OpenRaiser/Intern-Atlas huggingface dataset:
Characterizing IoT Data and its Quality for Use
Venue: arXiv cs.OH 2019
Year: 2019

The Internet of Things (IoT) is a cyber physical social system that encompasses science, enterprise and societal domains. Data is the most important commodity in IoT, enabling the "smarts" through analytics and decision making. IoT environments can generate and consume vast amounts of data. But managing this data effectively and gaining meaningful insights from it requires us to understand its characteristics. Traditional scientific, enterprise and big data management approaches may not be adequate, and have to evolve. Further, these characteristics and the physical deployment environments also impact the quality of the data for use. In this paper, we offer a taxonomy of IoT data characteristics, along with data quality considerations, that are constructed from the ground-up based on the diverse IoT domains and applications we review. We emphasize on the essential features, rather than a vast array of attributes. We also indicate factors that influence the data quality. Such a review is of value to IoT managers, data handlers and application composers in managing and making meaningful use of data, and for big data platform developers to offer meaningful solutions to address these considerations.
Benchmarking Neural Machine Translation for Southern African Languages
Venue: arXiv cs.CL 2019
Year: 2019

Unlike major Western languages, most African languages are very low-resourced. Furthermore, the resources that do exist are often scattered and difficult to obtain and discover. As a result, the data and code for existing research has rarely been shared. This has lead a struggle to reproduce reported results, and few publicly available benchmarks for African machine translation models exist. To start to address these problems, we trained neural machine translation models for 5 Southern African languages on publicly-available datasets. Code is provided for training the models and evaluate the models on a newly released evaluation set, with the aim of spur future research in the field for Southern African languages.
Beneficial perturbation network for continual learning
Venue: arXiv cs.LG 2019
Year: 2019

Sequential learning of multiple tasks in artificial neural networks using gradient descent leads to catastrophic forgetting, whereby previously learned knowledge is erased during learning of new, disjoint knowledge. Here, we propose a fundamentally new type of method - Beneficial Perturbation Network (BPN). We add task-dependent memory (biasing) units to allow the network to operate in different regimes for different tasks. We compute the most beneficial directions to train these units, in a manner inspired by recent work on adversarial examples. At test time, beneficial perturbations for a given task bias the network toward that task to overcome catastrophic forgetting. BPN is not only more parameter-efficient than network expansion methods, but also does not need to store any data from previous tasks, in contrast with episodic memory methods. Experiments on variants of the MNIST, CIFAR-10, CIFAR-100 datasets demonstrate strong performance of BPN when compared to the state-of-the-art.
Pseudo-solutions of word equations
Venue: arXiv cs.FL 2019
Year: 2019

We present a framework which allows a uniform approach to the recently introduced concept of pseudo-repetitions on words in the morphic case. This framework is at the same time more general and simpler. We introduce the concept of a pseudo-solution and a pseudo-rank of an equation. In particular, this allows to prove that if a classical equation forces periodicity then it also forces pseudo-periodicity. Consequently, there is no need to investigate generalizations of important equations one by one.
Gauge theory and twins paradox of disentangled representations
Venue: arXiv cs.LG 2019
Year: 2019

Achieving disentangled representations of information is one of the key goals of deep network based machine learning system. Recently there are more discussions on this issue. In this paper, by comparing the geometric structure of disentangled representation and the geometry of the evolution of mixed states in quantum mechanics, we give a fibre bundle based geometric picture of disentangled representation which can be regarded as a kind of gauge theory. From this perspective we can build a connection between the disentangled representations and the twins paradox in relativity. This can help to clarify some problems about disentangled representation.
Computer-Supported Collaborative Learning in Software Engineering Education: A Systematic Mapping Study
Venue: arXiv cs.SE 2019
Year: 2019

Computer-supported collaborative learning (CSCL) has been a steady topic of research since the early 1990s, and the trend has continued to this date. The basic benefits of CSCL in the classroom have been established in many fields of education to improve especially student motivation and critical thinking. In this paper we present a systematic mapping study about the state of research of computer-supported collaborative learning in software engineering education. The mapping study examines published articles from 2003 to 2013 to find out how this field of science has progressed. Ongoing research topics in CSCL in software engineering education concern wider learning communities and the effectiveness of different collaborative approaches. We found that while the research establishes the benefits of CSCL in several different environments from local to global ones, these approaches are not always detailed and comparative enough to pinpoint which factors have enabled their success.
Machine Learning Testing: Survey, Landscapes and Horizons
Venue: arXiv cs.LG 2019
Year: 2019

This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research. It covers 144 papers on testing properties (e.g., correctness, robustness, and fairness), testing components (e.g., the data, learning program, and framework), testing workflow (e.g., test generation and test evaluation), and application scenarios (e.g., autonomous driving, machine translation). The paper also analyses trends concerning datasets, research trends, and research focus, concluding with research challenges and promising research directions in ML testing.
A Framework for Qualitative Communications Using Big Packet Protocol
Venue: arXiv cs.NI 2019
Year: 2019

In the current Internet architecture, a packet is a minimal or fundamental unit upon which different actions such as classification,forwarding, or discarding are performed by the network nodes.When faced with constrained or poor network conditions, a packet is subjected to undesirable drops and re-transmissions, resulting in unpredictable delays and subsequent traffic overheads in the network. Alternately, we introduce qualitative communication services which allow partial, yet timely, delivery of a packet instead of dropping it entirely. These services allow breaking down packet payloads into smaller units (called chunks), enabling much finer granularity of bandwidth utilization. We propose Packet Wash as a new operation in forwarding nodes to support qualitative services. Upon packet error or network congestion, the forwarding node selectively removes some chunk(s)from the payload based on the relationship among the chunks or the individual significance level of each chunk. We also present a qualitative communication framework as well as a Packet Wash directive implemented in a newly evolved data plane technology,called Big Packet Protocol (BPP)
Topology Maintained Structure Encoding
Venue: arXiv cs.CV 2019
Year: 2019

Deep learning has been used as a powerful tool for various tasks in computer vision, such as image segmentation, object recognition and data generation. A key part of end-to-end training is designing the appropriate encoder to extract specific features from the input data. However, few encoders maintain the topological properties of data, such as connection structures and global contours. In this paper, we introduce a Voronoi Diagram encoder based on convex set distance (CSVD) and apply it in edge encoding. The boundaries of Voronoi cells is related to detected edges of structures and contours. The CSVD model improves contour extraction in CNN and structure generation in GAN. We also show the experimental results and demonstrate that the proposed model has great potentiality in different visual problems where topology information should be involved.
On the Role of Geometry in Geo-Localization
Venue: arXiv cs.CV 2019
Year: 2019

Humans can build a mental map of a geographical area to find their way and recognize places. The basic task we consider is geo-localization - finding the pose (position & orientation) of a camera in a large 3D scene from a single image. We aim to experimentally explore the role of geometry in geo-localization in a convolutional neural network (CNN) solution. We do so by ignoring the often available texture of the scene. We therefore deliberately avoid using texture or rich geometric details and use images projected from a simple 3D model of a city, which we term lean images. Lean images contain solely information that relates to the geometry of the area viewed (edges, faces, or relative depth). We find that the network is capable of estimating the camera pose from the lean images, and it does so not by memorization but by some measure of geometric learning of the geographical area. The main contributions of this paper are: (i) providing insight into the role of geometry in the CNN learning process; and (ii) demonstrating the power of CNNs for recovering camera pose using lean images.
Guessing Individual Sequences: Generating Randomized Guesses Using Finite-State Machines
Venue: arXiv cs.IT 2019
Year: 2019

Motivated by earlier results on universal randomized guessing, we consider an individual-sequence approach to the guessing problem: in this setting, the goal is to guess a secret, individual (deterministic) vector $x^n=(x_1,\ldots,x_n)$, by using a finite-state machine that sequentially generates randomized guesses from a stream of purely random bits. We define the finite-state guessing exponent as the asymptotic normalized logarithm of the minimum achievable moment of the number of randomized guesses, generated by any finite-state machine, until $x^n$ is guessed successfully. We show that the finite-state guessing exponent of any sequence is intimately related to its finite-state compressibility (due to Lempel and Ziv), and it is asymptotically achieved by the decoder of (a certain modified version of) the 1978 Lempel-Ziv data compression algorithm (a.k.a. the LZ78 algorithm), fed by purely random bits. The results are also extended to the case where the guessing machine has access to a side information sequence, $y^n=(y_1,\ldots,y_n)$, which is also an individual sequence.
Lawn: an Unbound Low Latency Timer Data Structure for Large Scale, High Throughput Systems
Venue: arXiv cs.DS 2019
Year: 2019

As demand for Real-Time applications rises among the general public, the importance of enabling large-scale, unbound algorithms to solve conventional problems with low to no latency is critical for product viability. Timer algorithms are prevalent in the core mechanisms behind operating systems, network protocol implementation, stream processing, and several database capabilities. This paper presents a field-tested algorithm for low latency, unbound range timer structure, based upon the well excepted Timing Wheel algorithm. Using a set of queues hashed by TTL, the algorithm allows for a simpler implementation, minimal overhead no overflow and no performance degradation in comparison to the current state of the algorithms under typical use cases.
Men-in-the-Middle Attack Simulation on Low Energy Wireless Devices using Software Define Radio
Venue: arXiv cs.CR 2019
Year: 2019

The article presents a method of organizing men-in-the-middle attack and penetration test on Bluetooth Low Energy devices and ZigBee packets using software define radio with sniffing and spoofing packets, capture and analysis techniques on wireless waves with the focus on Bluetooth. The paper contains the analysis of the latest scientific work in this area, provides a comparative analysis of SDRs and the rationale for the choice of hardware, gives the sequence of actions for collecting wireless data packets and data collection from ZigBee and BLE devices, and analyzes ways to improve captured wireless packet analysis techniques. For the study collected experimental setup, the results of which are analyzed in real time. The collected wireless data packets are compared with those sent. The result of the experiment shows the weaknesses of local wireless networks.
Automatic Co-Registration of Aerial Imagery and Untextured Model Data Utilizing Average Shading Gradients
Venue: arXiv cs.CV 2019
Year: 2019

The comparison of current image data with existing 3D model data of a scene provides an efficient method to keep models up to date. In order to transfer information between 2D and 3D data, a preliminary co-registration is necessary. In this paper, we present a concept to automatically co-register aerial imagery and untextured 3D model data. To refine a given initial camera pose, our algorithm computes dense correspondence fields using SIFT flow between gradient representations of the model and camera image, from which 2D-3D correspondences are obtained. These correspondences are then used in an iterative optimization scheme to refine the initial camera pose by minimizing the reprojection error. Since it is assumed that the model does not contain texture information, our algorithm is built up on an existing method based on Average Shading Gradients (ASG) to generate gradient images based on raw geometry information only. We apply our algorithm for the co-registering of aerial photographs to an untextured, noisy mesh model. We have investigated different magnitudes of input error and show that the proposed approach can reduce the final reprojection error to a minimum of 1.27 plus-minus 0.54 pixels, which is less than 10 % of its initial value. Furthermore, our evaluation shows that our approach outperforms the accuracy of a standard Iterative Closest Point (ICP) implementation.
Joint Multi-frame Detection and Segmentation for Multi-cell Tracking
Venue: arXiv cs.CV 2019
Year: 2019

Tracking living cells in video sequence is difficult, because of cell morphology and high similarities between cells. Tracking-by-detection methods are widely used in multi-cell tracking. We perform multi-cell tracking based on the cell centroid detection, and the performance of the detector has high impact on tracking performance. In this paper, UNet is utilized to extract inter-frame and intra-frame spatio-temporal information of cells. Detection performance of cells in mitotic phase is improved by multi-frame input. Good detection results facilitate multi-cell tracking. A mitosis detection algorithm is proposed to detect cell mitosis and the cell lineage is built up. Another UNet is utilized to acquire primary segmentation. Jointly using detection and primary segmentation, cells can be fine segmented in highly dense cell population. Experiments are conducted to evaluate the effectiveness of our method, and results show its state-of-the-art performance.
Heuristic Approach Towards Countermeasure Selection using Attack Graphs
Venue: arXiv cs.CR 2019
Year: 2019

Selecting the optimal set of countermeasures is a challenging task that involves various considerations and tradeoffs such as prioritizing the risks to mitigate and costs. The vast majority of studies for selecting a countermeasure deployment are based on a limited risk assessment procedure that utilizes the common vulnerability scoring system (CVSS). Such a risk assessment procedure does not necessarily consider the prerequisites and exploitability of a specific asset, cannot distinguish insider from outsider threat actor, and does not express the consequences of exploiting a vulnerability as well as the attacker's lateral movements. Other studies applied a more extensive risk assessment procedure that relies on manual work and repeated assessment. These solutions however, do not consider the network topology and do not specify the optimal position for deploying the countermeasures, and therefore are less practical. In this paper we suggest a heuristic search approach for selecting the optimal countermeasure deployment under a given budget limitation. The proposed method expresses the risk of the system using an extended attack graph modeling, which considers the prerequisites and consequences of exploiting a vulnerability, examines the attacker's potential lateral movements, and express the physical network topology as well as vulnerabilities in network protocols. In addition, unlike previous studies which utilizes attack graph for countermeasure planning, the proposed methods does not require re-generating the attack graph at each stage of the procedure, which is computationally heavy, and therefore it provides a more accurate and practical countermeasure deployment planning process.
Latent Multi-Criteria Ratings for Recommendations
Venue: arXiv cs.LG 2019
Year: 2019

Multi-criteria recommender systems have been increasingly valuable for helping consumers identify the most relevant items based on different dimensions of user experiences. However, previously proposed multi-criteria models did not take into account latent embeddings generated from user reviews, which capture latent semantic relations between users and items. To address these concerns, we utilize variational autoencoders to map user reviews into latent embeddings, which are subsequently compressed into low-dimensional discrete vectors. The resulting compressed vectors constitute latent multi-criteria ratings that we use for the recommendation purposes via standard multi-criteria recommendation methods. We show that the proposed latent multi-criteria rating approach outperforms several baselines significantly and consistently across different datasets and performance evaluation measures.
A Modular and Extensible Software Architecture for Particle Dynamics
Venue: arXiv cs.SE 2019
Year: 2019

Creating a highly parallel and flexible discrete element software requires an interdisciplinary approach, where expertise from different disciplines is combined. On the one hand domain specialists provide interaction models between particles. On the other hand high-performance computing specialists optimize the code to achieve good performance on different hardware architectures. In particular, the software must be carefully crafted to achieve good scaling on massively parallel supercomputers. Combining all this in a flexible and extensible, widely usable software is a challenging task. In this article we outline the design decisions and concepts of a newly developed particle dynamics code MESA-PD that is implemented as part of the waLBerla multi-physics framework. Extensibility, flexibility, but also performance and scalability are primary design goals for the new software framework. In particular, the new modular architecture is designed such that physical models can be modified and extended by domain scientists without understanding all details of the parallel computing functionality and the underlying distributed data structures that are needed to achieve good performance on current supercomputer architectures. This goal is achieved by combining the high performance simulation framework waLBerla with code generation techniques. All code and the code generator are released as open source under GPLv3 within the publicly available waLBerla framework (www.walberla.net).
NeuroTrajectory: A Neuroevolutionary Approach to Local State Trajectory Learning for Autonomous Vehicles
Venue: arXiv cs.RO 2019
Year: 2019

Autonomous vehicles are controlled today either based on sequences of decoupled perception-planning-action operations, either based on End2End or Deep Reinforcement Learning (DRL) systems. Current deep learning solutions for autonomous driving are subject to several limitations (e.g. they estimate driving actions through a direct mapping of sensors to actuators, or require complex reward shaping methods). Although the cost function used for training can aggregate multiple weighted objectives, the gradient descent step is computed by the backpropagation algorithm using a single-objective loss. To address these issues, we introduce NeuroTrajectory, which is a multi-objective neuroevolutionary approach to local state trajectory learning for autonomous driving, where the desired state trajectory of the ego-vehicle is estimated over a finite prediction horizon by a perception-planning deep neural network. In comparison to DRL methods, which predict optimal actions for the upcoming sampling time, we estimate a sequence of optimal states that can be used for motion control. We propose an approach which uses genetic algorithms for training a population of deep neural networks, where each network individual is evaluated based on a multi-objective fitness vector, with the purpose of establishing a so-called Pareto front of optimal deep neural networks. The performance of an individual is given by a fitness vector composed of three elements. Each element describes the vehicle's travel path, lateral velocity and longitudinal speed, respectively. The same network structure can be trained on synthetic, as well as on real-world data sequences. We have benchmarked our system against a baseline Dynamic Window Approach (DWA), as well as against an End2End supervised learning method.
Network Slicing Management Technique for Local 5G Micro-Operator Deployments
Venue: arXiv cs.NI 2019
Year: 2019

Local 5G networks are expected to emerge to serve different vertical sectors specific requirements. These networks can be deployed by traditional mobile network operators or entrant local operators. With a large number of verticals with different service requirements, while considering the network deployment cost in a single local area, it will not be economically feasible to deploy separate networks for each vertical. Thus, locally deployed 5G networks (aka micro operator networks) that can serve multiple verticals with multiple tenants in a location have gained increasing attention. Network slicing will enable a 5G micro-operator network to efficiently serve the multiple verticals and their tenants with different network requirements. This paper addresses how network slicing management functions can be used to implement, orchestrate and manage network slicing in different deployments of a local 5G micro-operator including the serving of closed, open and mixed customer groups. The paper proposes a descriptive technique by which different network slicing management functionalities defined by 3GPP can be used in coordination to create, orchestrate and manage network slicing for different deployment scenarios of a micro-operator. This is based on the network slice instance configuration type that can exist for each scenario. A network slice formation sequence is developed for the closed micro operator network to illustrate the tasks of the management functions. The results indicate that network slicing management plays a key role in designing local 5G networks that can serve different customer groups in the verticals.