OpenRaiser/Intern-Atlas huggingface dataset:
PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval
Venue: arXiv cs.CV 2017
Year: 2017

Remote sensing image retrieval(RSIR), which aims to efficiently retrieve data of interest from large collections of remote sensing data, is a fundamental task in remote sensing. Over the past several decades, there has been significant effort to extract powerful feature representations for this task since the retrieval performance depends on the representative strength of the features. Benchmark datasets are also critical for developing, evaluating, and comparing RSIR approaches. Current benchmark datasets are deficient in that 1) they were originally collected for land use/land cover classification and not image retrieval, 2) they are relatively small in terms of the number of classes as well the number of sample images per class, and 3) the retrieval performance has saturated. These limitations have severely restricted the development of novel feature representations for RSIR, particularly the recent deep-learning based features which require large amounts of training data. We therefore present in this paper, a new large-scale remote sensing dataset termed "PatternNet" that was collected specifically for RSIR. PatternNet was collected from high-resolution imagery and contains 38 classes with 800 images per class. We also provide a thorough review of RSIR approaches ranging from traditional handcrafted feature based methods to recent deep learning based ones. We evaluate over 35 methods to establish extensive baseline results for future RSIR research using the PatternNet benchmark.
Improved Methods for Computing Distances between Unordered Trees Using Integer Programming
Venue: arXiv cs.DS 2017
Year: 2017

Kondo et al. (DS 2014) proposed methods for computing distances between unordered rooted trees by transforming an instance of the distance computing problem into an instance of the integer programming problem. They showed that the tree edit distance, segmental distance, and bottom-up segmental distance problem can be respectively transformed into an integer program which has $O(nm)$ variables and $O(n^2m^2)$ constraints, where $n$ and $m$ are the number of nodes of input trees. In this work, we propose new integer programming formulations for these three distances and the bottom-up distance by applying dynamic programming approach. We divide the tree edit distance problem into $O(nm)$ subproblems each of which has only $O(n + m)$ constraints. For the other three distances, each subproblem can be reduced to a maximum weighted matching problem in a bipartite graph which can be solved in polynomial time. In order to evaluate our methods, we compare our method to the previous one due to Kondo et al. The experimental results show that the performance of our methods have been improved remarkably compared to that of the previous method.
A Decoding Approach to Reed-Solomon Codes from Their Definition
Venue: arXiv cs.IT 2017
Year: 2017

Because of their importance in applications and their quite simple definition, Reed-Solomon codes can be explained in any introductory course on coding theory. However, decoding algorithms for Reed-Solomon codes are far from being simple and it is difficult to fit them in introductory courses for undergraduates. We introduce a new decoding approach, in a self-contained presentation, which we think may be appropriate for introducing error correction of Reed-Solomon codes to nonexperts. In particular, we interpret Reed-Solomon codes by means of the degree of the interpolation polynomial of the code words and from this derive a decoding algorithm. Compared to the classical algorithms, our algorithm appears to arise more naturally from definitions and to be easier to understand. It is related to the Peterson-Gorenstein-Zierler algorithm.
Towards a Uniform Framework for Dynamic Analysis of Access Control Models
Venue: arXiv cs.CR 2017
Year: 2017

Security-critical system requirements are increasingly enforced through mandatory access control systems. These systems are controlled by security policies, highly sensitive system components, which emphasizes the paramount importance of formally verified security properties regarding policy correctness. For the class of safety-properties, addressing potential dynamic right proliferation, a number of known and tested formal analysis methods and tools already exist. Unfortunately, these methods need to be redesigned from scratch for each particular policy from a broad range of different application domains. In this paper, we seek to mitigate this problem by proposing a uniform formal framework, tailorable to a safety analysis algorithm for a specific application domain. We present a practical workflow, guided by model-based knowledge, that is capable of producing a meaningful formal safety definition along with an algorithm to heuristically analyze that safety. Our method is demonstrated based on security policies for the SELinux operating system. Keywords: Security engineering, security policies, access control systems, access control models, safety, heuristic analysis, SELinux.
Monitoring of Domain-Related Problems in Distributed Data Streams
Venue: arXiv cs.DS 2017
Year: 2017

Consider a network in which $n$ distributed nodes are connected to a single server. Each node continuously observes a data stream consisting of one value per discrete time step. The server has to continuously monitor a given parameter defined over all information available at the distributed nodes. That is, in any time step $t$, it has to compute an output based on all values currently observed across all streams. To do so, nodes can send messages to the server and the server can broadcast messages to the nodes. The objective is the minimisation of communication while allowing the server to compute the desired output. We consider monitoring problems related to the domain $D_t$ defined to be the set of values observed by at least one node at time $t$. We provide randomised algorithms for monitoring $D_t$, (approximations of) the size $|D_t|$ and the frequencies of all members of $D_t$. Besides worst-case bounds, we also obtain improved results when inputs are parameterised according to the similarity of observations between consecutive time steps. This parameterisation allows to exclude inputs with rapid and heavy changes, which usually lead to the worst-case bounds but might be rather artificial in certain scenarios.
Fair Airtime Allocation for Content Dissemination in WiFi-Direct-Based Mobile Social Networks
Venue: arXiv cs.NI 2017
Year: 2017

The vast penetration of smart mobile devices provides a unique opportunity to make mobile social networking pervasive by leveraging the feature of short-range wireless communication technologies (e.g. WiFi Direct). In this paper, we study local content dissemination in WiFi-Direct-based mobile social networks (MSNs). We propose a simple GO-coordinated dissemination strategy, as WiFi Direct does not originally support content dissemination. Due to mobility and the short transmission range, the duration of nodes in contact tends to be limited and consequently they compete for the limited airtime to disseminate their own data. Therefore, fair allocation of the limited airtime among the nodes is required. We focus on fairness in content dissemination rate, which is a key application-layer metric, rather than fairness in throughput or airtime and formulate the allocation problem as a generalized Nash bargaining game wherein the nodes bargain for a share of the limited airtime. The game is proved to have a unique optimal solution, and an algorithm with low complexity is designed to find the optimal solution. Furthermore, we propose a detailed scheduling approach to implement the optimal solution. We also present numerical results to evaluate the Nash bargaining based allocation and scheduling.
Noisy Softplus: an activation function that enables SNNs to be trained as ANNs
Venue: arXiv cs.NE 2017
Year: 2017

We extended the work of proposed activation function, Noisy Softplus, to fit into training of layered up spiking neural networks (SNNs). Thus, any ANN employing Noisy Softplus neurons, even of deep architecture, can be trained simply by the traditional algorithm, for example Back Propagation (BP), and the trained weights can be directly used in the spiking version of the same network without any conversion. Furthermore, the training method can be generalised to other activation units, for instance Rectified Linear Units (ReLU), to train deep SNNs off-line. This research is crucial to provide an effective approach for SNN training, and to increase the classification accuracy of SNNs with biological characteristics and to close the gap between the performance of SNNs and ANNs.
Comparison Between Asymmetric and Symmetric Channel-Based Authentication for MIMO Systems
Venue: arXiv cs.IT 2017
Year: 2017

Authentication is a key element of security, by which a receiver confirms the sender identity of a message. Typical approaches include either key-based authentication at the application layer or physical layer authentication (PLA), where a message is considered authentic if it appears to have gone through the legitimate channel. In both cases a source of randomness is needed, whereas for PLA the random nature of the communication channel is exploited. In this paper we compare the various approaches using in all cases the channel as a source of randomness. We consider a multiple-input multiple-output (MIMO) system with a finite number of antennas. Simple auto-regressive (AR) models for its evolution as well as the relation of the legitimate and attacker channel are considered. In this setting the attacker can either predict the key used for key-based authentication or forge the channel estimated at the legitimate receiver for PLA. The analysis includes both symmetric and asymmetric key-based authentication. We compare the schemes in terms of false alarm and missed detection probability and we outline best attack strategies.
DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self
Venue: arXiv cs.AI 2017
Year: 2017

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the-art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users.
The in-town monitoring system for ambulance dispatch centre
Venue: arXiv cs.CY 2017
Year: 2017

The paper presents the vehicles integrated monitoring system giving priorities for emergency vehicles. The described system exploits the data gathered by: geographical positioning systems and geographical information systems. The digital maps and roadside cameras provide the dispatchers with aims for in town ambulances traffic management. The method of vehicles positioning in the city network and algorithms for ambulances recognition by image processing techniques have been discussed in the paper. These priorities are needed for an efficient life-saving actions that require the real-time controlling strategies.
NotiMind: Utilizing Responses to Smart Phone Notifications as Affective sensors
Venue: arXiv cs.CY 2017
Year: 2017

Today's mobile phone users are faced with large numbers of notifications on social media, ranging from new followers on Twitter and emails to messages received from WhatsApp and Facebook. These digital alerts continuously disrupt activities through instant calls for attention. This paper examines closely the way everyday users interact with notifications and their impact on users' emotion. Fifty users were recruited to download our application NotiMind and use it over a five-week period. Users' phones collected thousands of social and system notifications along with affect data collected via self-reported PANAS tests three times a day. Results showed a noticeable correlation between positive affective measures and keyboard activities. When large numbers of Post and Remove notifications occur, a corresponding increase in negative affective measures is detected. Our predictive model has achieved a good accuracy level using three different classifiers "in the wild" (F-measure 74-78% within-subject model, 72-76% global model). Our findings show that it is possible to automatically predict when people are experiencing positive, neutral or negative affective states based on interactions with notifications. We also show how our findings open the door to a wide range of applications in relation to emotion awareness on social and mobile communication.
LO-FAT: Low-Overhead Control Flow ATtestation in Hardware
Venue: arXiv cs.CR 2017
Year: 2017

Attacks targeting software on embedded systems are becoming increasingly prevalent. Remote attestation is a mechanism that allows establishing trust in embedded devices. However, existing attestation schemes are either static and cannot detect control-flow attacks, or require instrumentation of software incurring high performance overheads. To overcome these limitations, we present LO-FAT, the first practical hardware-based approach to control-flow attestation. By leveraging existing processor hardware features and commonly-used IP blocks, our approach enables efficient control-flow attestation without requiring software instrumentation. We show that our proof-of-concept implementation based on a RISC-V SoC incurs no processor stalls and requires reasonable area overhead.
Six Challenges for Neural Machine Translation
Venue: arXiv cs.CL 2017
Year: 2017

We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. We show both deficiencies and improvements over the quality of phrase-based statistical machine translation.
On the Secure Energy Efficiency of TAS/MRC with Relaying and Jamming Strategies (Extended Version)
Venue: arXiv cs.CR 2017
Year: 2017

In this paper we investigate the secure energy efficiency (SEE) in a cooperative scenario where all nodes are equipped with multiple antennas. Moreover, we employ secrecy rate and power allocation at Alice and at the relay in order to maximize the SEE, subjected to a constraint in terms of a minimal required secrecy outage probability. Only the channel state information (CSI) with respect to the legitimate nodes is available. Then, we compare the Artificial-Noise (AN) scheme with CSI-Aided Decode-and-Forward (CSI-DF), which exploits the CSI to choose the best communication path (direct or cooperative). Our results show that CSI-DF outperforms AN in terms of SEE in most scenarios, except when Eve is closer to the relay or with the increase of antennas at Eve. Additionally, we also show that the maximization of SEE implies in an optimal number of antennas to be used at each node, which is due to the trade-off between secure throughput and power consumption.
A New Probabilistic Algorithm for Approximate Model Counting
Venue: arXiv cs.AI 2017
Year: 2017

Constrained counting is important in domains ranging from artificial intelligence to software analysis. There are already a few approaches for counting models over various types of constraints. Recently, hashing-based approaches achieve both theoretical guarantees and scalability, but still rely on solution enumeration. In this paper, a new probabilistic polynomial time approximate model counter is proposed, which is also a hashing-based universal framework, but with only satisfiability queries. A variant with a dynamic stopping criterion is also presented. Empirical evaluation over benchmarks on propositional logic formulas and SMT(BV) formulas shows that the approach is promising.
Deep Control - a simple automatic gain control for memory efficient and high performance training of deep convolutional neural networks
Venue: arXiv cs.CV 2017
Year: 2017

Training a deep convolutional neural net typically starts with a random initialisation of all filters in all layers which severely reduces the forward signal and back-propagated error and leads to slow and sub-optimal training. Techniques that counter that focus on either increasing the signal or increasing the gradients adaptively but the model behaves very differently at the beginning of training compared to later when stable pathways through the net have been established. To compound this problem the effective minibatch size varies greatly between layers at different depths and between individual filters as activation sparsity typically increases with depth leading to a reduction in effective learning rate since gradients may superpose rather than add and this further compounds the covariate shift problem as deeper neurons are less able to adapt to upstream shift. Proposed here is a method of automatic gain control of the signal built into each convolutional neuron that achieves equivalent or superior performance than batch normalisation and is compatible with single sample or minibatch gradient descent. The same model is used both for training and inference. The technique comprises a scaled per sample map mean subtraction from the raw convolutional filter output followed by scaling of the difference.
Recurrent Inference Machines for Solving Inverse Problems
Venue: arXiv cs.NE 2017
Year: 2017

Much of the recent research on solving iterative inference problems focuses on moving away from hand-chosen inference algorithms and towards learned inference. In the latter, the inference process is unrolled in time and interpreted as a recurrent neural network (RNN) which allows for joint learning of model and inference parameters with back-propagation through time. In this framework, the RNN architecture is directly derived from a hand-chosen inference algorithm, effectively limiting its capabilities. We propose a learning framework, called Recurrent Inference Machines (RIM), in which we turn algorithm construction the other way round: Given data and a task, train an RNN to learn an inference algorithm. Because RNNs are Turing complete [1, 2] they are capable to implement any inference algorithm. The framework allows for an abstraction which removes the need for domain knowledge. We demonstrate in several image restoration experiments that this abstraction is effective, allowing us to achieve state-of-the-art performance on image denoising and super-resolution tasks and superior across-task generalization.
DAReing to reduce the annotation overheads of verified programs
Venue: arXiv cs.SE 2017
Year: 2017

Modern program verifiers use the same uniform program text to both specify and implement programs. The program text is also used to provide the necessary guidance to ensure that the program satisfies its specification. The amount of guidance required is often called the annotation overhead. This can be high and is often seen as a hindrance for wider use of program verifiers, as development time is increased and the guidance may obfuscate the program text. In this paper we introduce the DARe tool, which automatically removes as much unnecessary guidance as possible for the Dafny program verifier. The tool is integrated with the Dafny IDE. To evaluate DARe, we apply it to 252 programs from the Dafny library and analyse the degree to which it is able to remove unnecessary guidance. Our results are very encouraging as a staggering 88% of the guidance can be removed.
Identifying Condition-Action Statements in Medical Guidelines Using Domain-Independent Features
Venue: arXiv cs.CL 2017
Year: 2017

This paper advances the state of the art in text understanding of medical guidelines by releasing two new annotated clinical guidelines datasets, and establishing baselines for using machine learning to extract condition-action pairs. In contrast to prior work that relies on manually created rules, we report experiment with several supervised machine learning techniques to classify sentences as to whether they express conditions and actions. We show the limitations and possible extensions of this work on text mining of medical guidelines.
A Hybrid Observer for a Distributed Linear System with a Changing Neighbor Graph
Venue: arXiv cs.SY 2017
Year: 2017

A hybrid observer is described for estimating the state of an $m>0$ channel, $n$-dimensional, continuous-time, distributed linear system of the form $\dot{x} = Ax,\;y_i = C_ix,\;i\in\{1,2,\ldots, m\}$. The system's state $x$ is simultaneously estimated by $m$ agents assuming each agent $i$ senses $y_i$ and receives appropriately defined data from each of its current neighbors. Neighbor relations are characterized by a time-varying directed graph $\mathbb{N}(t)$ whose vertices correspond to agents and whose arcs depict neighbor relations. Agent $i$ updates its estimate $x_i$ of $x$ at "event times" $t_1,t_2,\ldots $ using a local observer and a local parameter estimator. The local observer is a continuous time linear system whose input is $y_i$ and whose output $w_i$ is an asymptotically correct estimate of $L_ix$ where $L_i$ a matrix with kernel equaling the unobservable space of $(C_i,A)$. The local parameter estimator is a recursive algorithm designed to estimate, prior to each event time $t_j$, a constant parameter $p_j$ which satisfies the linear equations $w_k(t_j-τ) = L_kp_j+μ_k(t_j-τ),\;k\in\{1,2,\ldots,m\}$, where $τ$ is a small positive constant and $μ_k$ is the state estimation error of local observer $k$. Agent $i$ accomplishes this by iterating its parameter estimator state $z_i$, $q$ times within the interval $[t_j-τ, t_j)$, and by making use of the state of each of its neighbors' parameter estimators at each iteration. The updated value of $x_i$ at event time $t_j$ is then $x_i(t_j) = e^{Aτ}z_i(q)$. Subject to the assumptions that (i) the neighbor graph $\mathbb{N}(t)$ is strongly connected for all time, (ii) the system whose state is to be estimated is jointly observable, (iii) $q$ is sufficiently large, it is shown that each estimate $x_i$ converges to $x$ exponentially fast as $t\rightarrow \infty$ at a rate which can be controlled.