OpenRaiser/Intern-Atlas huggingface dataset:
Approximate Solution Approach and Performability Evaluation of Large Scale Beowulf Clusters
Venue: arXiv cs.PF 2019
Year: 2019

Beowulf clusters are very popular and deployed worldwide in support of scientific computing, because of the high computational power and performance. However, they also pose several challenges, and yet they need to provide high availability. The practical large-scale Beowulf clusters result in unpredictable, fault-tolerant, often detrimental outcomes. Successful development of high performance in storing and processing huge amounts of data in large-scale clusters necessitates accurate quality of service (QoS) evaluation. This leads to develop as well as design, analytical models to understand and predict of complex system behaviour in order to ensure availability of large-scale systems. Exact modelling of such clusters is not feasible due to the nature of the large scale nodes and the diversity of user requests. An analytical model for QoS of large-scale server farms and solution approaches are necessary. In this paper, analytical modelling of large-scale Beowulf clusters is considered together with availability issues. A generic and flexible approximate solution approach is developed to handle large number of nodes for performability evaluation. The proposed analytical model and the approximate solution approach provide flexibility to evaluate the QoS measurements for such systems. In order to show the efficacy and the accuracy of the proposed approach, the results obtained from the analytical model are validated with the results obtained from the discrete event simulations.
Channel Capacity Enhancement of SWIPT based CNOMA Downlink Transmission with Orbital Angular Momentum over Rician Fading Channel
Venue: arXiv cs.NI 2019
Year: 2019

In this paper, the integration of cooperative non-orthogonal multiple access (CNOMA) and power splitting based simultaneous wireless information and power transfer (SWIPT) is proposed to improve the spectral and energy efficiency. Moreover, different orbital angular momentum (OAM) based signals are also considered in this paper to transmit additional symbols to cell center user (CCU) and cell edge user (CEU). In the proposed technique, CCU is used as energy-constrained relay to forward signal to CEU. CCU harvests energy from the transmitted signal by the BS using the power splitting technique. The additional symbols are transmitted from the BS to CCU and CEU by utilizing different modes of OAM. This paper investigates the ergodic sum capacity (ESC) of the proposed technique along with analytical derivations over Rician fading channels. Finally, the performance in terms of ESC and energy efficiency over conventional technique compared to conventional multiple access is demonstrated by analysis and simulation.
Stack sorting with restricted stacks
Venue: arXiv cs.DS 2019
Year: 2019

The (classical) problem of characterizing and enumerating permutations that can be sorted using two stacks connected in series is still largely open. In the present paper we address a related problem, in which we impose restrictions both on the procedure and on the stacks. More precisely, we consider a greedy algorithm where we perform the rightmost legal operation (here "rightmost" refers to the usual representation of stack sorting problems). Moreover, the first stack is required to be $σ$-avoiding, for some permutation $σ$, meaning that, at each step, the elements maintained in the stack avoid the pattern $σ$ when read from top to bottom. Since the set of permutations which can be sorted by such a device (which we call $σ$-machine) is not always a class, it would be interesting to understand when it happens. We will prove that the set of $σ$-machines whose associated sortable permutations are not a class is counted by Catalan numbers. Moreover, we will analyze two specific $σ$-machines in full details (namely when $σ=321$ and $σ=123$), providing for each of them a complete characterization and enumeration of sortable permutations.
Querying Knowledge via Multi-Hop English Questions
Venue: arXiv cs.CL 2019
Year: 2019

The inherent difficulty of knowledge specification and the lack of trained specialists are some of the key obstacles on the way to making intelligent systems based on the knowledge representation and reasoning (KRR) paradigm commonplace. Knowledge and query authoring using natural language, especially controlled natural language (CNL), is one of the promising approaches that could enable domain experts, who are not trained logicians, to both create formal knowledge and query it. In previous work, we introduced the KALM system (Knowledge Authoring Logic Machine) that supports knowledge authoring (and simple querying) with very high accuracy that at present is unachievable via machine learning approaches. The present paper expands on the question answering aspect of KALM and introduces KALM-QA (KALM for Question Answering) that is capable of answering much more complex English questions. We show that KALM-QA achieves 100% accuracy on an extensive suite of movie-related questions, called MetaQA, which contains almost 29,000 test questions and over 260,000 training questions. We contrast this with a published machine learning approach, which falls far short of this high mark.
Quantitative Impact Evaluation of an Abstraction Layer for Data Stream Processing Systems
Venue: arXiv cs.PF 2019
Year: 2019

With the demand to process ever-growing data volumes, a variety of new data stream processing frameworks have been developed. Moving an implementation from one such system to another, e.g., for performance reasons, requires adapting existing applications to new interfaces. Apache Beam addresses these high substitution costs by providing an abstraction layer that enables executing programs on any of the supported streaming frameworks. In this paper, we present a novel benchmark architecture for comparing the performance impact of using Apache Beam on three streaming frameworks: Apache Spark Streaming, Apache Flink, and Apache Apex. We find significant performance penalties when using Apache Beam for application development in the surveyed systems. Overall, usage of Apache Beam for the examined streaming applications caused a high variance of query execution times with a slowdown of up to a factor of 58 compared to queries developed without the abstraction layer. All developed benchmark artifacts are publicly available to ensure reproducible results.
When can we improve on sample average approximation for stochastic optimization?
Venue: arXiv cs.LG 2019
Year: 2019

We explore the performance of sample average approximation in comparison with several other methods for stochastic optimization when there is information available on the underlying true probability distribution. The methods we evaluate are (a) bagging; (b) kernel smoothing; (c) maximum likelihood estimation (MLE); and (d) a Bayesian approach. We use two test sets, the first has a quadratic objective function allowing for very different types of interaction between the random component and the univariate decision variable. Here the sample average approximation is remarkably effective and only consistently outperformed by a Bayesian approach. The second test set is a portfolio optimization problem in which we use different covariance structures for a set of 5 stocks. Here bagging, MLE and a Bayesian approach all do well.
Online Set-Based Dynamic Analysis for Sound Predictive Race Detection
Venue: arXiv cs.SE 2019
Year: 2019

Predictive data race detectors find data races that exist in executions other than the observed execution. Smaragdakis et al. introduced the causally-precedes (CP) relation and a polynomial-time analysis for sound (no false races) predictive data race detection. However, their analysis cannot scale beyond analyzing bounded windows of execution traces. This work introduces a novel dynamic analysis called Raptor that computes CP soundly and completely. Raptor is inherently an online analysis that analyzes and finds all CP-races of an execution trace in its entirety. An evaluation of a prototype implementation of Raptor shows that it scales to program executions that the prior CP analysis cannot handle, finding data races that the prior CP analysis cannot find.
Representation Learning for Classical Planning from Partially Observed Traces
Venue: arXiv cs.AI 2019
Year: 2019

Specifying a complete domain model is time-consuming, which has been a bottleneck of AI planning technique application in many real-world scenarios. Most classical domain-model learning approaches output a domain model in the form of the declarative planning language, such as STRIPS or PDDL, and solve new planning instances by invoking an existing planner. However, planning in such a representation is sensitive to the accuracy of the learned domain model which probably cannot be used to solve real planning problems. In this paper, to represent domain models in a vectorization representation way, we propose a novel framework based on graph neural network (GNN) integrating model-free learning and model-based planning, called LP-GNN. By embedding propositions and actions in a graph, the latent relationship between them is explored to form a domain-specific heuristics. We evaluate our approach on five classical planning domains, comparing with the classical domain-model learner ARMS. The experimental results show that the domain models learned by our approach are much more effective on solving real planning problems.
DaiMoN: A Decentralized Artificial Intelligence Model Network
Venue: arXiv cs.LG 2019
Year: 2019

We introduce DaiMoN, a decentralized artificial intelligence model network, which incentivizes peer collaboration in improving the accuracy of machine learning models for a given classification problem. It is an autonomous network where peers may submit models with improved accuracy and other peers may verify the accuracy improvement. The system maintains an append-only decentralized ledger to keep the log of critical information, including who has trained the model and improved its accuracy, when it has been improved, by how much it has improved, and where to find the newly updated model. DaiMoN rewards these contributing peers with cryptographic tokens. A main feature of DaiMoN is that it allows peers to verify the accuracy improvement of submitted models without knowing the test labels. This is an essential component in order to mitigate intentional model overfitting by model-improving peers. To enable this model accuracy evaluation with hidden test labels, DaiMoN uses a novel learnable Distance Embedding for Labels (DEL) function proposed in this paper. Specific to each test dataset, DEL scrambles the test label vector by embedding it in a low-dimension space while approximately preserving the distance between the dataset's test label vector and a label vector inferred by the classifier. It therefore allows proof-of-improvement (PoI) by peers without providing them access to true test labels. We provide analysis and empirical evidence that under DEL, peers can accurately assess model accuracy. We also argue that it is hard to invert the embedding function and thus, DEL is resilient against attacks aiming to recover test labels in order to cheat. Our prototype implementation of DaiMoN is available at https://github.com/steerapi/daimon.
Non-Orthogonal Multiple Access with Spatial Modulation in Downlink Coordinated Multipoint Transmission
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, a joint transmission coordinated multi-point based non-orthogonal multiple access (JT-COMP NOMA) combined with spatial modulation (SM), termed as JT-COMP NOMA-SM, is proposed to enhance capacity. User capacity and ergodic sum capacity (ESC) of M number coordinated multi-point base stations (COMP BSs) within N number of cells are analyzed by considering imperfect successive interference cancellation (SIC) and imperfect channel state information (CSI). The performances of the proposed syatem are compared with non-orthogonal multiple access (NOMA), and joint transmission coordinated multi-point combined with virtual user pairing based non-orthogonal multiple access (JT-COMP VP-NOMA) by both simulation and analysis. The results show that the proposed system has the same cell center user (CCU) capacity compared to JT-COMP VP-NOMA and a higher cell edge user (CEU) capacity than the other schemes. ESC of the proposed system outperforms the other schemes due to enhancing CEU capacity. Imperfect SIC and imperfect CSI may degrade capacity. The proposed system can maintain CEU capacity better than the other schemes if the number of cells is increased. It happens because SM works beyond Shannon upper bounds which can mitigate inter-cell interference (ICI).
Automatic mobility analysis of parallel mechanisms: an algorithm approach based on position and orientation characteristic equations
Venue: arXiv cs.RO 2019
Year: 2019

The determination of the mobility of parallel mechanisms (PM) is a fundamental problem. An automatic and intelligent analysis platform will be a significant tool for the design and optimization of mechanical systems. Based on the theory of position and orientation characteristics (POC) equations, a systematic approach to computer-aided mobility analysis of PMs is presented in this paper. First, a digital model for topological structures which has a mapping relationship with position and orientation characteristics of mechanism is proposed. It describes not only the dimension of the motion output, but also gives the mapping relationship between the output characteristic and the axis of the kinematic joints. Secondly, algorithmic rules are established that convert the union and intersection operations of POC into the binary logical operations and the automatic analysis of POC are realized. Then, the algorithm of the automatic mobility analysis of PMs and its implementation with VC++ are written .The mobility and its properties (POC) will also be analyzed and displayed automatically after introducing by users of the data of topological structures representation. Finally, typical examples are provided to show the effectiveness of the software platform.
A Comparative Evaluation of Visual and Natural Language Question Answering Over Linked Data
Venue: arXiv cs.IR 2019
Year: 2019

With the growing number and size of Linked Data datasets, it is crucial to make the data accessible and useful for users without knowledge of formal query languages. Two approaches towards this goal are knowledge graph visualization and natural language interfaces. Here, we investigate specifically question answering (QA) over Linked Data by comparing a diagrammatic visual approach with existing natural language-based systems. Given a QA benchmark (QALD7), we evaluate a visual method which is based on iteratively creating diagrams until the answer is found, against four QA systems that have natural language queries as input. Besides other benefits, the visual approach provides higher performance, but also requires more manual input. The results indicate that the methods can be used complementary, and that such a combination has a large positive impact on QA performance, and also facilitates additional features such as data exploration.
Language Modelling for Sound Event Detection with Teacher Forcing and Scheduled Sampling
Venue: arXiv cs.SD 2019
Year: 2019

A sound event detection (SED) method typically takes as an input a sequence of audio frames and predicts the activities of sound events in each frame. In real-life recordings, the sound events exhibit some temporal structure: for instance, a "car horn" will likely be followed by a "car passing by". While this temporal structure is widely exploited in sequence prediction tasks (e.g., in machine translation), where language models (LM) are exploited, it is not satisfactorily modeled in SED. In this work we propose a method which allows a recurrent neural network (RNN) to learn an LM for the SED task. The method conditions the input of the RNN with the activities of classes at the previous time step. We evaluate our method using F1 score and error rate (ER) over three different and publicly available datasets; the TUT-SED Synthetic 2016 and the TUT Sound Events 2016 and 2017 datasets. The obtained results show an increase of 9% and 2% at the F1 (higher is better) and a decrease of 7% and 2% at ER (lower is better) for the TUT Sound Events 2016 and 2017 datasets, respectively, when using our method. On the contrary, with our method there is a decrease of 4% at F1 score and an increase of 7% at ER for the TUT-SED Synthetic 2016 dataset.
End-to-end Learning for GMI Optimized Geometric Constellation Shape
Venue: arXiv cs.IT 2019
Year: 2019

Autoencoder-based geometric shaping is proposed that includes optimizing bit mappings. Up to 0.2 bits/QAM symbol gain in GMI is achieved for a variety of data rates and in the presence of transceiver impairments. The gains can be harvested with standard binary FEC at no cost w.r.t. conventional BICM.
Learning sparsity in reservoir computing through a novel bio-inspired algorithm
Venue: arXiv cs.LG 2019
Year: 2019

The mushroom body is the key network for the representation of learned olfactory stimuli in Drosophila and insects. The sparse activity of Kenyon cells, the principal neurons in the mushroom body, plays a key role in the learned classification of different odours. In the specific case of the fruit fly, the sparseness of the network is enforced by an inhibitory feedback neuron called APL, and by an intrinsic high firing threshold of the Kenyon cells. In this work we took inspiration from the fruit fly brain to formulate a novel machine learning algorithm that is able to optimize the sparsity level of a reservoir by changing the firing thresholds of the nodes. The sparsity is only applied on the readout layer so as not to change the timescales of the reservoir and to allow the derivation of a one-layer update rule for the firing thresholds. The proposed algorithm is a combination of learning a neuron-specific sparsity threshold via gradient descent and a global sparsity threshold via a Markov chain Monte Carlo method. The proposed model outperforms the standard gradient descent, which is limited to the readout weights of the reservoir, on two example tasks. It demonstrates how the learnt sparse representation can lead to better classification performance, memorization ability and convergence time.
Logical Segmentation of Source Code
Venue: arXiv cs.SE 2019
Year: 2019

Many software analysis methods have come to rely on machine learning approaches. Code segmentation - the process of decomposing source code into meaningful blocks - can augment these methods by featurizing code, reducing noise, and limiting the problem space. Traditionally, code segmentation has been done using syntactic cues; current approaches do not intentionally capture logical content. We develop a novel deep learning approach to generate logical code segments regardless of the language or syntactic correctness of the code. Due to the lack of logically segmented source code, we introduce a unique data set construction technique to approximate ground truth for logically segmented code. Logical code segmentation can improve tasks such as automatically commenting code, detecting software vulnerabilities, repairing bugs, labeling code functionality, and synthesizing new code.
PPAC: A Versatile In-Memory Accelerator for Matrix-Vector-Product-Like Operations
Venue: arXiv cs.AR 2019
Year: 2019

Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. Most existing PIM architectures are either general-purpose but only support atomistic operations, or are specialized to accelerate a single task. We propose the Parallel Processor in Associative Content-addressable memory (PPAC), a novel in-memory accelerator that supports a range of matrix-vector-product (MVP)-like operations that find use in traditional and emerging applications. PPAC is, for example, able to accelerate low-precision neural networks, exact/approximate hash lookups, cryptography, and forward error correction. The fully-digital nature of PPAC enables its implementation with standard-cell-based CMOS, which facilitates automated design and portability among technology nodes. To demonstrate the efficacy of PPAC, we provide post-layout implementation results in 28nm CMOS for different array sizes. A comparison with recent digital and mixed-signal PIM accelerators reveals that PPAC is competitive in terms of throughput and energy-efficiency, while accelerating a wide range of applications and simplifying development.
Hyperparameter Optimisation with Early Termination of Poor Performers
Venue: arXiv cs.LG 2019
Year: 2019

It is typical for a machine learning system to have numerous hyperparameters that affect its learning rate and prediction quality. Finding a good combination of the hyperparameters is, however, a challenging job. This is mainly because evaluation of each combination is extremely expensive computationally; indeed, training a machine learning system on real data with just a single combination of hyperparameters usually takes hours or even days. In this paper, we address this challenge by trying to predict the performance of the machine learning system with a given combination of hyperparameters without completing the expensive learning process. Instead, we terminate the training process at an early stage, collect the model performance data and use it to predict which of the combinations of hyperparameters is most promising. Our preliminary experiments show that such a prediction improves the performance of the commonly used random search approach.
Sound Search by Text Description or Vocal Imitation?
Venue: arXiv cs.HC 2019
Year: 2019

Searching sounds by text labels is often difficult, as text descriptions cannot describe the audio content in detail. Query by vocal imitation bridges such gap and provides a novel way to sound search. Several algorithms for sound search by vocal imitation have been proposed and evaluated in a simulation environment, however, they have not been deployed into a real search engine nor evaluated by real users. This pilot work conducts a subjective study to compare these two approaches to sound search, and tries to answer the question of which approach works better for what kinds of sounds. To do so, we developed two web-based search engines for sound, one by vocal imitation (Vroom!) and the other by text description (TextSearch). We also developed an experimental framework to host these engines to collect statistics of user behaviors and ratings. Results showed that Vroom! received significantly higher search satisfaction ratings than TextSearch did for sound categories that were difficult for subjects to describe by text. Results also showed a better overall ease-of-use rating for Vroom! than TextSearch on the limited sound library in our experiments. These findings suggest advantages of vocal-imitation-based search for sound in practice.