OpenRaiser/Intern-Atlas huggingface dataset:
Snowball: Iterative Model Evolution and Confident Sample Discovery for Semi-Supervised Learning on Very Small Labeled Datasets
Venue: arXiv cs.CV 2019
Year: 2019

In this work, we develop a joint sample discovery and iterative model evolution method for semi-supervised learning on very small labeled training sets. We propose a master-teacher-student model framework to provide multi-layer guidance during the model evolution process with multiple iterations and generations. The teacher model is constructed by performing an exponential moving average of the student models obtained from past training steps. The master network combines the knowledge of the student and teacher models with additional access to newly discovered samples. The master and teacher models are then used to guide the training of the student network by enforcing the consistence between their predictions of unlabeled samples and evolve all models when more and more samples are discovered. Our extensive experiments demonstrate that the discovering confident samples from the unlabeled dataset, once coupled with the above master-teacher-student network evolution, can significantly improve the overall semi-supervised learning performance. For example, on the CIFAR-10 dataset, with a very small set of 250 labeled samples, our method achieves an error rate of 11.81 %, more than 38 % lower than the state-of-the-art method Mean-Teacher (49.91 %).
Engineering Boolean Matrix Multiplication for Multiple-Accelerator Shared-Memory Architectures
Venue: arXiv cs.DS 2019
Year: 2019

We study the problem of multiplying two bit matrices with entries either over the Boolean algebra $(0,1,\vee,\wedge)$ or over the binary field $(0,1,+,\cdot)$. We engineer high-performance open-source algorithm implementations for contemporary multiple-accelerator shared-memory architectures, with the objective of time-and-energy-efficient scaling up to input sizes close to the available shared memory capacity. For example, given two terabinary-bit square matrices as input, our implementations compute the Boolean product in approximately 2100 seconds (1.0 Pbop/s at 3.3 pJ/bop for a total of 2.1 kWh/product) and the binary product in less than 950 seconds (2.4 effective Pbop/s at 1.5 effective pJ/bop for a total of 0.92 kWh/product) on an NVIDIA DGX-1 with power consumption at peak system power (3.5 kW). Our contributions are (a) for the binary product, we use alternative-basis techniques of Karstadt and Schwartz [SPAA '17] to design novel alternative-basis variants of Strassen's recurrence for $2\times 2$ block multiplication [Numer. Math. 13 (1969)] that have been optimized for both the number of additions and low working memory, (b) structuring the parallel block recurrences and the memory layout for coalescent and register-localized execution on accelerator hardware, (c) low-level engineering of the innermost block products for the specific target hardware, and (d) structuring the top-level shared-memory implementation to feed the accelerators with data and integrate the results for input and output sizes beyond the aggregate memory capacity of the available accelerators.
Interference mitigation techniques for a dense heterogeneous area network in machine-to-machine communications
Venue: arXiv cs.NI 2019
Year: 2019

With the advent of Machine-to-Machine (M2M) communications, various networking consumer industrial and autonomous systems exchange messages in the real world in order to achieve their objectives. Parts of these systems are comprised of short-range wireless networks in the form of clusters that collectively cover a large geographical area. In these clusters, the nodes that represent the cluster heads need to deal with two types of communications: one is within the cluster and the other is from the cluster to the sink node. As the number of clusters increases, it takes multiple hops for the cluster head to forward data to the sink node, thus resulting in a low packet delivery rate and throughput. To solve this problem, we propose a heterogeneous area network in which the cluster head is equipped with two types of radios: the IEEE 802.15.4 and IEEE 802.11 radios. The former is for the devices within the cluster to communicate, whereas the latter is for the cluster heads to communicate to the sink node. Although the IEEE 802.11 links increase the link capacity, the IEEE 802.11 radio and the IEEE 802.15.4 radio might share the 2.4 GHz unlicensed band, thus giving rise to the inter-network collisions or interference. To tackle this problem and to maintain decent Qualityof-Service (QoS) for the network, we subsequently present two interference mitigation techniques, in which a Blank Burst (BB) period is proposed so that the IEEE 802.15.4 radios can be suspended while the IEEE 802.11 radios are active. Simulation results show the proposed two methods can effectively mitigate the inter-network collisions and are superior to the existing technique, which uses an adaptive aggregation technique to mitigate the inter-network collisions.
Multi-DoF Time Domain Passivity Approach Based Drift Compensation for Telemanipulation
Venue: arXiv cs.RO 2019
Year: 2019

When, in addition to stability, position synchronization is also desired in bilateral teleoperation, Time Domain Passivity Approach (TDPA) alone might not be able to fulfill the desired objective. This is due to an undesired effect caused by admittance type passivity controllers, namely position drift. Previous works focused on developing TDPA-based drift compensation methods to solve this issue. It was shown that, in addition to reducing drift, one of the proposed methods was able to keep the force signals within their normal range, guaranteeing the safety of the task. However, no multi-DoF treatment of those approaches has been addressed. In that scope, this paper focuses on providing an extension of previous TDPA-based approaches to multi-DoF Cartesian-space teleoperation. An analysis of the convergence properties of the presented method is also provided. In addition, its applicability to multi-DoF devices is shown through hardware experiments and numerical simulation with round-trip time delays up to 700 ms.
Scaling agile on large enterprise level -- systematic bundling and application of state of the art approaches for lasting agile transitions
Venue: arXiv cs.SE 2019
Year: 2019

Organizations are looking for ways of establishing agile and lean process for delivery. Many approaches exist in the form of frameworks, methods and tools to setup an individual composition for a best fit. The challenge is that large organizations are heterogeneous and diverse, and hence there is no "one size fits all" approach. To facilitate a systematic implementation of agile and lean, this article proposes a transition kit based on abstraction. This kit scouts and bundles state of the art methods and tools from the agile and lean community to align them with governance and compliance aspects of the specific enterprise. Coaching of the application of the transition kit ensures an adequate instantiation. The instantiation handles business domain specific aspects and standards. A coaching governance ensures continuous improvement. An example of the systematic application of the transition approach as well as its scaling is demonstrated through its application in the Volkswagen Group IT.
Regression-based sparse polynomial chaos for uncertainty quantification of subsurface flow models
Venue: arXiv cs.CE 2019
Year: 2019

Surrogate-modelling techniques including Polynomial Chaos Expansion (PCE) is commonly used for statistical estimation (aka. Uncertainty Quantification) of quantities of interests obtained from expensive computational models. PCE is a data-driven regression-based technique that relies on spectral polynomials as basis-functions. In this technique, the outputs of few numerical simulations are used to estimate the PCE coefficients within a regression framework combined with regularization techniques where the regularization parameters are estimated using standard cross-validation as applied in supervised machine learning methods. In the present work, we introduce an efficient method for estimating the PCE coefficients combining Elastic Net regularization with a data-driven feature ranking approach. Our goal is to increase the probability of identifying the most significant PCE components by assigning each of the PCE coefficients a numerical value reflecting the magnitude of the coefficient and its stability with respect to perturbations in the input data. In our evaluations, the proposed approach has shown high convergence rate for high-dimensional problems, where standard feature ranking might be challenging due to the curse of dimensionality. The presented method is implemented within a standard machine learning library (Scikit-learn) allowing for easy experimentation with various solvers and regularization techniques (e.g. Tikhonov, LASSO, LARS, Elastic Net) and enabling automatic cross-validation techniques using a widely used and well tested implementation. We present a set of numerical tests on standard analytical functions, a two-phase subsurface flow model and a simulation dataset for CO2 sequestration in a saline aquifer. For all test cases, the proposed approach resulted in a significant increase in PCE convergence rates.
Social Networks as a Tool for a Higher Education Institution Image Creation
Venue: arXiv cs.HC 2019
Year: 2019

The article presents the dynamics of social networks users increase, depending on the total world population from 2010 to 2018. It also identifies the most popular social networks in Ukraine. The systematic risk indicator of using social networks relative to the total number of Internet resources users is determined. Types of social intercourse in the process of the higher education institution image creation are presented. The peculiarities of using social networks in the formation of a positive image of an educational institution are highlighted. The statistical indicators of user actions in the official group of the Faculty of Mathematics and Information Technologies of Vasyl Stus Donetsk National University in January, February and March 2019 are presented, as well as the average attraction coefficient of users depending on the subject of publications. The main technologies of astroturfing in the creation process of the higher education institution negative image are considered.
Learning Test Traces
Venue: arXiv cs.SE 2019
Year: 2019

Modern software projects include automated tests written to check the programs' functionality. The set of functions invoked by a test is called the trace of the test, and the action of obtaining a trace is called tracing. There are many tracing tools since traces are useful for a variety of software engineering tasks such as test generation, fault localization, and test execution planning. A major drawback in using test traces is that obtaining them, i.e., tracing, can be costly in terms of computational resources and runtime. Prior work attempted to address this in various ways, e.g., by selectively tracing only some of the software components or compressing the trace on-the-fly. However, all these approaches still require building the project and executing the test in order to get its (partial, possibly compressed) trace. This is still very costly in many cases. In this work, we propose a method to predict the trace of each test without executing it, based only on static properties of the test and the tested program, as well as past experience on different tests. This prediction is done by applying supervised learning to learn the relation between various static features of test and function and the likelihood that one will include the other in its trace. Then, we show how to use the predicted traces in a recent automated troubleshooting paradigm called Learn Diagnose and plan (LDP), instead of the actual, costly-to-obtain, test traces. In a preliminary evaluation on real-world open-source projects, we observe that our prediction quality is reasonable. In addition, using our trace predictions in LDP yields almost the same results comparing to when using real traces, while requiring less overhead.
Büchi automata for distributed temporal logic
Venue: arXiv cs.MA 2019
Year: 2019

The distributed temporal logic DTL is a logic for reasoning about temporal properties of distributed systems from the local point of view of the system's agents, which are assumed to execute sequentially and to interact by means of synchronous event sharing. Different versions of DTL have been provided over the years for a number of different applications, reflecting different perspectives on how non-local information can be accessed by each agent. In this paper, we propose a novel notion of distributed Büchi automaton envisaged to encompass DTL with a model-checking mechanism.
Probabilities in Session Types
Venue: arXiv cs.LO 2019
Year: 2019

This paper deals with the probabilistic behaviours of distributed systems described by a process calculus considering both probabilistic internal choices and nondeterministic external choices. For this calculus we define and study a typing system which extends the multiparty session types in order to deal also with probabilistic behaviours. The calculus and its typing system are motivated and illustrated by a running example.
Status in flux: Unequal alliances can create power vacuums
Venue: arXiv cs.SI 2019
Year: 2019

Human groups show a variety of leadership structures from no leader, to changing leaders, to a single long-term leader. When a leader is deposed, the presence of a power vacuum can mean they are often quickly replaced. We lack an explanation of how such phenomena can emerge from simple rules of interaction between individuals. Here, we model transitions between different phases of leadership structure. We find a novel class of group dynamical behaviour where there is a single leader who is quickly replaced when they lose status, demonstrating a power vacuum. The model uses a dynamic network of individuals who non-coercively form and break alliances with one-another, with a key parameter modelling inequality in these alliances. We argue the model can explain transitions in leadership structure in the Neolithic Era from relatively equal hunter-gatherer societies, to groups with chieftains which change over time, to groups with an institutionalised leader on a paternal lineage. Our model demonstrates how these transitions can be explained by the impact of technological developments such as food storage and/or weapons, which meant that alliances became more unequal. In general terms, our approach provides a quantitative understanding of how technology and social norms can affect leadership dynamics and structures.
Let's agree to disagree: learning highly debatable multirater labelling
Venue: arXiv cs.CV 2019
Year: 2019

Classification and differentiation of small pathological objects may greatly vary among human raters due to differences in training, expertise and their consistency over time. In a radiological setting, objects commonly have high within-class appearance variability whilst sharing certain characteristics across different classes, making their distinction even more difficult. As an example, markers of cerebral small vessel disease, such as enlarged perivascular spaces (EPVS) and lacunes, can be very varied in their appearance while exhibiting high inter-class similarity, making this task highly challenging for human raters. In this work, we investigate joint models of individual rater behaviour and multirater consensus in a deep learning setting, and apply it to a brain lesion object-detection task. Results show that jointly modelling both individual and consensus estimates leads to significant improvements in performance when compared to directly predicting consensus labels, while also allowing the characterization of human-rater consistency.
Dispersion of Mobile Robots in the Global Communication Model
Venue: arXiv cs.DC 2019
Year: 2019

The dispersion problem on graphs asks $k\leq n$ robots placed initially arbitrarily on the nodes of an $n$-node anonymous graph to reposition autonomously to reach a configuration in which each robot is on a distinct node of the graph. This problem is of significant interest due to its relationship to other fundamental robot coordination problems, such as exploration, scattering, load balancing etc. In this paper, we consider dispersion in the {\em global communication} model where a robot can communicate with any other robot in the graph (but the graph is unknown to robots). We provide three novel deterministic algorithms, two for arbitrary graphs and one for arbitrary trees, in a synchronous setting where all robots perform their actions in every time step. For arbitrary graphs, our first algorithm is based on a DFS traversal and guarantees $O(\min(m,kΔ))$ steps runtime using $Θ(\log (\max(k,Δ)))$ bits at each robot, where $m$ is the number of edges and $Δ$ is the maximum degree of the graph. The second algorithm for arbitrary graphs is based on a BFS traversal and guarantees $O( \max(D,k) Δ(D+Δ))$ steps runtime using $O(\max(D,Δ\log k))$ bits at each robot, where $D$ is the diameter of the graph. The algorithm for arbitrary trees is also based on a BFS travesal and guarantees $O(D\max(D,k))$ steps runtime using $O(\max(D,Δ\log k))$ bits at each robot. Our results are significant improvements compared to the existing results established in the {\em local communication} model where a robot can communication only with other robots present at the same node. Particularly, the DFS-based algorithm is optimal for both memory and time in constant-degree arbitrary graphs. The BFS-based algorithm for arbitrary trees is optimal with respect to runtime when $k\leq O(D)$.
Do Cross Modal Systems Leverage Semantic Relationships?
Venue: arXiv cs.CV 2019
Year: 2019

Current cross-modal retrieval systems are evaluated using R@K measure which does not leverage semantic relationships rather strictly follows the manually marked image text query pairs. Therefore, current systems do not generalize well for the unseen data in the wild. To handle this, we propose a new measure, SemanticMap, to evaluate the performance of cross-modal systems. Our proposed measure evaluates the semantic similarity between the image and text representations in the latent embedding space. We also propose a novel cross-modal retrieval system using a single stream network for bidirectional retrieval. The proposed system is based on a deep neural network trained using extended center loss, minimizing the distance of image and text descriptions in the latent space from the class centers. In our system, the text descriptions are also encoded as images which enabled us to use a single stream network for both text and images. To the best of our knowledge, our work is the first of its kind in terms of employing a single stream network for cross-modal retrieval systems. The proposed system is evaluated on two publicly available datasets including MSCOCO and Flickr30K and has shown comparable results to the current state-of-the-art methods.
Weakly Supervised Universal Fracture Detection in Pelvic X-rays
Venue: arXiv cs.CV 2019
Year: 2019

Hip and pelvic fractures are serious injuries with life-threatening complications. However, diagnostic errors of fractures in pelvic X-rays (PXRs) are very common, driving the demand for computer-aided diagnosis (CAD) solutions. A major challenge lies in the fact that fractures are localized patterns that require localized analyses. Unfortunately, the PXRs residing in hospital picture archiving and communication system do not typically specify region of interests. In this paper, we propose a two-stage hip and pelvic fracture detection method that executes localized fracture classification using weakly supervised ROI mining. The first stage uses a large capacity fully-convolutional network, i.e., deep with high levels of abstraction, in a multiple instance learning setting to automatically mine probable true positive and definite hard negative ROIs from the whole PXR in the training data. The second stage trains a smaller capacity model, i.e., shallower and more generalizable, with the mined ROIs to perform localized analyses to classify fractures. During inference, our method detects hip and pelvic fractures in one pass by chaining the probability outputs of the two stages together. We evaluate our method on 4 410 PXRs, reporting an area under the ROC curve value of 0.975, the highest among state-of-the-art fracture detection methods. Moreover, we show that our two-stage approach can perform comparably to human physicians (even outperforming emergency physicians and surgeons), in a preliminary reader study of 23 readers.
Artificial Neural Networks and Adaptive Neuro-fuzzy Models for Prediction of Remaining Useful Life
Venue: arXiv cs.LG 2019
Year: 2019

The U.S. water distribution system contains thousands of miles of pipes constructed from different materials, and of various sizes, and age. These pipes suffer from physical, environmental, structural and operational stresses, causing deterioration which eventually leads to their failure. Pipe deterioration results in increased break rates, reduced hydraulic capacity, and detrimental impacts on water quality. Therefore, it is crucial to use accurate models to forecast deterioration rates along with estimating the remaining useful life of the pipes to implement essential interference plans in order to prevent catastrophic failures. This paper discusses a computational model that forecasts the RUL of water pipes by applying Artificial Neural Networks (ANNs) as well as Adaptive Neural Fuzzy Inference System (ANFIS). These models are trained and tested acquired field data to identify the significant parameters that impact the prediction of RUL. It is concluded that, on average, with approximately 10\% of wall thickness loss in existing cast iron, ductile iron, asbestos-cement, and steel water pipes, the reduction of the remaining useful life is approximately 50%
Fast BFS-Based Triangle Counting on GPUs
Venue: arXiv cs.DC 2019
Year: 2019

In this paper, we propose a novel method to compute triangle counting on GPUs. Unlike previous formulations of graph matching, our approach is BFS-based by traversing the graph in an all-source-BFS manner and thus can be mapped onto GPUs in a massively parallel fashion. Our implementation uses the Gunrock programming model and we evaluate our implementation in runtime and memory consumption compared with previous state-of-the-art work. We sustain a peak traversed-edges-per-second (TEPS) rate of nearly 10 GTEPS. Our algorithm is the most scalable and parallel among all existing GPU implementations and also outperforms all existing CPU distributed implementations. This work specifically focuses on leveraging our implementation on the triangle counting problem for the Subgraph Isomorphism Graph Challenge 2019, demonstrating a geometric mean speedup over the 2018 champion of 3.84x.
Atypical Facial Landmark Localisation with Stacked Hourglass Networks: A Study on 3D Facial Modelling for Medical Diagnosis
Venue: arXiv cs.LG 2019
Year: 2019

While facial biometrics has been widely used for identification purpose, it has recently been researched as medical biometrics for a range of diseases. In this chapter, we investigate the facial landmark detection for atypical 3D facial modelling in facial palsy cases, while potentially such modelling can assist the medical diagnosis using atypical facial features. In our work, a study of landmarks localisation methods such as stacked hourglass networks is conducted and evaluated to ascertain their accuracy when presented with unseen atypical faces. The evaluation highlights that the state-of-the-art stacked hourglass architecture outperforms other traditional methods.