OpenRaiser/Intern-Atlas huggingface dataset:
Multifunctionality in embodied agents: Three levels of neural reuse
Venue: arXiv cs.NE 2018
Year: 2018

The brain in conjunction with the body is able to adapt to new environments and perform multiple behaviors through reuse of neural resources and transfer of existing behavioral traits. Although mechanisms that underlie this ability are not well understood, they are largely attributed to neuromodulation. In this work, we demonstrate that an agent can be multifunctional using the same sensory and motor systems across behaviors, in the absence of modulatory mechanisms. Further, we lay out the different levels at which neural reuse can occur through a dynamical filtering of the brain-body-environment system's operation: structural network, autonomous dynamics, and transient dynamics. Notably, transient dynamics reuse could only be explained by studying the brain-body-environment system as a whole and not just the brain. The multifunctional agent we present here demonstrates neural reuse at all three levels.
Scalable Downward Routing for Wireless Sensor Networks and Internet of Things Actuation
Venue: arXiv cs.NI 2018
Year: 2018

In this paper, we study the downward routing for network control/actuation in large-scale and heterogeneous wireless sensor networks (WSNs) and Internet of Things (IoT). We propose the Opportunistic Source Routing (OSR), a scalable and reliable downward routing protocol for WSNs/IoT. OSR introduces opportunistic routing into traditional source routing based on the parent set of a node's upward routing in data collection, significantly addressing the drastic link dynamics in low-power and lossy WSNs. We devise a novel adaptive Bloom filter mechanism to effectively and efficiently encode a downward source-route in OSR, which enables a significant reduction of the length of source-route field in packet header. OSR is scalable to very large-size WSN/IoT deployments, since each resource-constrained node in the network only stores the set of its direct children. The probabilistic nature of the Bloom filter passively explores opportunistic routing. Upon a delivery failure at any hop along the downward path, OSR actively performs opportunistic routing to bypass the obsolete/bad link. We demonstrate the desirable scalability of OSR against the standard RPL downward routing. We evaluate the performance of OSR via both simulations and real-world testbed experiments, in comparison with the standard RPL (both storing mode and non-storing mode), ORPL, and the representative dissemination protocol Drip. Our results show that OSR significantly outperforms RPL and ORPL in scalability and reliability. OSR also achieves significantly better energy efficiency compared to TinyRPL and Drip which are based on the same TinyOS platform as OSR implementation.
On Minrank and the Lovász Theta Function
Venue: arXiv cs.DS 2018
Year: 2018

Two classical upper bounds on the Shannon capacity of graphs are the $\vartheta$-function due to Lovász and the minrank parameter due to Haemers. We provide several explicit constructions of $n$-vertex graphs with a constant $\vartheta$-function and minrank at least $n^δ$ for a constant $δ>0$ (over various prime order fields). This implies a limitation on the $\vartheta$-function-based algorithmic approach to approximating the minrank parameter of graphs. The proofs involve linear spaces of multivariate polynomials and the method of higher incidence matrices.
Quasi-Optimal Partial Order Reduction
Venue: arXiv cs.PL 2018
Year: 2018

A dynamic partial order reduction (DPOR) algorithm is optimal when it always explores at most one representative per Mazurkiewicz trace. Existing literature suggests that the reduction obtained by the non-optimal, state-of-the-art Source-DPOR (SDPOR) algorithm is comparable to optimal DPOR. We show the first program with $\mathop{\mathcal{O}}(n)$ Mazurkiewicz traces where SDPOR explores $\mathop{\mathcal{O}}(2^n)$ redundant schedules and identify the cause of the blow-up as an NP-hard problem. Our main contribution is a new approach, called Quasi-Optimal POR, that can arbitrarily approximate an optimal exploration using a provided constant k. We present an implementation of our method in a new tool called Dpu using specialised data structures. Experiments with Dpu, including Debian packages, show that optimality is achieved with low values of k, outperforming state-of-the-art tools.
Symmetries of Quantified Boolean Formulas
Venue: arXiv cs.LO 2018
Year: 2018

While symmetries are well understood for Boolean formulas and successfully exploited in practical SAT solving, less is known about symmetries in quantified Boolean formulas (QBF). There are some works introducing adaptions of propositional symmetry breaking techniques, with a theory covering only very specific parts of QBF symmetries. We present a general framework that gives a concise characterization of symmetries of QBF. Our framework naturally incorporates the duality of universal and existential symmetries resulting in a general basis for QBF symmetry breaking.
Toward Architectural Knowledge Sustainability. New Opportunities to Extend the Longevity of Systems
Venue: arXiv cs.SE 2018
Year: 2018

Complex software systems must be maintained for years or decades, and the effort and cost to maintain them are often high, involving continuous refactoring to ensure their longevity in the face of changing requirements. In this article, we introduce the notion of architectural knowledge (AK) sustainability as a new concept to support architects dealing with the evolution of long-lived systems. Architecture sustainability refers to the ability of the architecture to endure over time with the minimum number of refactoring cycles possible. We suggest that sustainability of the AK is a function of how stable the decisions are, and we discuss a set of sustainability criteria and metrics useful to estimate the sustainability of this AK.
Notable Characteristics Search through Knowledge Graphs
Venue: arXiv cs.DB 2018
Year: 2018

Query answering routinely employs knowledge graphs to assist the user in the search process. Given a knowledge graph that represents entities and relationships among them, one aims at complementing the search with intuitive but effective mechanisms. In particular, we focus on the comparison of two or more entities and the detection of unexpected, surprising properties, called notable characteristics. Such characteristics provide intuitive explanations of the peculiarities of the selected entities with respect to similar entities. We propose a solid probabilistic approach that first retrieves entity nodes similar to the query nodes provided by the user, and then exploits distributional properties to understand whether a certain attribute is interesting or not. Our preliminary experiments demonstrate the solidity of our approach and show that we are able to discover notable characteristics that are indeed interesting and relevant for the user.
The Complex Event Recognition Group
Venue: arXiv cs.AI 2018
Year: 2018

The Complex Event Recognition (CER) group is a research team, affiliated with the National Centre of Scientific Research "Demokritos" in Greece. The CER group works towards advanced and efficient methods for the recognition of complex events in a multitude of large, heterogeneous and interdependent data streams. Its research covers multiple aspects of complex event recognition, from efficient detection of patterns on event streams to handling uncertainty and noise in streams, and machine learning techniques for inferring interesting patterns. Lately, it has expanded to methods for forecasting the occurrence of events. It was founded in 2009 and currently hosts 3 senior researchers, 5 PhD students and works regularly with under-graduate students.
Reasoning in a Hierarchical System with Missing Group Size Information
Venue: arXiv cs.AI 2018
Year: 2018

The paper analyzes the problem of judgments or preferences subsequent to initial analysis by autonomous agents in a hierarchical system where the higher level agents does not have access to group size information. We propose methods that reduce instances of preference reversal of the kind encountered in Simpson's paradox.
Ethical and Social Aspects of Self-Driving Cars
Venue: arXiv cs.CY 2018
Year: 2018

As an envisaged future of transportation, self-driving cars are being discussed from various perspectives, including social, economical, engineering, computer science, design, and ethics. On the one hand, self-driving cars present new engineering problems that are being gradually successfully solved. On the other hand, social and ethical problems are typically being presented in the form of an idealized unsolvable decision-making problem, the so-called trolley problem, which is grossly misleading. We argue that an applied engineering ethical approach for the development of new technology is what is needed; the approach should be applied, meaning that it should focus on the analysis of complex real-world engineering problems. Software plays a crucial role for the control of self-driving cars; therefore, software engineering solutions should seriously handle ethical and social considerations. In this paper we take a closer look at the regulative instruments, standards, design, and implementations of components, systems, and services and we present practical social and ethical challenges that have to be met, as well as novel expectations for software engineering.
Global Model Interpretation via Recursive Partitioning
Venue: arXiv cs.LG 2018
Year: 2018

In this work, we propose a simple but effective method to interpret black-box machine learning models globally. That is, we use a compact binary tree, the interpretation tree, to explicitly represent the most important decision rules that are implicitly contained in the black-box machine learning models. This tree is learned from the contribution matrix which consists of the contributions of input variables to predicted scores for each single prediction. To generate the interpretation tree, a unified process recursively partitions the input variable space by maximizing the difference in the average contribution of the split variable between the divided spaces. We demonstrate the effectiveness of our method in diagnosing machine learning models on multiple tasks. Also, it is useful for new knowledge discovery as such insights are not easily identifiable when only looking at single predictions. In general, our work makes it easier and more efficient for human beings to understand machine learning models.
Evaluating Compositionality in Sentence Embeddings
Venue: arXiv cs.CL 2018
Year: 2018

An important challenge for human-like AI is compositional semantics. Recent research has attempted to address this by using deep neural networks to learn vector space embeddings of sentences, which then serve as input to other tasks. We present a new dataset for one such task, `natural language inference' (NLI), that cannot be solved using only word-level knowledge and requires some compositionality. We find that the performance of state of the art sentence embeddings (InferSent; Conneau et al., 2017) on our new dataset is poor. We analyze the decision rules learned by InferSent and find that they are consistent with simple heuristics that are ecologically valid in its training dataset. Further, we find that augmenting training with our dataset improves test performance on our dataset without loss of performance on the original training dataset. This highlights the importance of structured datasets in better understanding and improving AI systems.
A Unified Implicit Dialog Framework for Conversational Search
Venue: arXiv cs.CL 2018
Year: 2018

We propose a unified Implicit Dialog framework for goal-oriented, information seeking tasks of Conversational Search applications. It aims to enable dialog interactions with domain data without replying on explicitly encoded the rules but utilizing the underlying data representation to build the components required for dialog interaction, which we refer as Implicit Dialog in this work. The proposed framework consists of a pipeline of End-to-End trainable modules. A centralized knowledge representation is used to semantically ground multiple dialog modules. An associated set of tools are integrated with the framework to gather end users' input for continuous improvement of the system. The goal is to facilitate development of conversational systems by identifying the components and the data that can be adapted and reused across many end-user applications. We demonstrate our approach by creating conversational agents for several independent domains.
Learning a Neural-network-based Representation for Open Set Recognition
Venue: arXiv cs.LG 2018
Year: 2018

Open set recognition problems exist in many domains. For example in security, new malware classes emerge regularly; therefore malware classification systems need to identify instances from unknown classes in addition to discriminating between known classes. In this paper we present a neural network based representation for addressing the open set recognition problem. In this representation instances from the same class are close to each other while instances from different classes are further apart, resulting in statistically significant improvement when compared to other approaches on three datasets from two different domains.
Bang-Bang Charging of Electrical Vehicles by Smart Grid Technology
Venue: arXiv cs.SY 2018
Year: 2018

The success of the transportation electricification in this century particularly requires the penentration of the internet of plug-in electric vehicles (PEVs) into the smart power grid. Beside the function of serving the traditional residential power demand, next-generation power grids also aim to support the internet of PEVs at the same time. The distinct difference between the traditional power demand and PEVs' power demand is that while the statistics of the former is rich enough for treating it as inelastic/known before hand, the latter is unknown until random PEVs' arrivals. Massive penentration of PEVs certainly causes the grid unpredictable fluctuation. The present paper considers the joint PEVs charging coordination and grid power generation to minimizing both of the negative impact of PEVs' integration and the cost of power generation while meeting the grid operating constraints and all parties' demand. The bang-bang PEVs charging strategy is adopted to exploit its simple implementation. By using a recently developed model predictive control (MPC) model for this problem, the online compuation is based on a predictive mixed integer nonlinear programming (MINP). A new solution computation for this optimization problem is developed. Its capacity of achieving the globally optimal solution is shown by numerical comparison between its performance and that by an off-line optimal solution.
Information Scaling Law of Deep Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

With the rapid development of Deep Neural Networks (DNNs), various network models that show strong computing power and impressive expressive power are proposed. However, there is no comprehensive informational interpretation of DNNs from the perspective of information theory. Due to the nonlinear function and the uncertain number of layers and neural units used in the DNNs, the network structure shows nonlinearity and complexity. With the typical DNNs named Convolutional Arithmetic Circuits (ConvACs), the complex DNNs can be converted into mathematical formula. Thus, we can use rigorous mathematical theory especially the information theory to analyse the complicated DNNs. In this paper, we propose a novel information scaling law scheme that can interpret the network's inner organization by information theory. First, we show the informational interpretation of the activation function. Secondly, we prove that the information entropy increases when the information is transmitted through the ConvACs. Finally, we propose the information scaling law of ConvACs through making a reasonable assumption.
Phishing Techniques in Mobile Devices
Venue: arXiv cs.CR 2018
Year: 2018

The rapid evolution in mobile devices and communication technology has increased the number of mobile device users dramatically. The mobile device has replaced many other devices and is used to perform many tasks ranging from establishing a phone call to performing critical and sensitive tasks like money payments. Since the mobile device is accompanying a person most of his time, it is highly probably that it includes personal and sensitive data for that person. The increased use of mobile devices in daily life made mobile systems an excellent target for attacks. One of the most important attacks is phishing attack in which an attacker tries to get the credential of the victim and impersonate him. In this paper, analysis of different types of phishing attacks on mobile devices is provided. Mitigation techniques - anti-phishing techniques - are also analyzed. Assessment of each technique and a summary of its advantages and disadvantages is provided. At the end, important steps to guard against phishing attacks are provided. The aim of the work is to put phishing attacks on mobile systems in light, and to make people aware of these attacks and how to avoid them
Automatic localization and decoding of honeybee markers using deep convolutional neural networks
Venue: arXiv cs.CV 2018
Year: 2018

The honeybee is a fascinating model animal to investigate how collective behavior emerges from (inter-)actions of thousands of individuals. Bees may acquire unique memories throughout their lives. These experiences affect social interactions even over large time frames. Tracking and identifying all bees in the colony over their lifetimes therefore may likely shed light on the interplay of individual differences and colony behavior. This paper proposes a software pipeline based on two deep convolutional neural networks for the localization and decoding of custom binary markers that honeybees carry from their first to the last day in their life. We show that this approach outperforms similar systems proposed in recent literature. By opening this software for the public, we hope that the resulting datasets will help advancing the understanding of honeybee collective intelligence.
Mobility as an Alternative Communication Channel: A Survey
Venue: arXiv cs.NI 2018
Year: 2018

We review the research literature investigating systems in which mobile entities can carry data while they move. These entities can be either mobile by nature (e.g., human beings and animals) or mobile by design (e.g., trains, airplanes, and cars). The movements of such entities equipped with storage capabilities create a communication channel which can help overcome the limitations or the lack of conventional data networks. Common limitations include the mismatch between the capacity offered by these networks and the traffic demand or their limited deployment owing to environmental factors. Application scenarios include offloading traffic off legacy networks for capacity improvement, bridging connectivity gaps, or deploying ad hoc networks in challenging environments for coverage enhancement.
Metric Factorization: Recommendation beyond Matrix Factorization
Venue: arXiv cs.IR 2018
Year: 2018

In the past decade, matrix factorization has been extensively researched and has become one of the most popular techniques for personalized recommendations. Nevertheless, the dot product adopted in matrix factorization based recommender models does not satisfy the inequality property, which may limit their expressiveness and lead to sub-optimal solutions. To overcome this problem, we propose a novel recommender technique dubbed as {\em Metric Factorization}. We assume that users and items can be placed in a low dimensional space and their explicit closeness can be measured using Euclidean distance which satisfies the inequality property. To demonstrate its effectiveness, we further designed two variants of metric factorization with one for rating estimation and the other for personalized item ranking. Extensive experiments on a number of real-world datasets show that our approach outperforms existing state-of-the-art by a large margin on both rating prediction and item ranking tasks.
Trade-offs Between Weak-Noise Estimation Performance and Outage Exponents in Nonlinear Modulation
Venue: arXiv cs.IT 2018
Year: 2018

We focus on the problem of modulating a parameter onto a power-limited signal transmitted over a discrete-time Gaussian channel and estimating this parameter at the receiver. Considering the well-known threshold effect in non-linear modulation systems, our approach is the following: instead of deriving upper and lower bounds on the total estimation error, which weigh both weak-noise errors and anomalous errors beyond the threshold, we separate the two kinds of errors. In particular, we derive upper and lower bounds on the best achievable trade-off between the exponential decay rate of the weak-noise expected error cost and the exponential decay rate of the probability of the anomalous error event, also referred to as the outage event. This outage event is left to be defined as part of the communication system design problem. Our achievability scheme, which is based on lattice codes, meets the lower bound at the high signal-to-noise (SNR) limit and for a certain range of trade-offs between the weak--noise error cost and the outage exponent.
Modeling of Facial Aging and Kinship: A Survey
Venue: arXiv cs.CV 2018
Year: 2018

Computational facial models that capture properties of facial cues related to aging and kinship increasingly attract the attention of the research community, enabling the development of reliable methods for age progression, age estimation, age-invariant facial characterization, and kinship verification from visual data. In this paper, we review recent advances in modeling of facial aging and kinship. In particular, we provide an up-to date, complete list of available annotated datasets and an in-depth analysis of geometric, hand-crafted, and learned facial representations that are used for facial aging and kinship characterization. Moreover, evaluation protocols and metrics are reviewed and notable experimental results for each surveyed task are analyzed. This survey allows us to identify challenges and discuss future research directions for the development of robust facial models in real-world conditions.
Transient Stability Assessment of Cascade Tripping of Renewable Sources Using SOS
Venue: arXiv cs.SY 2018
Year: 2018

There has been significant increase in penetration of renewable generation (RG) sources all over the world. Localized concentration of many such generators could initiate a cascade tripping sequence that might threaten the stability of the entire system. Understanding the impact of cascade tripping process would help the system planner identify trip sequences that must be blocked in order to increase stability. In this work, we attempt to understand the consequences of cascade tripping mechanism through a Lyapunov approach. A conservative definition for the stability region (SR) along with its estimation for a given cascading sequence using sum of squares (SOS) programming is proposed. Finally, a simple probabilistic definition of the SR is used to visualize the risk of instability and understand the impact of blocking trip sequences. A 3-machine system with significant RG penetration is used to demonstrate the idea.