data, their application to live in situ visualization and computational steering is hampered by performance requirements. Here, we present the design of a software framework for interactive VR in situ visualization of parallel numerical simulations, as well as a working prototype implementation. Our design is targeted towards meeting the performance requirements for VR, and our work is packaged in a framework that allows for easy instrumentation of simulations. Our preliminary results inform about the technical feasibility of the architecture, as well as the challenges that remain.
Effects of Depth Layer Switching between an Optical See-Through Head-Mounted Display and a Body-Proximate Display
Venue: arXiv cs.HC 2019
Year: 2019

Optical see-through head-mounted displays (OST HMDs) typically display virtual content at a fixed focal distance while users need to integrate this information with real-world information at different depth layers. This problem is pronounced in body-proximate multi-display systems, such as when an OST HMD is combined with a smartphone or smartwatch. While such joint systems open up a new design space, they also reduce users' ability to integrate visual information. We quantify this cost by presenting the results of an experiment (n=24) that evaluates human performance in a visual search task across an OST HMD and a body-proximate display at 30 cm. The results reveal that task completion time increases significantly by approximately 50 % and the error rate increases significantly by approximately 100 % compared to visual search on a single depth layer. These results highlight a design trade-off when designing joint OST HMD-body proximate display systems.
Analysis of Big Data Technology for Health Care Services
Venue: arXiv cs.CY 2019
Year: 2019

Deep learning and other big data technologies have over time become very powerful and accurate. There are algorithms and models developed that have near human accuracy in their task. In health care, the amount of data available is massive and hence, these technologies have a great scope in health care. This paper reviews a few interesting contributions to the field specifically to medical imaging, genomics and patient health records.
The Difficulties of Addressing Interdisciplinary Challenges at the Foundations of Data Science
Venue: arXiv cs.CY 2019
Year: 2019

The National Science Foundation's Transdisciplinary Research in Principles of Data Science (TRIPODS) program aims to integrate three areas central to the foundations of data by uniting the statistics, mathematics, and theoretical computer science research communities. The program aims to provide a model for funding cross-cutting research and facilitating interactions among the three disciplines. Challenges associated with orchestrating fruitful interactions are described.
Characterizing the Performance of Executing Many-tasks on Summit
Venue: arXiv cs.DC 2019
Year: 2019

Many scientific workloads are comprised of many tasks, where each task is an independent simulation or analysis of data. The execution of millions of tasks on heterogeneous HPC platforms requires scalable dynamic resource management and multi-level scheduling. RADICAL-Pilot (RP) -- an implementation of the Pilot abstraction, addresses these challenges and serves as an effective runtime system to execute workloads comprised of many tasks. In this paper, we characterize the performance of executing many tasks using RP when interfaced with JSM and PRRTE on Summit: RP is responsible for resource management and task scheduling on acquired resource; JSM or PRRTE enact the placement of launching of scheduled tasks. Our experiments provide lower bounds on the performance of RP when integrated with JSM and PRRTE. Specifically, for workloads comprised of homogeneous single-core, 15 minutes-long tasks we find that: PRRTE scales better than JSM for > O(1000) tasks; PRRTE overheads are negligible; and PRRTE supports optimizations that lower the impact of overheads and enable resource utilization of 63% when executing O(16K), 1-core tasks over 404 compute nodes.
Destination-aware Adaptive Traffic Flow Rule Aggregation in Software-Defined Networks
Venue: arXiv cs.NI 2019
Year: 2019

In this paper, we propose a destination-aware adaptive traffic flow rule aggregation (DATA) mechanism for facilitating traffic flow monitoring in SDN-based networks. This method adapts the number of flow table entries in SDN switches according to the level of detail of traffic flow information that other mechanisms (e.g. for traffic engineering, traffic monitoring, intrusion detection) require. It also prevents performance degradation of the SDN switches by keeping the number of flow table entries well below a critical level. This level is not preset as a hard threshold but learned during operation by using a machine-learning based algorithm. The DATA method is implemented within a RESTful application (DATA App) which monitors and analyzes the ongoing network traffic and provides instructions to the SDN controller to adapt the traffic flow matching strategies accordingly. A thorough performance evaluation of DATA is conducted in an SDN emulation environment. The results show that---compared to the default behavior of common SDN controllers---the proposed DATA approach yields significant SDN switch performance improvements while still providing detailed traffic flow information on demand.
Modelling Cooperation in a Dynamic Healthcare System
Venue: arXiv cs.GT 2019
Year: 2019

Our research is concerned with studying behavioural changes within a dynamic system, i.e. health care, and their effects on the decision-making process. Evolutionary Game theory is applied to investigate the most probable strategy(ies) adopted by individuals in a finite population based on the interactions among them with an eye to modelling behaviour using the following metrics: cost of investment, cost of management, cost of treatment, reputation benefit for the provider(s), and the gained health benefit for the patient.
ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisons
Venue: arXiv cs.CL 2019
Year: 2019

While dialogue remains an important end-goal of natural language research, the difficulty of evaluation is an oft-quoted reason why it remains troublesome to make real progress towards its solution. Evaluation difficulties are actually two-fold: not only do automatic metrics not correlate well with human judgments, but also human judgments themselves are in fact difficult to measure. The two most used human judgment tests, single-turn pairwise evaluation and multi-turn Likert scores, both have serious flaws as we discuss in this work. We instead provide a novel procedure involving comparing two full dialogues, where a human judge is asked to pay attention to only one speaker within each, and make a pairwise judgment. The questions themselves are optimized to maximize the robustness of judgments across different annotators, resulting in better tests. We also show how these tests work in self-play model chat setups, resulting in faster, cheaper tests. We hope these tests become the de facto standard, and will release open-source code to that end.