OpenRaiser/Intern-Atlas huggingface dataset:
Computational Thinking in Education: Where does it Fit? A systematic literary review
Venue: arXiv cs.CY 2017
Year: 2017

Computational Thinking (CT) has been described as an essential skill which everyone should learn and can therefore include in their skill set. Seymour Papert is credited as concretising Computational Thinking in 1980 but since Wing popularised the term in 2006 and brought it to the international community's attention, more and more research has been conducted on CT in education. The aim of this systematic literary review is to give educators and education researchers an overview of what work has been carried out in the domain, as well as potential gaps and opportunities that still exist. Overall it was found in this review that, although there is a lot of work currently being done around the world in many different educational contexts, the work relating to CT is still in its infancy. Along with the need to create an agreed-upon definition of CT lots of countries are still in the process of, or have not yet started, introducing CT into curriculums in all levels of education. It was also found that Computer Science/Computing, which could be the most obvious place to teach CT, has yet to become a mainstream subject in some countries, although this is improving. Of encouragement to educators is the wealth of tools and resources being developed to help teach CT as well as more and more work relating to curriculum development. For those teachers looking to incorporate CT into their schools or classes then there are bountiful options which include programming, hands-on exercises and more. The need for more detailed lesson plans and curriculum structure however, is something that could be of benefit to teachers.
Minimax Robust Decentralized Detection in Parallel Sensor Networks
Venue: arXiv cs.IT 2017
Year: 2017

Minimax robust decentralized detection is studied for parallel sensor networks. Random variables corresponding to sensor observations are assumed to follow a distribution function, which belongs to an uncertainty class. It has been proven that, for some uncertainty classes, if all probability distributions are absolutely continuous with respect to a common measure, the joint stochastic boundedness property, which is the fundamental rule for the derivations in Veerevalli's work, does not hold. This raises a natural question whether minimax robust decentralized detection is possible if the uncertainty classes do not own this property. The answer to this question has been shown to be positive, which leads to a generalization of the work of Veerevalli. Moreover, due to a direct consequence of Tsitsiklis's work, quantization functions at the sensors are not required to be monotone. For the proposed model, some specific examples have been provided and possible generalizations have been discussed.
Classifying Symmetrical Differences and Temporal Change in Mammography Using Deep Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

We investigate the addition of symmetry and temporal context information to a deep Convolutional Neural Network (CNN) with the purpose of detecting malignant soft tissue lesions in mammography. We employ a simple linear mapping that takes the location of a mass candidate and maps it to either the contra-lateral or prior mammogram and Regions Of Interest (ROI) are extracted around each location. We subsequently explore two different architectures (1) a fusion model employing two datastreams were both ROIs are fed to the network during training and testing and (2) a stage-wise approach where a single ROI CNN is trained on the primary image and subsequently used as feature extractor for both primary and symmetrical or prior ROIs. A 'shallow' Gradient Boosted Tree (GBT) classifier is then trained on the concatenation of these features and used to classify the joint representation. Results shown a significant increase in performance using the first architecture and symmetry information, but only marginal gains in performance using temporal data and the other setting. We feel results are promising and can greatly be improved when more temporal data becomes available.
\$1 Today or \$2 Tomorrow? The Answer is in Your Facebook Likes
Venue: arXiv cs.AI 2017
Year: 2017

In economics and psychology, delay discounting is often used to characterize how individuals choose between a smaller immediate reward and a larger delayed reward. People with higher delay discounting rate (DDR) often choose smaller but more immediate rewards (a "today person"). In contrast, people with a lower discounting rate often choose a larger future rewards (a "tomorrow person"). Since the ability to modulate the desire of immediate gratification for long term rewards plays an important role in our decision-making, the lower discounting rate often predicts better social, academic and health outcomes. In contrast, the higher discounting rate is often associated with problematic behaviors such as alcohol/drug abuse, pathological gambling and credit card default. Thus, research on understanding and moderating delay discounting has the potential to produce substantial societal benefits.
Learning to Partition using Score Based Compatibilities
Venue: arXiv cs.LG 2017
Year: 2017

We study the problem of learning to partition users into groups, where one must learn the compatibilities between the users to achieve optimal groupings. We define four natural objectives that optimize for average and worst case compatibilities and propose new algorithms for adaptively learning optimal groupings. When we do not impose any structure on the compatibilities, we show that the group formation objectives considered are $NP$ hard to solve and we either give approximation guarantees or prove inapproximability results. We then introduce an elegant structure, namely that of \textit{intrinsic scores}, that makes many of these problems polynomial time solvable. We explicitly characterize the optimal groupings under this structure and show that the optimal solutions are related to \emph{homophilous} and \emph{heterophilous} partitions, well-studied in the psychology literature. For one of the four objectives, we show $NP$ hardness under the score structure and give a $\frac{1}{2}$ approximation algorithm for which no constant approximation was known thus far. Finally, under the score structure, we propose an online low sample complexity PAC algorithm for learning the optimal partition. We demonstrate the efficacy of the proposed algorithm on synthetic and real world datasets.
Weight Design of Distributed Approximate Newton Algorithms for Constrained Optimization
Venue: arXiv cs.NA 2017
Year: 2017

Motivated by economic dispatch and linearly-constrained resource allocation problems, this paper proposes a novel Distributed Approx-Newton algorithm that approximates the standard Newton optimization method. A main property of this distributed algorithm is that it only requires agents to exchange constant-size communication messages. The convergence of this algorithm is discussed and rigorously analyzed. In addition, we aim to address the problem of designing communication topologies and weightings that are optimal for second-order methods. To this end, we propose an effective approximation which is loosely based on completing the square to address the NP-hard bilinear optimization involved in the design. Simulations demonstrate that our proposed weight design applied to the Distributed Approx-Newton algorithm has a superior convergence property compared to existing weighted and distributed first-order gradient descent methods.
Generative Adversarial Residual Pairwise Networks for One Shot Learning
Venue: arXiv cs.CV 2017
Year: 2017

Deep neural networks achieve unprecedented performance levels over many tasks and scale well with large quantities of data, but performance in the low-data regime and tasks like one shot learning still lags behind. While recent work suggests many hypotheses from better optimization to more complicated network structures, in this work we hypothesize that having a learnable and more expressive similarity objective is an essential missing component. Towards overcoming that, we propose a network design inspired by deep residual networks that allows the efficient computation of this more expressive pairwise similarity objective. Further, we argue that regularization is key in learning with small amounts of data, and propose an additional generator network based on the Generative Adversarial Networks where the discriminator is our residual pairwise network. This provides a strong regularizer by leveraging the generated data samples. The proposed model can generate plausible variations of exemplars over unseen classes and outperforms strong discriminative baselines for few shot classification tasks. Notably, our residual pairwise network design outperforms previous state-of-theart on the challenging mini-Imagenet dataset for one shot learning by getting over 55% accuracy for the 5-way classification task over unseen classes.
Relaxed Bi-quadratic Optimization for Joint Filter-Signal Design in Signal-Dependent STAP
Venue: arXiv cs.SY 2017
Year: 2017

We investigate an alternative solution method to the joint signal-beamformer optimization problem considered by Setlur and Rangaswamy[1]. First, we directly demonstrate that the problem, which minimizes the received noise, interference, and clutter power under a minimum variance distortionless response (MVDR) constraint, is generally non-convex and provide concrete insight into the nature of the nonconvexity. Second, we employ the theory of biquadratic optimization and semidefinite relaxations to produce a relaxed version of the problem, which we show to be convex. The optimality conditions of this relaxed problem are examined and a variety of potential solutions are found, both analytically and numerically.
An embedded segmental K-means model for unsupervised segmentation and clustering of speech
Venue: arXiv cs.CL 2017
Year: 2017

Unsupervised segmentation and clustering of unlabelled speech are core problems in zero-resource speech processing. Most approaches lie at methodological extremes: some use probabilistic Bayesian models with convergence guarantees, while others opt for more efficient heuristic techniques. Despite competitive performance in previous work, the full Bayesian approach is difficult to scale to large speech corpora. We introduce an approximation to a recent Bayesian model that still has a clear objective function but improves efficiency by using hard clustering and segmentation rather than full Bayesian inference. Like its Bayesian counterpart, this embedded segmental K-means model (ES-KMeans) represents arbitrary-length word segments as fixed-dimensional acoustic word embeddings. We first compare ES-KMeans to previous approaches on common English and Xitsonga data sets (5 and 2.5 hours of speech): ES-KMeans outperforms a leading heuristic method in word segmentation, giving similar scores to the Bayesian model while being 5 times faster with fewer hyperparameters. However, its clusters are less pure than those of the other models. We then show that ES-KMeans scales to larger corpora by applying it to the 5 languages of the Zero Resource Speech Challenge 2017 (up to 45 hours), where it performs competitively compared to the challenge baseline.
Concurrent Software Design Based on Constraints on State Diagrams
Venue: arXiv cs.SE 2017
Year: 2017

Concurrent software for engineering computations consists of multiple cooperating modules. The behavior of individual modules is described by means on state diagrams. In the paper, the constraints on state diagrams are proposed, allowing for the specification of designer's intentions as to the synchronization of modules. Also, the translation of state diagrams (with enforcement constraints) into Concurrent State Machines is shown, which provides formal framework for the verification of inter-module synchronization. An example of engineering software design based on the method is presented.
TokTrack: A Complete Token Provenance and Change Tracking Dataset for the English Wikipedia
Venue: arXiv cs.CL 2017
Year: 2017

We present a dataset that contains every instance of all tokens (~ words) ever written in undeleted, non-redirect English Wikipedia articles until October 2016, in total 13,545,349,787 instances. Each token is annotated with (i) the article revision it was originally created in, and (ii) lists with all the revisions in which the token was ever deleted and (potentially) re-added and re-deleted from its article, enabling a complete and straightforward tracking of its history. This data would be exceedingly hard to create by an average potential user as it is (i) very expensive to compute and as (ii) accurately tracking the history of each token in revisioned documents is a non-trivial task. Adapting a state-of-the-art algorithm, we have produced a dataset that allows for a range of analyses and metrics, already popular in research and going beyond, to be generated on complete-Wikipedia scale; ensuring quality and allowing researchers to forego expensive text-comparison computation, which so far has hindered scalable usage. We show how this data enables, on token-level, computation of provenance, measuring survival of content over time, very detailed conflict metrics, and fine-grained interactions of editors like partial reverts, re-additions and other metrics, in the process gaining several novel insights.
k-Anonymously Private Search over Encrypted Data
Venue: arXiv cs.CR 2017
Year: 2017

In this paper we compare the performance of various homomorphic encryption methods on a private search scheme that can achieve $k$-anonymity privacy. To make our benchmarking fair, we use open sourced cryptographic libraries which are written by experts and well scrutinized. We find that Goldwasser-Micali encryption achieves good enough performance for practical use, whereas fully homomorphic encryptions are much slower than partial ones like Goldwasser-Micali and Paillier.
Tangible Holograms: Towards Mobile Physical Augmentation of Virtual Objects
Venue: arXiv cs.HC 2017
Year: 2017

The last two decades have seen the emergence and steady development of tangible user interfaces. While most of these interfaces are applied for input - with output still on traditional computer screens - the goal of programmable matter and actuated shape-changing materials is to directly use the physical objects for visual or tangible feedback. Advances in material sciences and flexible display technologies are investigated to enable such reconfigurable physical objects. While existing solutions aim for making physical objects more controllable via the digital world, we propose an approach where holograms (virtual objects) in a mixed reality environment are augmented with physical variables such as shape, texture or temperature. As such, the support for mobility forms an important contribution of the proposed solution since it enables users to freely move within and across environments. Furthermore, our augmented virtual objects can co-exist in a single environment with programmable matter and other actuated shape-changing solutions. The future potential of the proposed approach is illustrated in two usage scenarios and we hope that the presentation of our work in progress on a novel way to realise tangible holograms will foster some lively discussions in the CHI community.
State-dependent Priority Scheduling for Networked Control Systems
Venue: arXiv cs.SY 2017
Year: 2017

Networked control systems (NCS) have attracted considerable attention in recent years. While the stabilizability and optimal control of NCS for a given communication system has already been studied extensively, the design of the communication system for NCS has recently seen an increase in more thorough investigation. In this paper, we address an optimal scheduling problem for a set of NCS sharing a dedicated communication channel, providing performance bounds and asymptotic stability. We derive a suboptimal scheduling policy with dynamic state-based priorities calculated at the sensors, which are then used for stateless priority queuing in the network, making it both scalable and efficient to implement on routers or multi-layer switches. These properties are beneficial towards leveraging existing IP networks for control, which will be a crucial factor for the proliferation of wide-area NCS applications. By allowing for an arbitrary number of concurrent transmissions, we are able to investigate the relationship between available bandwidth, transmission rate, and delay. To demonstrate the feasibility of our approach, we provide a proof-of-concept implementation of the priority scheduler using real networking hardware.
The Emergence of Crowdsourcing among Pokémon Go Players
Venue: arXiv cs.SI 2017
Year: 2017

Since its launching, Pok{é}mon Go has been pointed as the largest gaming phenomenon of the smartphone age. As the game requires the user to walk in the real world to see and capture Pok{é}mons, a new wave of crowdsourcing apps have emerged to allow users to collaborate with each other, sharing where and when Pok{é}mons were found. In this paper we characterize one of such initiatives, called PokeCrew. Our analyses uncover a set of aspects of user behavior and system usage in such emerging crowdsourcing task, helping unveil some problems and benefits. We hope our effort can inspire the design of new crowdsourcing systems.
Redynis: Traffic-aware dynamic repartitioning for a distributed key-value store
Venue: arXiv cs.DB 2017
Year: 2017

Most modern data stores tend to be distributed, to enable the scaling of the data across multiple instances of commodity hardware. Although this ensures a near unlimited potential for storage, the data itself is not always ideally partitioned, and the cost of a network round-trip may cause a degradation of end-user experience with respect to response latency. The problem being solved is bringing the data objects closer to the frequent sources of requests using a dynamic repartitioning algorithm. This is important if the objective is to mitigate the overhead of network latency, and especially so if the partitions are widely geo-distributed. The intention is to bring these features to an existing distributed key-value store product, Redis.
Secure Management of Low Power Fitness Trackers
Venue: arXiv cs.CR 2017
Year: 2017

The increasing popular interest in personal telemetry, also called the Quantified Self or "lifelogging", has induced a popularity surge for wearable personal fitness trackers. Fitness trackers automatically collect sensor data about the user throughout the day, and integrate it into social network accounts. Solution providers have to strike a balance between many constraints, leading to a design process that often puts security in the back seat. Case in point, we reverse engineered and identified security vulnerabilities in Fitbit Ultra and Gammon Forerunner 610, two popular and representative fitness tracker products. We introduce FitBite and GarMax, tools to launch efficient attacks against Fitbit and Garmin. We devise SensCrypt, a protocol for secure data storage and communication, for use by makers of affordable and lightweight personal trackers. SensCrypt thwarts not only the attacks we introduced, but also defends against powerful JTAG Read attacks. We have built Sens.io, an Arduino Uno based tracker platform, of similar capabilities but at a fraction of the cost of current solutions. On Sens.io, SensCrypt imposes a negligible write overhead and significantly reduces the end-to-end sync overhead of Fitbit and Garmin.
Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors
Venue: arXiv cs.CV 2017
Year: 2017

One of the challenges in Content-Based Image Retrieval (CBIR) is to reduce the semantic gaps between low-level features and high-level semantic concepts. In CBIR, the images are represented in the feature space and the performance of CBIR depends on the type of selected feature representation. Late fusion also known as visual words integration is applied to enhance the performance of image retrieval. The recent advances in image retrieval diverted the focus of research towards the use of binary descriptors as they are reported computationally efficient. In this paper, we aim to investigate the late fusion of Fast Retina Keypoint (FREAK) and Scale Invariant Feature Transform (SIFT). The late fusion of binary and local descriptor is selected because among binary descriptors, FREAK has shown good results in classification-based problems while SIFT is robust to translation, scaling, rotation and small distortions. The late fusion of FREAK and SIFT integrates the performance of both feature descriptors for an effective image retrieval. Experimental results and comparisons show that the proposed late fusion enhances the performances of image retrieval.
Depth-Optimal Quantum Circuit Placement for Arbitrary Topologies
Venue: arXiv cs.ET 2017
Year: 2017

A significant hurdle towards realization of practical and scalable quantum computing is to protect the quantum states from inherent noises during the computation. In physical implementation of quantum circuits, a long-distance interaction between two qubits is undesirable since, it can be interpreted as a noise. Therefore, multiple quantum technologies and quantum error correcting codes strongly require the interacting qubits to be arranged in a nearest neighbor (NN) fashion. The current literature on converting a given quantum circuit to an NN-arranged one mainly considered chained qubit topologies or Linear Nearest Neighbor (LNN) topology. However, practical quantum circuit realizations, such as Nuclear Magnetic Resonance (NMR), may not have an LNN topology. To address this gap, we consider an arbitrary qubit topology. We present an Integer Linear Programming (ILP) formulation for achieving minimal logical depth while guaranteeing the nearest neighbor arrangement between the interacting qubits. We substantiate our claim with studies on diverse network topologies and prominent quantum circuit benchmarks.
Towards a Functional System Architecture for Automated Vehicles
Venue: arXiv cs.SY 2017
Year: 2017

This paper presents a functional system architecture for an automated vehicle. It provides an overall, generic structure that is independent of a specific implementation of a particular vehicle project. Yet, it has been inspired and cross-checked with a real world automated driving implementation in the Stadtpilot project at the Technische Universität Braunschweig. The architecture entails aspects like environment and self perception, planning and control, localization, map provision, Vehicle-To-X-communication, and interaction with human operators.
Deep Residual Learning for Instrument Segmentation in Robotic Surgery
Venue: arXiv cs.CV 2017
Year: 2017

Detection, tracking, and pose estimation of surgical instruments are crucial tasks for computer assistance during minimally invasive robotic surgery. In the majority of cases, the first step is the automatic segmentation of surgical tools. Prior work has focused on binary segmentation, where the objective is to label every pixel in an image as tool or background. We improve upon previous work in two major ways. First, we leverage recent techniques such as deep residual learning and dilated convolutions to advance binary-segmentation performance. Second, we extend the approach to multi-class segmentation, which lets us segment different parts of the tool, in addition to background. We demonstrate the performance of this method on the MICCAI Endoscopic Vision Challenge Robotic Instruments dataset.