OpenRaiser/Intern-Atlas huggingface dataset:
Parity-Based Concurrent Error Detection Schemes for the ChaCha Stream Cipher
Venue: arXiv cs.IT 2019
Year: 2019

We propose two parity-based concurrent error detection schemes for the Quarterround of the ChaCha stream cipher to protect from transient and permanent faults. They offer a trade-off between implementation overhead and error coverage. The second approach can detect any odd-weight error on the in-/output and intermediate signals of a Quarterround, while the first one requires less logic.
Designing Mobile Interaction Guidelines to Account for Situationally Induced Impairments and Disabilities (SIID) and Severely Constraining Situational Impairments (SCSI)
Venue: arXiv cs.HC 2019
Year: 2019

This research investigates the variety and complexity of situational impairment events (SIE) that are being experienced by users of smartphone technology of all abilities. The authors have created a classification system to help describe the different types of SIE as well as differentiate a certain subgroup of events that were identified as severely constraining. Continuing research examined workarounds that users deploy when attempting to complete a mobile I/O transaction in the presence of an SIE, as well as social/cultural barriers to attempting mobile interaction that users recognize but do not always follow. The ultimate goal of this research arc would be the creation of guidelines to assist mobile designers and researchers in the accounting of SIE and perhaps different design considerations for those events deemed severely constraining.
Guidelines for data analysis scripts
Venue: arXiv cs.SE 2019
Year: 2019

Unorganized heaps of analysis code are a growing liability as data analysis pipelines are getting longer and more complicated. This is worrying, as neuroscience papers are getting retracted due to programmer error. In this paper, some guidelines are presented that help keep analysis code well organized, easy to understand and convenient to work with: 1. Each analysis step is one script 2. A script either processes a single recording, or aggregates across recordings, never both 3. One master script to run the entire analysis 4. Save all intermediate results 5. Visualize all intermediate results 6. Each parameter and filename is defined only once 7. Distinguish files that are part of the official pipeline from other scripts In addition to discussing the reasoning behind each guideline, an example analysis pipeline is presented as a case study to see how each guideline translates into code.
On Machine Learning DoS Attack Identification from Cloud Computing Telemetry
Venue: arXiv cs.CR 2019
Year: 2019

The detection of Denial of Service (DoS) attacks remains a challenge for the cloud environment, affecting a massive number of services and applications hosted by such virtualized infrastructures. Typically, in the literature, the detection of DoS attacks is performed solely by analyzing the traffic of packets in the network. This work advocates for the use of telemetry from the cloud to detect DoS attacks using Machine Learning algorithms. Our hypothesis is based on richness of such native data collection services, with metrics from both physical and virtual hosts. Our preliminary results demonstrate that DoS can be identified accurately with k-Nearest Neighbors (kNN) and decision tree (CART).
Assisted Sound Sample Generation with Musical Conditioning in Adversarial Auto-Encoders
Venue: arXiv cs.SD 2019
Year: 2019

Generative models have thrived in computer vision, enabling unprecedented image processes. Yet the results in audio remain less advanced. Our project targets real-time sound synthesis from a reduced set of high-level parameters, including semantic controls that can be adapted to different sound libraries and specific tags. These generative variables should allow expressive modulations of target musical qualities and continuously mix into new styles. To this extent we train AEs on an orchestral database of individual note samples, along with their intrinsic attributes: note class, timbre domain and extended playing techniques. We condition the decoder for control over the rendered note attributes and use latent adversarial training for learning expressive style parameters that can ultimately be mixed. We evaluate both generative performances and latent representation. Our ablation study demonstrates the effectiveness of the musical conditioning mechanisms. The proposed model generates notes as magnitude spectrograms from any probabilistic latent code samples, with expressive control of orchestral timbres and playing styles. Its training data subsets can directly be visualized in the 3D latent representation. Waveform rendering can be done offline with GLA. In order to allow real-time interactions, we fine-tune the decoder with a pretrained MCNN and embed the full waveform generation pipeline in a plugin. Moreover the encoder could be used to process new input samples, after manipulating their latent attribute representation, the decoder can generate sample variations as an audio effect would. Our solution remains rather fast to train, it can directly be applied to other sound domains, including an user's libraries with custom sound tags that could be mapped to specific generative controls. As a result, it fosters creativity and intuitive audio style experimentations.
Evolving Indoor Navigational Strategies Using Gated Recurrent Units In NEAT
Venue: arXiv cs.NE 2019
Year: 2019

Simultaneous Localisation and Mapping (SLAM) algorithms are expensive to run on smaller robotic platforms such as Micro-Aerial Vehicles. Bug algorithms are an alternative that use relatively little processing power, and avoid high memory consumption by not building an explicit map of the environment. Bug Algorithms achieve relatively good performance in simulated and robotic maze solving domains. However, because they are hand-designed, a natural question is whether they are globally optimal control policies. In this work we explore the performance of Neuroevolution - specifically NEAT - at evolving control policies for simulated differential drive robots carrying out generalised maze navigation. We extend NEAT to include Gated Recurrent Units (GRUs) to help deal with long term dependencies. We show that both NEAT and our NEAT-GRU can repeatably generate controllers that outperform I-Bug (an algorithm particularly well-suited for use in real robots) on a test set of 209 indoor maze like environments. We show that NEAT-GRU is superior to NEAT in this task but also that out of the 2 systems, only NEAT-GRU can continuously evolve successful controllers for a much harder task in which no bearing information about the target is provided to the agent.
Agile Process Consultation -- An Applied Psychology Approach to Agility
Venue: arXiv cs.SE 2019
Year: 2019

An agile change effort in an organization needs to be understood in relation to human processes. Such theory and accompanying tools already existed almost 50 years ago in applied psychology. The core ideas of Agile Process Consultation are that a client initiating a change toward more agility often does not know what is wrong and the consultant needs to diagnose the problem jointly with the client. It is also assumed that the agile consultant cannot know the organizational culture of the client's organization, which means that the client needs to be making the decisions based on the suggestions provided by the consultant. Since agile project management is spreading across the enterprise and outside of software development, we need situational approaches instead of prescribing low-level practices.
The Importance of Conflict Resolution Techniques in Autonomous Agile Teams
Venue: arXiv cs.SE 2019
Year: 2019

Today, software companies usually organize their work in teams. Social science research on team development has shown that for a team to reach a productive and autonomous stage, it has to be able to manage internal conflicts and disagreements efficiently. To better facilitate the team development process, we argue that software engineers' needs additional training in negotiation skills and conflict resolution. In this position paper, we outline ideas for what aspects to consider in such training. As an example, we argue that a majority of the conflicts originate from team-level factors and that they, therefore, should be managed on the team-level instead of in relation to dyads.
A Reference Vector based Many-Objective Evolutionary Algorithm with Feasibility-aware Adaptation
Venue: arXiv cs.NE 2019
Year: 2019

The infeasible parts of the objective space in difficult many-objective optimization problems cause trouble for evolutionary algorithms. This paper proposes a reference vector based algorithm which uses two interacting engines to adapt the reference vectors and to evolve the population towards the true Pareto Front (PF) s.t. the reference vectors are always evenly distributed within the current PF to provide appropriate guidance for selection. The current PF is tracked by maintaining an archive of undominated individuals, and adaptation of reference vectors is conducted with the help of another archive that contains layers of reference vectors corresponding to different density. Experimental results show the expected characteristics and competitive performance of the proposed algorithm TEEA.
Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments
Venue: arXiv cs.LG 2019
Year: 2019

Reproducibility in reinforcement learning is challenging: uncontrolled stochasticity from many sources, such as the learning algorithm, the learned policy, and the environment itself have led researchers to report the performance of learned agents using aggregate metrics of performance over multiple random seeds for a single environment. Unfortunately, there are still pernicious sources of variability in reinforcement learning agents that make reporting common summary statistics an unsound metric for performance. Our experiments demonstrate the variability of common agents used in the popular OpenAI Baselines repository. We make the case for reporting post-training agent performance as a distribution, rather than a point estimate.
Fast and Resource Competitive Broadcast in Multi-channel Radio Networks
Venue: arXiv cs.DC 2019
Year: 2019

Consider a single-hop, multi-channel, synchronous radio network in which a source node needs to disseminate a message to all other $n-1$ nodes. An adversary called Eve, which captures environmental noise and potentially malicious interference, aims to disrupt this process via jamming. Assume sending, listening, or jamming on one channel for one time slot costs unit energy. The question is, if Eve spends $T$ units of energy on jamming, can we devise broadcast algorithms in which each node's cost is $o(T)$? Previous results show such resource competitive algorithms do exist in the single-channel setting: each node can receive the message within $\tilde{O}(T+n)$ time slots while spending only $\tilde{O}(\sqrt{T/n}+1)$ energy. In this paper, we show that when Eve is oblivious, the existence of multiple channels allows even faster message dissemination, while preserving resource competitiveness. Specifically, we have identified an efficient "epidemic broadcast" scheme in the multi-channel setting that is robust again jamming. Extending this scheme leads to a randomized algorithm called MultiCast which uses $n/2$ channels, and accomplishes broadcast in $\tilde{O}(T/n+1)$ time slots while costing each node only $\tilde{O}(\sqrt{T/n}+1)$ energy. When the value of $n$ is unknown, we further propose MultiCastAdv, in which each node's running time is $\tilde{O}(T/(n^{1-2α})+n^{2α})$, and each node's cost is $\tilde{O}(\sqrt{T/(n^{1-2α})}+n^{2α})$. Here, $0<α<1/4$ is a tunable parameter affecting the constant hiding behind the big-$O$ notation. To handle the issue of limited channel availability, we have also devised variants for both MultiCast and MultiCastAdv that can work in networks in which only $C$ channels are available, for any $C\geq 1$. These variants remain to be resource competitive, and have (near) optimal time complexity in many cases.
Animo: Sharing Biosignals on a Smartwatch for Lightweight Social Connection
Venue: arXiv cs.HC 2019
Year: 2019

We present Animo, a smartwatch app that enables people to share and view each other's biosignals. We designed and engineered Animo to explore new ground for smartwatch-based biosignals social computing systems: identifying opportunities where these systems can support lightweight and mood-centric interactions. In our work we develop, explore, and evaluate several innovative features designed for dyadic communication of heart rate. We discuss the results of a two-week study (N=34), including new communication patterns participants engaged in, and outline the design landscape for communicating with biosignals on smartwatches.
L1-norm Tucker Tensor Decomposition
Venue: arXiv cs.NA 2019
Year: 2019

Tucker decomposition is a common method for the analysis of multi-way/tensor data. Standard Tucker has been shown to be sensitive against heavy corruptions, due to its L2-norm-based formulation which places squared emphasis to peripheral entries. In this work, we explore L1-Tucker, an L1-norm based reformulation of standard Tucker decomposition. After formulating the problem, we present two algorithms for its solution, namely L1-norm Higher-Order Singular Value Decomposition (L1-HOSVD) and L1-norm Higher-Order Orthogonal Iterations (L1-HOOI). The presented algorithms are accompanied by complexity and convergence analysis. Our numerical studies on tensor reconstruction and classification corroborate that L1-Tucker, implemented by means of the proposed methods, attains similar performance to standard Tucker when the processed data are corruption-free, while it exhibits sturdy resistance against heavily corrupted entries.
Legal Area Classification: A Comparative Study of Text Classifiers on Singapore Supreme Court Judgments
Venue: arXiv cs.CL 2019
Year: 2019

This paper conducts a comparative study on the performance of various machine learning (``ML'') approaches for classifying judgments into legal areas. Using a novel dataset of 6,227 Singapore Supreme Court judgments, we investigate how state-of-the-art NLP methods compare against traditional statistical models when applied to a legal corpus that comprised few but lengthy documents. All approaches tested, including topic model, word embedding, and language model-based classifiers, performed well with as little as a few hundred judgments. However, more work needs to be done to optimize state-of-the-art methods for the legal domain.
Topic Grouper: An Agglomerative Clustering Approach to Topic Modeling
Venue: arXiv cs.IR 2019
Year: 2019

We introduce Topic Grouper as a complementary approach in the field of probabilistic topic modeling. Topic Grouper creates a disjunctive partitioning of the training vocabulary in a stepwise manner such that resulting partitions represent topics. It is governed by a simple generative model, where the likelihood to generate the training documents via topics is optimized. The algorithm starts with one-word topics and joins two topics at every step. It therefore generates a solution for every desired number of topics ranging between the size of the training vocabulary and one. The process represents an agglomerative clustering that corresponds to a binary tree of topics. A resulting tree may act as a containment hierarchy, typically with more general topics towards the root of tree and more specific topics towards the leaves. Topic Grouper is not governed by a background distribution such as the Dirichlet and avoids hyper parameter optimizations. We show that Topic Grouper has reasonable predictive power and also a reasonable theoretical and practical complexity. Topic Grouper can deal well with stop words and function words and tends to push them into their own topics. Also, it can handle topic distributions, where some topics are more frequent than others. We present typical examples of computed topics from evaluation datasets, where topics appear conclusive and coherent. In this context, the fact that each word belongs to exactly one topic is not a major limitation; in some scenarios this can even be a genuine advantage, e.g.~a related shopping basket analysis may aid in optimizing groupings of articles in sales catalogs.
On Model Adaptation for Sensorimotor Control of Robots
Venue: arXiv cs.RO 2019
Year: 2019

In this article, we address the problem of computing adaptive sensorimotor models that can be used for guiding the motion of robotic systems with uncertain action-to-perception relations. The formulation of the uncalibrated sensor-based control problem is first presented, then, various computational methods for building adaptive sensorimotor models are derived and analysed. The proposed methodology is exemplified with two cases of study: (i) shape control of deformable objects with unknown properties, and (ii) soft manipulation of ultrasonic probes with uncalibrated sensors.
Flint for Safer Smart Contracts
Venue: arXiv cs.PL 2019
Year: 2019

The Ethereum blockchain platform supports the execution of decentralised applications or smart contracts. These typically hold and transfer digital currency to other parties on the platform; however, they have been subject to numerous attacks due to the unintentional introduction of bugs. Over a billion dollars worth of currency has been stolen since its release in July 2015. As smart contracts cannot be updated after deployment, it is imperative that the programming language supports the development of robust contracts. We propose Flint, a new statically-typed programming language specifically designed for writing robust smart contracts. Flint's features enforce the writing of safe and predictable code. To encourage good practices, we introduce protection blocks. Protection blocks restrict who can run code and when (using typestate) it can be executed. To prevent vulnerabilities relating to the unintentional loss of currency, Flint Asset traits provide safe atomic operations, ensuring the state of contracts is always consistent. Writes to state are restricted, simplifying reasoning about smart contracts.
Analysis and Identification of Controller Interaction and Limitation in VSC-HVDC Grid
Venue: arXiv cs.SY 2019
Year: 2019

With the advancement of the idea of the HVDC grid, it becomes imperative to study the interaction of controller and identification of modes of oscillations. The paper presents the complete model of HVDC grid with detailed modeling of controllers, phase lock loops (PLLs), dc grid and the ac systems. An analytical method combining participation factors and Eigen-sensitivity is proposed to determine the relative stability of the HVDC grid with respect to parameter variations. The paper also proposes the identification of inter-area and controller interaction modes on the basis of the nearest distance between the eigenvalues using the derived sensitivity indices. The critical parameters are varied in a mixed short circuit ratio (SCR) HVDC grid and predictions from the analysis method are confirmed with time-domain simulation in PSCAD simulator
A Hybrid Traffic Speed Forecasting Approach Integrating Wavelet Transform and Motif-based Graph Convolutional Recurrent Neural Network
Venue: arXiv cs.CV 2019
Year: 2019

Traffic forecasting is crucial for urban traffic management and guidance. However, existing methods rarely exploit the time-frequency properties of traffic speed observations, and often neglect the propagation of traffic flows from upstream to downstream road segments. In this paper, we propose a hybrid approach that learns the spatio-temporal dependency in traffic flows and predicts short-term traffic speeds on a road network. Specifically, we employ wavelet transform to decompose raw traffic data into several components with different frequency sub-bands. A Motif-based Graph Convolutional Recurrent Neural Network (Motif-GCRNN) and Auto-Regressive Moving Average (ARMA) are used to train and predict low-frequency components and high-frequency components, respectively. In the Motif-GCRNN framework, we integrate Graph Convolutional Networks (GCNs) with local sub-graph structures - Motifs - to capture the spatial correlations among road segments, and apply Long Short-Term Memory (LSTM) to extract the short-term and periodic patterns in traffic speeds. Experiments on a traffic dataset collected in Chengdu, China, demonstrate that the proposed hybrid method outperforms six state-of-art prediction methods.
Online Sampling in the Parameter Space of a Neural Network for GPU-accelerated Motion Planning of Autonomous Vehicles
Venue: arXiv cs.RO 2019
Year: 2019

This paper proposes online sampling in the parameter space of a neural network for GPU-accelerated motion planning of autonomous vehicles. Neural networks are used as controller parametrization since they can handle nonlinear non-convex systems and their complexity does not scale with prediction horizon length. Network parametrizations are sampled at each sampling time and then held constant throughout the prediction horizon. Controls still vary over the prediction horizon due to varying feature vectors fed to the network. Full-dimensional vehicles are modeled by polytopes. Under the assumption of obstacle point data, and their extrapolation over a prediction horizon under constant velocity assumption, collision avoidance reduces to linear inequality checks. Steering and longitudinal acceleration controls are determined simultaneously. The proposed method is designed for parallelization and therefore well-suited to benefit from continuing advancements in hardware such as GPUs. Characteristics of proposed method are illustrated in 5 numerical simulation experiments including dynamic obstacle avoidance, waypoint tracking requiring alternating forward and reverse driving with maximal steering, and a reverse parking scenario.
From News to Medical: Cross-domain Discourse Segmentation
Venue: arXiv cs.CL 2019
Year: 2019

The first step in discourse analysis involves dividing a text into segments. We annotate the first high-quality small-scale medical corpus in English with discourse segments and analyze how well news-trained segmenters perform on this domain. While we expectedly find a drop in performance, the nature of the segmentation errors suggests some problems can be addressed earlier in the pipeline, while others would require expanding the corpus to a trainable size to learn the nuances of the medical domain.