OpenRaiser/Intern-Atlas huggingface dataset:
Microservices, Continuous Architecture, and Technical Debt Interest: An Empirical Study
Venue: arXiv cs.SE 2018
Year: 2018

Continuous Architecture (CA) is an approach that supports companies in decreasing the time between deliveries. Migration to microservices is one of the most common situations when companies adopt continuous architecting processes [4]. Companies commonly adopt an initial migration strategy to extract some components from the monolithic system as microservices, making use of simplified microservices patterns [5][4]. As an example, companies commonly directly connect the microservices to the legacy monolithic system and do not adopt any message bus at the beginning. When the system starts to grow in complexity, they usually start re-architecting their systems, considering different architectural patterns. Some companies introduce API gateway patterns to simplify the management and load balancing of the different services, while others also consider a lightweight message bus [4][5][6]. All these architectural changes require deep refactoring of the system, thereby increasing the risk of new faults being introduced. In this paper, we report the preliminary results of work in progress, where we monitored the TD of an SME (SMEs = small and medium enterprises) that adopted a CA approach while migrating a legacy monolithic system to an ecosystem of microservices. To the best of our knowledge, no studies exist on the impact of postponed activities on the TD, especially in the context of CA and microservices. This work will help companies understand how TD grows and changes over time while at the same time opening up new avenues for future research on the analysis of TD interest in continuous architecting processes.
Une architecture cognitive et affective orient{é}e interaction
Venue: arXiv cs.AI 2018
Year: 2018

In this paper, we present CAIO, a Cognitive and Affective Interaction-Oriented architecture for social human-robot interactions (HRI), allowing robots to reason on mental states (including emotions), and to act physically, emotionally and verbally. We also present a short scenario and implementation on a Nao robot.
HAR-Net:Fusing Deep Representation and Hand-crafted Features for Human Activity Recognition
Venue: arXiv cs.LG 2018
Year: 2018

Wearable computing and context awareness are the focuses of study in the field of artificial intelligence recently. One of the most appealing as well as challenging applications is the Human Activity Recognition (HAR) utilizing smart phones. Conventional HAR based on Support Vector Machine relies on subjective manually extracted features. This approach is time and energy consuming as well as immature in prediction due to the partial view toward which features to be extracted by human. With the rise of deep learning, artificial intelligence has been making progress toward being a mature technology. This paper proposes a new approach based on deep learning and traditional feature engineering called HAR-Net to address the issue related to HAR. The study used the data collected by gyroscopes and acceleration sensors in android smart phones. The raw sensor data was put into the HAR-Net proposed. The HAR-Net fusing the hand-crafted features and high-level features extracted from convolutional network to make prediction. The performance of the proposed method was proved to be 0.9% higher than the original MC-SVM approach. The experimental results on the UCI dataset demonstrate that fusing the two kinds of features can make up for the shortage of traditional feature engineering and deep learning techniques.
Alzheimer's Disease Diagnosis Based on Cognitive Methods in Virtual Environments and Emotions Analysis
Venue: arXiv cs.CV 2018
Year: 2018

Dementia is a syndrome characterised by the decline of different cognitive abilities. Alzheimer's Disease (AD) is the most common dementia affecting cognitive domains such as memory and learning, perceptual-motion or executive function. High rate of deaths and high cost for detection, treatments and patient's care count amongst its consequences. Early detection of AD is considered of high importance for improving the quality of life of patients and their families. The aim of this thesis is to introduce novel non-invasive early diagnosis methods in order to speed the diagnosis, reduce the associated costs and make them widely accessible. Novel AD's screening tests based on virtual environments using new immersive technologies combined with advanced Human Computer Interaction (HCI) systems are introduced. Four tests demonstrate the wide range of screening mechanisms based on cognitive domain impairments that can be designed using virtual environments. The use of emotion recognition to analyse AD symptoms has been also proposed. A novel multimodal dataset was specifically created to remark the autobiographical memory deficits of AD patients. Data from this dataset is used to introduce novel descriptors for Electroencephalogram (EEG) and facial images data. EEG features are based on quaternions in order to keep the correlation information between sensors, whereas, for facial expression recognition, a preprocessing method for motion magnification and descriptors based on origami crease pattern algorithm are proposed to enhance facial micro-expressions. These features have been proved on classifiers such as SVM and Adaboost for the classification of reactions to autobiographical stimuli such as long and short term memories.
Understanding the Role of Two-Sided Argumentation in Online Consumer Reviews: A Language-Based Perspective
Venue: arXiv cs.CL 2018
Year: 2018

This paper examines the effect of two-sided argumentation on the perceived helpfulness of online consumer reviews. In contrast to previous works, our analysis thereby sheds light on the reception of reviews from a language-based perspective. For this purpose, we propose an intriguing text analysis approach based on distributed text representations and multi-instance learning to operationalize the two-sidedness of argumentation in review texts. A subsequent empirical analysis using a large corpus of Amazon reviews suggests that two-sided argumentation in reviews significantly increases their helpfulness. We find this effect to be stronger for positive reviews than for negative reviews, whereas a higher degree of emotional language weakens the effect. Our findings have immediate implications for retailer platforms, which can utilize our results to optimize their customer feedback system and to present more useful product reviews.
Ranking Archived Documents for Structured Queries on Semantic Layers
Venue: arXiv cs.IR 2018
Year: 2018

Archived collections of documents (like newspaper and web archives) serve as important information sources in a variety of disciplines, including Digital Humanities, Historical Science, and Journalism. However, the absence of efficient and meaningful exploration methods still remains a major hurdle in the way of turning them into usable sources of information. A semantic layer is an RDF graph that describes metadata and semantic information about a collection of archived documents, which in turn can be queried through a semantic query language (SPARQL). This allows running advanced queries by combining metadata of the documents (like publication date) and content-based semantic information (like entities mentioned in the documents). However, the results returned by such structured queries can be numerous and moreover they all equally match the query. In this paper, we deal with this problem and formalize the task of "ranking archived documents for structured queries on semantic layers". Then, we propose two ranking models for the problem at hand which jointly consider: i) the relativeness of documents to entities, ii) the timeliness of documents, and iii) the temporal relations among the entities. The experimental results on a new evaluation dataset show the effectiveness of the proposed models and allow us to understand their limitations
Teaching Syntax by Adversarial Distraction
Venue: arXiv cs.CL 2018
Year: 2018

Existing entailment datasets mainly pose problems which can be answered without attention to grammar or word order. Learning syntax requires comparing examples where different grammar and word order change the desired classification. We introduce several datasets based on synthetic transformations of natural entailment examples in SNLI or FEVER, to teach aspects of grammar and word order. We show that without retraining, popular entailment models are unaware that these syntactic differences change meaning. With retraining, some but not all popular entailment models can learn to compare the syntax properly.
The Role of Emotion in Problem Solving: First Results from Observing Chess
Venue: arXiv cs.HC 2018
Year: 2018

In this paper we present results from recent experiments that suggest that chess players associate emotions to game situations and reactively use these associations to guide search for planning and problem solving. We describe the design of an instrument for capturing and interpreting multimodal signals of humans engaged in solving challenging problems. We review results from a pilot experiment with human experts engaged in solving challenging problems in Chess that revealed an unexpected observation of rapid changes in emotion as players attempt to solve challenging problems. We propose a cognitive model that describes the process by which subjects select chess chunks for use in interpretation of the game situation and describe initial results from a second experiment designed to test this model.
Content Protection in Named Data Networking: Challenges and Potential Solutions
Venue: arXiv cs.CR 2018
Year: 2018

Information-Centric Networks (ICN) are promising alternatives to current Internet architecture since the Internet struggles with a number of issues such as scalability, mobility and security. ICN offers a number of potential benefits including reduced congestion and enhanced delivery performance by employing content caching, simpler network configurations and stronger security for the content. Named Data Networking (NDN), an instance of the ICN, enables content delivery instead of host-centric approaches by naming data rather than the host. In order to make NDN practical in the real world, the challenging issues of content security need to be addressed. In this article, we examine the architecture, content security as well as possible solutions to these issues of NDN, with a special focus on content integrity and provenance. We propose a variety of digital signature schemes to achieve the data integrity and origin authentication in NDN for various applications, which include cost-effective signatures, privacy-preserving signatures, network coding signatures, and post-quantum signatures. We also present the speed-up techniques in generating signatures and verifying signatures such as pre-computation, batch verification and server-aided verification to reduce the computational cost of the producers and receivers in NDN. A number of certificate-free trust management approaches and possible adoptions in NDN are investigated.
Trajectory Generation for Millimeter Scale Ferromagnetic Swimmers: Theory and Experiments
Venue: arXiv cs.SY 2018
Year: 2018

Microrobots have the potential to impact many areas such as microsurgery, micromanipulation and minimally invasive sensing. Due to their small size, microrobots swim in a regime that is governed by low Reynolds number hydrodynamics. In this paper, we consider small scale artificial swimmers that are fabricated using ferromagnetic filaments and locomote in response to time varying external magnetic fields. We motivate the design of previously proposed control laws using tools from geometric mechanics and also demonstrate how new control laws can be synthesized to generate net translation in such swimmers. We further describe how to modify these control inputs to make the swimmers track rich trajectories in the workspace by investigating stability properties of their limit cycles in the orientation angles phase space. Following a systematic design optimization, we develop a principled approach to encode internal magnetization distributions in millimeter scale ferromagnetic filaments. We verify and demonstrate this procedure experimentally and finally show translation, trajectory tracking and turning in place locomotion in these optimal swimmers using a Helmholtz coils setup.
Virtual Network Embedding via Decomposable LP Formulations: Orientations of Small Extraction Width and Beyond
Venue: arXiv cs.NI 2018
Year: 2018

The Virtual Network Embedding Problem (VNEP) considers the efficient allocation of resources distributed in a substrate network to a set of request networks. Many existing works discuss either heuristics or exact algorithms, resulting in a choice between quick runtimes and quality guarantees for the solutions. Recently, the first fixed-parameter tractable (FPT) approximation algorithm for the VNEP with arbitrary request and substrate topologies has been published by Rost and Schmid. This algorithm is based on a LP formulation and is FPT in the newly introduced graph parameter extraction width (EW). It therefore combines positive traits of heuristics and exact approaches: The runtime is polynomial for instances with bounded EW, and the algorithm returns approximate solutions with high probability. We propose two extensions of this algorithm to optimize its runtime. Firstly, we develop a new LP formulation related to tree decompositions. We show that this LP formulation is always smaller, and that the resulting algorithm is FPT in the new parameter extraction label width (ELW). We improve on two important results by Rost and Schmid: For centrally rooted half-wheel topologies, the EW scales linearly with request size, whereas the ELW is constant. Further, adding any number of paths parallel to an existing edge increases the EW by at most the maximum degree of the request. By contrast, the ELW only increases by at most one. Lastly, we show that finding the minimal ELW is NP-hard. Secondly, we consider the approach of partitioning the request into subgraphs which are processed independently, yielding even smaller LP formulations. While this algorithm may lead to higher ELW within the subgraphs, we show that this increase is always smaller than the size of the inter-subgraph boundary. In particular, the algorithm has zero additional cost when subgraphs are separated by a single node.
Online Human Gesture Recognition using Recurrent Neural Networks and Wearable Sensors
Venue: arXiv cs.RO 2018
Year: 2018

Gestures are a natural communication modality for humans. The ability to interpret gestures is fundamental for robots aiming to naturally interact with humans. Wearable sensors are promising to monitor human activity, in particular the usage of triaxial accelerometers for gesture recognition have been explored. Despite this, the state of the art presents lack of systems for reliable online gesture recognition using accelerometer data. The article proposes SLOTH, an architecture for online gesture recognition, based on a wearable triaxial accelerometer, a Recurrent Neural Network (RNN) probabilistic classifier and a procedure for continuous gesture detection, relying on modelling gesture probabilities, that guarantees (i) good recognition results in terms of precision and recall, (ii) immediate system reactivity.
A novel pyramidal-FSMN architecture with lattice-free MMI for speech recognition
Venue: arXiv cs.SD 2018
Year: 2018

Deep Feedforward Sequential Memory Network (DFSMN) has shown superior performance on speech recognition tasks. Based on this work, we propose a novel network architecture which introduces pyramidal memory structure to represent various context information in different layers. Additionally, res-CNN layers are added in the front to extract more sophisticated features as well. Together with lattice-free maximum mutual information (LF-MMI) and cross entropy (CE) joint training criteria, experimental results show that this approach achieves word error rates (WERs) of 3.62% and 10.89% respectively on Librispeech and LDC97S62 (Switchboard 300 hours) corpora. Furthermore, Recurrent neural network language model (RNNLM) rescoring is applied and a WER of 2.97% is obtained on Librispeech.
The Information Flow Foundation for Conceptual Knowledge Organization
Venue: arXiv cs.AI 2018
Year: 2018

The sharing of ontologies between diverse communities of discourse allows them to compare their own information structures with that of other communities that share a common terminology and semantics - ontology sharing facilitates interoperability between online knowledge organizations. This paper demonstrates how ontology sharing is formalizable within the conceptual knowledge model of Information Flow (IF). Information Flow indirectly represents sharing through a specifiable, ontology extension hierarchy augmented with synonymic type equivalencing - two ontologies share terminology and meaning through a common generic ontology that each extends. Using the paradigm of participant community ontologies formalized as IF logics, a common shared extensible ontology formalized as an IF theory, participant community specification links from the common ontology to the participating community ontology formalizable as IF theory interpretations, this paper argues that ontology sharing is concentrated in a virtual ontology of community connections, and demonstrates how this virtual ontology is computable as the fusion of the participant ontologies - the quotient of the sum of the participant ontologies modulo the ontological sharing structure.
Some Requests for Machine Learning Research from the East African Tech Scene
Venue: arXiv cs.LG 2018
Year: 2018

Based on 46 in-depth interviews with scientists, engineers, and CEOs, this document presents a list of concrete machine research problems, progress on which would directly benefit tech ventures in East Africa.
A Text Classification Application: Poet Detection from Poetry
Venue: arXiv cs.IR 2018
Year: 2018

With the widespread use of the internet, the size of the text data increases day by day. Poems can be given as an example of the growing text. In this study, we aim to classify poetry according to poet. Firstly, data set consisting of three different poetry of poets written in English have been constructed. Then, text categorization techniques are implemented on it. Chi-Square technique are used for feature selection. In addition, five different classification algorithms are tried. These algorithms are Sequential minimal optimization, Naive Bayes, C4.5 decision tree, Random Forest and k-nearest neighbors. Although each classifier showed very different results, over the 70% classification success rate was taken by sequential minimal optimization technique.
Spectrogram-channels u-net: a source separation model viewing each channel as the spectrogram of each source
Venue: arXiv cs.SD 2018
Year: 2018

Sound source separation has attracted attention from Music Information Retrieval(MIR) researchers, since it is related to many MIR tasks such as automatic lyric transcription, singer identification, and voice conversion. In this paper, we propose an intuitive spectrogram-based model for source separation by adapting U-Net. We call it Spectrogram-Channels U-Net, which means each channel of the output corresponds to the spectrogram of separated source itself. The proposed model can be used for not only singing voice separation but also multi-instrument separation by changing only the number of output channels. In addition, we propose a loss function that balances volumes between different sources. Finally, we yield performance that is state-of-the-art on both separation tasks.
A Theoretical Framework of Approximation Error Analysis of Evolutionary Algorithms
Venue: arXiv cs.NE 2018
Year: 2018

In the empirical study of evolutionary algorithms, the solution quality is evaluated by either the fitness value or approximation error. The latter measures the fitness difference between an approximation solution and the optimal solution. Since the approximation error analysis is more convenient than the direct estimation of the fitness value, this paper focuses on approximation error analysis. However, it is straightforward to extend all related results from the approximation error to the fitness value. Although the evaluation of solution quality plays an essential role in practice, few rigorous analyses have been conducted on this topic. This paper aims at establishing a novel theoretical framework of approximation error analysis of evolutionary algorithms for discrete optimization. This framework is divided into two parts. The first part is about exact expressions of the approximation error. Two methods, Jordan form and Schur's triangularization, are presented to obtain an exact expression. The second part is about upper bounds on approximation error. Two methods, convergence rate and auxiliary matrix iteration, are proposed to estimate the upper bound. The applicability of this framework is demonstrated through several examples.
Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time
Venue: arXiv cs.AI 2018
Year: 2018

This paper investigates how to utilize different forms of human interaction to safely train autonomous systems in real-time by learning from both human demonstrations and interventions. We implement two components of the Cycle-of-Learning for Autonomous Systems, which is our framework for combining multiple modalities of human interaction. The current effort employs human demonstrations to teach a desired behavior via imitation learning, then leverages intervention data to correct for undesired behaviors produced by the imitation learner to teach novel tasks to an autonomous agent safely, after only minutes of training. We demonstrate this method in an autonomous perching task using a quadrotor with continuous roll, pitch, yaw, and throttle commands and imagery captured from a downward-facing camera in a high-fidelity simulated environment. Our method improves task completion performance for the same amount of human interaction when compared to learning from demonstrations alone, while also requiring on average 32% less data to achieve that performance. This provides evidence that combining multiple modes of human interaction can increase both the training speed and overall performance of policies for autonomous systems.
Deep Convolutional Neural Network Applied to Quality Assessment for Video Tracking
Venue: arXiv cs.CV 2018
Year: 2018

Surveillance videos often suffer from blur and exposure distortions that occur during acquisition and storage, which can adversely influence following automatic image analysis results on video-analytic tasks. The purpose of this paper is to deploy an algorithm that can automatically assess the presence of exposure distortion in videos. In this work we to design and build one architecture for deep learning applied to recognition of distortions in a video. The goal is to know if the video present exposure distortions. Such an algorithm could be used to enhance or restoration image or to create an object tracker distortion-aware.