OpenRaiser/Intern-Atlas huggingface dataset:
Paperclickers: Affordable Solution for Classroom Response Systems
Venue: arXiv cs.CY 2017
Year: 2017

We propose a low-cost classroom response system requiring a single mobile device for the teacher and cards with printed codes for the students. We aim at broadening the adoption of active learning techniques in developing countries, offering a tool for easy implementation. We embody the solution as a smartphone application, describing the development history, pitfalls, and lessons learned that might be helpful for other small academic teams. We also described the results of the first round usability tests we performed on the first prototype, and how the affected the current version of the software. A beta release version is currently available for the public at large.
On Reusing Pilots Among Interfering Cells in Massive MIMO
Venue: arXiv cs.IT 2017
Year: 2017

Pilot contamination, caused by the reuse of pilots among interfering cells, remains as a significant obstacle that limits the performance of massive multi-input multi-output antenna systems. To handle this problem, less aggressive reuse of pilots involving allocation of additional pilots for interfering users is closely examined in this paper. Hierarchical pilot reuse methods are proposed, which effectively mitigate pilot contamination and increase the net throughput of the system. Among the suggested hierarchical pilot reuse schemes, the optimal way of assigning pilots to different users is obtained in a closed-form solution which maximizes the net sum-rate in a given coherence time. Simulation results confirm that when the ratio of the channel coherence time to the number of users in each cell is sufficiently large, less aggressive reuse of pilots yields significant performance advantage relative to the case where all cells reuse the same pilot set.
Linear Prediction based Data Detection of Convolutional Coded DQPSK in SIMO-OFDM
Venue: arXiv cs.IT 2017
Year: 2017

Data detection of convolutional coded differential quaternary phase shift keyed (DQPSK) signals using a predictive Viterbi algorithm (VA) based receiver, is presented for single input, multiple output - orthogonal frequency division multiplexed (OFDM) systems. The receiver has both error correcting capability and also the ability to perform channel estimation (prediction). The predictive VA operates on a supertrellis with just $S_{\mathrm{ST}}=S_{\mathrm{E}}\times 2^{P-1}$ states instead of $S_{\mathrm{ST}}=S_{\mathrm{E}}\times 2^{P}$ states, where the complexity reduction is achieved by using the concept of isometry (here $S_{\mathrm{E}}$ denotes the number of states in the encoder trellis and $P$ denotes the prediction order). Though the linear prediction based data detection in turbo coded OFDM and the bit interleaved coded (BIC) OFDM systems perform better than the proposed approach in terms of bit error rate (BER) for a given signal to noise ratio (SNR), the decoding delay of the proposed approach is significantly lower than that of the BIC and the turbo coded OFDM systems.
An automatic deep learning approach for coronary artery calcium segmentation
Venue: arXiv cs.CV 2017
Year: 2017

Coronary artery calcium (CAC) is a significant marker of atherosclerosis and cardiovascular events. In this work we present a system for the automatic quantification of calcium score in ECG-triggered non-contrast enhanced cardiac computed tomography (CT) images. The proposed system uses a supervised deep learning algorithm, i.e. convolutional neural network (CNN) for the segmentation and classification of candidate lesions as coronary or not, previously extracted in the region of the heart using a cardiac atlas. We trained our network with 45 CT volumes; 18 volumes were used to validate the model and 56 to test it. Individual lesions were detected with a sensitivity of 91.24%, a specificity of 95.37% and a positive predicted value (PPV) of 90.5%; comparing calcium score obtained by the system and calcium score manually evaluated by an expert operator, a Pearson coefficient of 0.983 was obtained. A high agreement (Cohen's k = 0.879) between manual and automatic risk prediction was also observed. These results demonstrated that convolutional neural networks can be effectively applied for the automatic segmentation and classification of coronary calcifications.
Theoretical Computer Science for the Working Category Theorist
Venue: arXiv cs.LO 2017
Year: 2017

Theoretical computer science discusses foundational issues about computations. It asks and answers questions such as "What is a computation?", "What is computable?", "What is efficiently computable?","What is information?", "What is random?", "What is an algorithm?", etc. We will present many of the major themes and theorems with the basic language of category theory. Surprisingly, many interesting theorems and concepts of theoretical computer science are easy consequences of functoriality and composition when you look at the right categories and functors connecting them.
Grand Challenges of Traceability: The Next Ten Years
Venue: arXiv cs.SE 2017
Year: 2017

In 2007, the software and systems traceability community met at the first Natural Bridge symposium on the Grand Challenges of Traceability to establish and address research goals for achieving effective, trustworthy, and ubiquitous traceability. Ten years later, in 2017, the community came together to evaluate a decade of progress towards achieving these goals. These proceedings document some of that progress. They include a series of short position papers, representing current work in the community organized across four process axes of traceability practice. The sessions covered topics from Trace Strategizing, Trace Link Creation and Evolution, Trace Link Usage, real-world applications of Traceability, and Traceability Datasets and benchmarks. Two breakout groups focused on the importance of creating and sharing traceability datasets within the research community, and discussed challenges related to the adoption of tracing techniques in industrial practice. Members of the research community are engaged in many active, ongoing, and impactful research projects. Our hope is that ten years from now we will be able to look back at a productive decade of research and claim that we have achieved the overarching Grand Challenge of Traceability, which seeks for traceability to be always present, built into the engineering process, and for it to have "effectively disappeared without a trace". We hope that others will see the potential that traceability has for empowering software and systems engineers to develop higher-quality products at increasing levels of complexity and scale, and that they will join the active community of Software and Systems traceability researchers as we move forward into the next decade of research.
Island Loss for Learning Discriminative Features in Facial Expression Recognition
Venue: arXiv cs.CV 2017
Year: 2017

Over the past few years, Convolutional Neural Networks (CNNs) have shown promise on facial expression recognition. However, the performance degrades dramatically under real-world settings due to variations introduced by subtle facial appearance changes, head pose variations, illumination changes, and occlusions. In this paper, a novel island loss is proposed to enhance the discriminative power of the deeply learned features. Specifically, the IL is designed to reduce the intra-class variations while enlarging the inter-class differences simultaneously. Experimental results on four benchmark expression databases have demonstrated that the CNN with the proposed island loss (IL-CNN) outperforms the baseline CNN models with either traditional softmax loss or the center loss and achieves comparable or better performance compared with the state-of-the-art methods for facial expression recognition.
Random Projection and Its Applications
Venue: arXiv cs.LG 2017
Year: 2017

Random Projection is a foundational research topic that connects a bunch of machine learning algorithms under a similar mathematical basis. It is used to reduce the dimensionality of the dataset by projecting the data points efficiently to a smaller dimensions while preserving the original relative distance between the data points. In this paper, we are intended to explain random projection method, by explaining its mathematical background and foundation, the applications that are currently adopting it, and an overview on its current research perspective.
Towards Agent-Based Model Specification in Smart Grid: A Cognitive Agent-based Computing Approach
Venue: arXiv cs.MA 2017
Year: 2017

A smart grid can be considered as a complex network where each node represents a generation unit or a consumer. Whereas links can be used to represent transmission lines. One way to study complex systems is by using the agent-based modeling (ABM) paradigm. An ABM is a way of representing a complex system of autonomous agents interacting with each other. Previously, a number of studies have been presented in the smart grid domain making use of the ABM paradigm. However, to the best of our knowledge, none of these studies have focused on the specification aspect of ABM. An ABM specification is important not only for understanding but also for replication of the model. In this study, we focus on development as well as specification of ABM for smart grid. We propose an ABM by using a combination of agent-based and complex network-based approaches. For ABM specification, we use ODD and DREAM specification approaches. We analyze these two specification approaches qualitatively as well as quantitatively. Extensive experiments demonstrate that DREAM is a most useful approach as compared with ODD for modeling as well as for replication of models for smart grid.
Network of Recurrent Neural Networks
Venue: arXiv cs.NE 2017
Year: 2017

We describe a class of systems theory based neural networks called "Network Of Recurrent neural networks" (NOR), which introduces a new structure level to RNN related models. In NOR, RNNs are viewed as the high-level neurons and are used to build the high-level layers. More specifically, we propose several methodologies to design different NOR topologies according to the theory of system evolution. Then we carry experiments on three different tasks to evaluate our implementations. Experimental results show our models outperform simple RNN remarkably under the same number of parameters, and sometimes achieve even better results than GRU and LSTM.
BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning
Venue: arXiv cs.PF 2017
Year: 2017

An ever increasing number of configuration parameters are provided to system users. But many users have used one configuration setting across different workloads, leaving untapped the performance potential of systems. A good configuration setting can greatly improve the performance of a deployed system under certain workloads. But with tens or hundreds of parameters, it becomes a highly costly task to decide which configuration setting leads to the best performance. While such task requires the strong expertise in both the system and the application, users commonly lack such expertise. To help users tap the performance potential of systems, we present BestConfig, a system for automatically finding a best configuration setting within a resource limit for a deployed system under a given application workload. BestConfig is designed with an extensible architecture to automate the configuration tuning for general systems. To tune system configurations within a resource limit, we propose the divide-and-diverge sampling method and the recursive bound-and-search algorithm. BestConfig can improve the throughput of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce the running time of Hive join job by about 50% and that of Spark join job by about 80%, solely by configuration adjustment.
A model for the analysis of security policies in service function chains
Venue: arXiv cs.CR 2017
Year: 2017

Two emerging architectural paradigms, i.e., Software Defined Networking (SDN) and Network Function Virtualization (NFV), enable the deployment and management of Service Function Chains (SFCs). A SFC is an ordered sequence of abstract Service Functions (SFs), e.g., firewalls, VPN-gateways,traffic monitors, that packets have to traverse in the route from source to destination. While this appealing solution offers significant advantages in terms of flexibility, it also introduces new challenges such as the correct configuration and ordering of SFs in the chain to satisfy overall security requirements. This paper presents a formal model conceived to enable the verification of correct policy enforcements in SFCs. Software tools based on the model can then be designed to cope with unwanted network behaviors (e.g., security flaws) deriving from incorrect interactions of SFs in the same SFC.
Automatic Streaming Segmentation of Stereo Video Using Bilateral Space
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we take advantage of binocular camera and propose an unsupervised algorithm based on semi-supervised segmentation algorithm and extracting foreground part efficiently. We creatively embed depth information into bilateral grid in the graph cut model and achieve considerable segmenting accuracy in the case of no user input. The experi- ment approves the high precision, time efficiency of our algorithm and its adaptation to complex natural scenario which is significant for practical application.
Automated fixing of access policy implementation in Industrial Networked Systems
Venue: arXiv cs.CR 2017
Year: 2017

Access control (AC) is the core of every architectural solution for information security. Indeed, no effective protection scheme can abstract from the careful design of access control policies, and infrastructures underlying modern Industrial Networked Systems (INSs) are not exceptions from this point of view. This paper presents a comprehensive framework for INS access control. The proposed approach enables the description of both positive and negative AC policies, by applying the Role Based Access Control (RBAC) paradigm to typical INS implementations, while taking into account different levels of abstraction. Suitable techniques are adopted to check whether or not policies are correctly implemented in the system (verification). When conflicts are detected, possible (re)assignments of credentials to the system users are automatically computed, that can be adopted to correct anomalies (conflict resolution).
On some difficulties in the addition of trapezoidal ordered fuzzy numbers
Venue: arXiv cs.DM 2017
Year: 2017

At the first, we revise the Kosinski definition of the sum of ordered fuzzy numbers. The associativity of revised sum is investigated here. In addition, we show that the multiple revised sum of finite sequence of trapezoidal ordered fuzzy numbers depends on its summands ordering.
CTD: Fast, Accurate, and Interpretable Method for Static and Dynamic Tensor Decompositions
Venue: arXiv cs.NA 2017
Year: 2017

How can we find patterns and anomalies in a tensor, or multi-dimensional array, in an efficient and directly interpretable way? How can we do this in an online environment, where a new tensor arrives each time step? Finding patterns and anomalies in a tensor is a crucial problem with many applications, including building safety monitoring, patient health monitoring, cyber security, terrorist detection, and fake user detection in social networks. Standard PARAFAC and Tucker decomposition results are not directly interpretable. Although a few sampling-based methods have previously been proposed towards better interpretability, they need to be made faster, more memory efficient, and more accurate. In this paper, we propose CTD, a fast, accurate, and directly interpretable tensor decomposition method based on sampling. CTD-S, the static version of CTD, provably guarantees a high accuracy that is 17 ~ 83x more accurate than that of the state-of-the-art method. Also, CTD-S is made 5 ~ 86x faster, and 7 ~ 12x more memory-efficient than the state-of-the-art method by removing redundancy. CTD-D, the dynamic version of CTD, is the first interpretable dynamic tensor decomposition method ever proposed. Also, it is made 2 ~ 3x faster than already fast CTD-S by exploiting factors at previous time step and by reordering operations. With CTD, we demonstrate how the results can be effectively interpreted in the online distributed denial of service (DDoS) attack detection.
LinXGBoost: Extension of XGBoost to Generalized Local Linear Models
Venue: arXiv cs.LG 2017
Year: 2017

XGBoost is often presented as the algorithm that wins every ML competition. Surprisingly, this is true even though predictions are piecewise constant. This might be justified in high dimensional input spaces, but when the number of features is low, a piecewise linear model is likely to perform better. XGBoost was extended into LinXGBoost that stores at each leaf a linear model. This extension, equivalent to piecewise regularized least-squares, is particularly attractive for regression of functions that exhibits jumps or discontinuities. Those functions are notoriously hard to regress. Our extension is compared to the vanilla XGBoost and Random Forest in experiments on both synthetic and real-world data sets.
Route Optimization of Electric Vehicles based on Dynamic Wireless Charging
Venue: arXiv cs.SY 2017
Year: 2017

One of the barriers to adoption of Electric Vehicles (EVs) is the anxiety around the limited driving range. Recent proposals have explored charging EVs on the move, using dynamic wireless charging which enables power exchange between the vehicle and the grid while the vehicle is moving. In this article, we focus on the intelligent routing of EVs in need of charging so that they can make most efficient use of the so-called {\it Mobile Energy Disseminators} (MEDs) which operates as mobile charging stations. We present a method for routing EVs around MEDs on the road network, which is based on constraint logic programming and optimisation using a graph-based shortest path algorithm. The proposed method exploits Inter-Vehicle (IVC) communications in order to eco-route electric vehicles. We argue that combining modern communications between vehicles and state of the art technologies on energy transfer, the driving range of EVs can be extended without the need for larger batteries or overtly costly infrastructure. We present extensive simulations in city conditions that show the driving range and consequently the overall travel time of electric vehicles is improved with intelligent routing in the presence of MEDs.
Disentangled Representations via Synergy Minimization
Venue: arXiv cs.LG 2017
Year: 2017

Scientists often seek simplified representations of complex systems to facilitate prediction and understanding. If the factors comprising a representation allow us to make accurate predictions about our system, but obscuring any subset of the factors destroys our ability to make predictions, we say that the representation exhibits informational synergy. We argue that synergy is an undesirable feature in learned representations and that explicitly minimizing synergy can help disentangle the true factors of variation underlying data. We explore different ways of quantifying synergy, deriving new closed-form expressions in some cases, and then show how to modify learning to produce representations that are minimally synergistic. We introduce a benchmark task to disentangle separate characters from images of words. We demonstrate that Minimally Synergistic (MinSyn) representations correctly disentangle characters while methods relying on statistical independence fail.
Artificial Bee Colony-based Adaptive Position Control of Electrohydraulic Servo Systems with Parameter Uncertainty
Venue: arXiv cs.SY 2017
Year: 2017

In this paper, a robust adaptive backstepping-based controller is developed for positioning the spool valve of Electro-Hydraulic Servo System (EHSS) with parameter fluctuations. Artificial Bee Colony (ABC) algorithm is utilized to drive the parameters of the proposed controller to a good neighbourhood of the solution space. The optimization problem is formulated such that both the tracking error and control signal are minimized concurrently. The results show that the proposed controller guarantees the uniform ultimate boundedness of the tracking error and control signal. Moreover, illustrative simulations validate the potentials and robustness of the proposed schemes in the presence of uncertainties. The proposed controller is also compared with sliding mode control.
Involving Users in the Design of a Serious Game for Security Questions Education
Venue: arXiv cs.CR 2017
Year: 2017

When using security questions most users still trade-off security for the convenience of memorability. This happens because most users find strong answers to security questions difficult to remember. Previous research in security education was successful in motivating users to change their behaviour towards security issues, through the use of serious games (i.e. games designed for a primary purpose other than pure entertainment). Hence, in this paper we evaluate the design of a serious game, to investigate the features and functionalities that users would find desirable in a game that aims to educate them to provide strong and memorable answers to security questions. Our findings reveal that: (1) even for security education games, rewards seem to motivate users to have a better learning experience; (2) functionalities which contain a social element (e.g. getting help from other players) do not seem appropriate for serious games related to security questions, because users fear that their acquaintances could gain access to their security questions; (3) even users who do not usually play games would seem to prefer to play security education games on a mobile device.
A Semantics Comparison Workbench for a Concurrent, Asynchronous, Distributed Programming Language
Venue: arXiv cs.SE 2017
Year: 2017

A number of high-level languages and libraries have been proposed that offer novel and simple to use abstractions for concurrent, asynchronous, and distributed programming. The execution models that realise them, however, often change over time---whether to improve performance, or to extend them to new language features---potentially affecting behavioural and safety properties of existing programs. This is exemplified by SCOOP, a message-passing approach to concurrent object-oriented programming that has seen multiple changes proposed and implemented, with demonstrable consequences for an idiomatic usage of its core abstraction. We propose a semantics comparison workbench for SCOOP with fully and semi-automatic tools for analysing and comparing the state spaces of programs with respect to different execution models or semantics. We demonstrate its use in checking the consistency of properties across semantics by applying it to a set of representative programs, and highlighting a deadlock-related discrepancy between the principal execution models of SCOOP. Furthermore, we demonstrate the extensibility of the workbench by generalising the formalisation of an execution model to support recently proposed extensions for distributed programming. Our workbench is based on a modular and parameterisable graph transformation semantics implemented in the GROOVE tool. We discuss how graph transformations are leveraged to atomically model intricate language abstractions, how the visual yet algebraic nature of the model can be used to ascertain soundness, and highlight how the approach could be applied to similar languages.
DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
Venue: arXiv cs.CL 2017
Year: 2017

We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems.