OpenRaiser/Intern-Atlas huggingface dataset:
Beamforming Design for Full-Duplex Two-Way Amplify-and-Forward MIMO Relay
Venue: arXiv cs.IT 2016
Year: 2016

We consider the full-duplex (FD) two-way amplify-and-forward relay system with imperfect cancelation of loopback self-interference (SI) and investigate joint design of relay and receive beamforming for minimizing the mean square error under a relay transmit power constraint. Due to loopback channel estimation error and limitation of analog-to-digital converter, the loopback SI cannot be completely canceled. Multiple antennas at the relay can help loopback SI suppression but beamforming is required to balance between the residual SI suppression and the desired signal transmission. Moreover, the relay beamforming matrix should be updated every time slot because the residual SI in the previous time slot is amplified by the current beamforming matrix and added to the received signals from the two sources in the current time slot. We derive the optimally balanced relay beamforming and receive beamforming matrices in closed form based on minimum mean square error, taking into account the propagation of the residual loopback SI from the first to the current time slot. We also propose beamforming design using only the channels of the m latest time slots, not from the first time slot. Based on our numerical results, we also identify when FD is beneficial and propose selection between FD and half-duplex according to signal-to-noise ratio and interference-to-noise ratio.
Mitigating Data Exfiltration in Storage-as-a-Service Clouds
Venue: arXiv cs.CR 2016
Year: 2016

Existing processes and methods for incident handling are geared towards infrastructures and operational models that will be increasingly outdated by cloud computing. Research has shown that to adapt incident handling to cloud computing environments, cloud customers must establish clarity about their requirements on Cloud Service Providers (CSPs) for successful handling of incidents and contract CSPs accordingly. Secondly, CSPs must strive to support these requirements and mirror them in their Service Level Agreements. Intrusion Detection Systems (IDS) have been used widely to detect malicious behaviors in network communication and hosts. Facing new application scenarios in Cloud Computing, the IDS approaches yield several problems since the operator of the IDS should be the user, not the administrator of the Cloud infrastructure. Cloud providers need to enable possibilities to deploy and configure IDS for the user - which poses its own challenges. Current research and commercial solutions primarily focus on protecting against Denial of Service attacks and attacks against the Cloud's virtual infrastructure. To counter these challenges, we propose a capability that aims to both detect and prevent the potential of data exfiltration by using a novel deception-based methodology. We also introduce a method of increasing the data protection level based on various threat conditions.
The Apps You Use Bring The Blogs to Follow
Venue: arXiv cs.IR 2016
Year: 2016

We tackle the blog recommendation problem in Tumblr for mobile users in this paper. Blog recommendation is challenging since most mobile users would suffer from the cold start when there are only a limited number of blogs followed by the user. Specifically to address this problem in the mobile domain, we take into account mobile apps, which typically provide rich information from the users. Based on the assumption that the user interests can be reflected from their app usage patterns, we propose to exploit the app usage data for improving blog recommendation. Building on the state-of-the-art recommendation framework, Factorization Machines (FM), we implement app-based FM that integrates app usage data with the user-blog follow relations. In this approach the blog recommendation is generated not only based on the blogs that the user followed before, but also the apps that the user has often used. We demonstrate in a series of experiments that app-based FM can outperform other alternative approaches to a significant extent. Our experimental results also show that exploiting app usage information is particularly effective for improving blog recommendation quality for cold start users.
The kernel method for lattice paths below a line of rational slope
Venue: arXiv cs.DM 2016
Year: 2016

We analyse some enumerative and asymptotic properties of lattice paths below a line of rational slope. We illustrate our approach with Dyck paths under a line of slope $2/5$. This answers Knuth's problem #4 from his "Flajolet lecture" during the conference "Analysis of Algorithms" (AofA'2014) in Paris in June 2014. Our approach extends the work of Banderier and Flajolet for asymptotics and enumeration of directed lattice paths to the case of generating functions involving several dominant singularities, and has applications to a full class of problems involving some "periodicities". A key ingredient in the proof is the generalization of an old trick by Knuth himself (for enumerating permutations sortable by a stack), promoted by Flajolet and others as the "kernel method". All the corresponding generating functions are algebraic, and they offer some new combinatorial identities, which can also be tackled in the A=B spirit of Wilf-Zeilberger-Petkovsek. We show how to obtain similar results for any rational slope. An interesting case is e.g. Dyck paths below the slope $2/3$ (this corresponds to the so-called Duchon's club model), for which we solve a conjecture related to the asymptotics of the area below such lattice paths. Our work also gives access to lattice paths below an irrational slope (e.g. Dyck paths below $y=x/\sqrt{2}$), a problem that we study in a companion article.
Privacy Knowledge Modelling for Internet of Things: A Look Back
Venue: arXiv cs.CY 2016
Year: 2016

Internet of Things (IoT) and cloud computing together give us the ability to sense, collect, process, and analyse data so we can use them to better understand behaviours, habits, preferences and life patterns of users and lead them to consume resources more efficiently. In such knowledge discovery activities, privacy becomes a significant challenge due to the extremely personal nature of the knowledge that can be derived from the data and the potential risks involved. Therefore, understanding the privacy expectations and preferences of stakeholders is an important task in the IoT domain. In this paper, we review how privacy knowledge has been modelled and used in the past in different domains. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT. Finally, we discuss major research challenges and opportunities.
Probabilistic Human Mobility Model in Indoor Environment
Venue: arXiv cs.HC 2016
Year: 2016

Understanding human mobility is important for the development of intelligent mobile service robots as it can provide prior knowledge and predictions of human distribution for robot-assisted activities. In this paper, we propose a probabilistic method to model human motion behaviors which is determined by both internal and external factors in an indoor environment. While the internal factors are represented by the individual preferences, aims and interests, the external factors are indicated by the stimulation of the environment. We model the randomness of human macro-level movement, e.g., the probability of visiting a specific place and staying time, under the Bayesian framework, considering the influence of both internal and external variables. We use two case studies in a shopping mall and in a college student dorm building to show the effectiveness of our proposed probabilistic human mobility model. Real surveillance camera data are used to validate the proposed model together with survey data in the case study of student dorm.
E-Embargoes: Discouraging the Deployment of Traffic Manipulating Boxes With Economic Incentives
Venue: arXiv cs.CR 2016
Year: 2016

An increasing number of systems have been proposed or deployed to the transit core of the Internet with the goal of observing and manipulating traffic in flight, systems we term Traffic Manipulating Boxes. Examples of these include: decoy routing systems, surveillance infrastructure like the NSA's alleged QUANTUM project, and traffic shaping middleboxes. In this work, we examine a new approach that a routing capable adversary might take to resisting these systems: the use of economic pressure to incentivize ISPs to remove them. Rather than directly attacking the availability of these systems, our attack inflicts economic losses, in the form of reduced transit revenue, on ISPs that deploy them, while at the same time incentivizing ISPs that do not. We alter and expand upon previous routing around decoys attack of Schuchard et al., by adjusting the priority given to avoiding TMBs. This reduces or eliminates the key costs faced by routing capable adversary while maintaining the effectiveness of the attack. Additionally, we show that since the flow of traffic on the Internet is directly related to the flow of cash between ISPs, a routing capable adversary is actually a powerful economic adversary. Our findings show that by preferentially using routes which are free of TMBs, some routing capable adversaries can inflict in excess of a billion dollars in annual revenue losses.
A Benes Based NoC Switching Architecture for Mixed Criticality Embedded Systems
Venue: arXiv cs.AR 2016
Year: 2016

Multi-core, Mixed Criticality Embedded (MCE) real-time systems require high timing precision and predictability to guarantee there will be no interference between tasks. These guarantees are necessary in application areas such as avionics and automotive, where task interference or missed deadlines could be catastrophic, and safety requirements are strict. In modern multi-core systems, the interconnect becomes a potential point of uncertainty, introducing major challenges in proving behaviour is always within specified constraints, limiting the means of growing system performance to add more tasks, or provide more computational resources to existing tasks. We present MCENoC, a Network-on-Chip (NoC) switching architecture that provides innovations to overcome this with predictable, formally verifiable timing behaviour that is consistent across the whole NoC. We show how the fundamental properties of Benes networks benefit MCE applications and meet our architecture requirements. Using SystemVerilog Assertions (SVA), formal properties are defined that aid the refinement of the specification of the design as well as enabling the implementation to be exhaustively formally verified. We demonstrate the performance of the design in terms of size, throughput and predictability, and discuss the application level considerations needed to exploit this architecture.
Dynamic Watermarking: Active Defense of Networked Cyber-Physical Systems
Venue: arXiv cs.SY 2016
Year: 2016

The coming decades may see the large scale deployment of networked cyber-physical systems to address global needs in areas such as energy, water, healthcare, and transportation. However, as recent events have shown, such systems are vulnerable to cyber attacks. Being safety critical, their disruption or misbehavior can cause economic losses or injuries and loss of life. It is therefore important to secure such networked cyber-physical systems against attacks. In the absence of credible security guarantees, there will be resistance to the proliferation of cyber-physical systems, which are much needed to meet global needs in critical infrastructures and services. This paper addresses the problem of secure control of networked cyber-physical systems. This problem is different from the problem of securing the communication network, since cyber-physical systems at their very essence need sensors and actuators that interface with the physical plant, and malicious agents may tamper with sensors or actuators, as recent attacks have shown. We consider physical plants that are being controlled by multiple actuators and sensors communicating over a network, where some sensors could be "malicious," meaning that they may not report the measurements that they observe. We address a general technique by which the actuators can detect the actions of malicious sensors in the system, and disable closed-loop control based on their information. This technique, called "watermarking," employs the technique of actuators injecting private excitation into the system which will reveal malicious tampering with signals. We show how such an active defense can be used to secure networked systems of sensors and actuators.
Defending Non-Bayesian Learning against Adversarial Attacks
Venue: arXiv cs.DC 2016
Year: 2016

This paper addresses the problem of non-Bayesian learning over multi-agent networks, where agents repeatedly collect partially informative observations about an unknown state of the world, and try to collaboratively learn the true state. We focus on the impact of the adversarial agents on the performance of consensus-based non-Bayesian learning, where non-faulty agents combine local learning updates with consensus primitives. In particular, we consider the scenario where an unknown subset of agents suffer Byzantine faults -- agents suffering Byzantine faults behave arbitrarily. Two different learning rules are proposed.
Least Cost Influence Maximization Across Multiple Social Networks
Venue: arXiv cs.SI 2016
Year: 2016

Recently in Online Social Networks (OSNs), the Least Cost Influence (LCI) problem has become one of the central research topics. It aims at identifying a minimum number of seed users who can trigger a wide cascade of information propagation. Most of existing literature investigated the LCI problem only based on an individual network. However, nowadays users often join several OSNs such that information could be spread across different networks simultaneously. Therefore, in order to obtain the best set of seed users, it is crucial to consider the role of overlapping users under this circumstances. In this article, we propose a unified framework to represent and analyze the influence diffusion in multiplex networks. More specifically, we tackle the LCI problem by mapping a set of networks into a single one via lossless and lossy coupling schemes. The lossless coupling scheme preserves all properties of original networks to achieve high quality solutions, while the lossy coupling scheme offers an attractive alternative when the running time and memory consumption are of primary concern. Various experiments conducted on both real and synthesized datasets have validated the effectiveness of the coupling schemes, which also provide some interesting insights into the process of influence propagation in multiplex networks.
Several Classes of Negabent Functions over Finite Fields
Venue: arXiv cs.IT 2016
Year: 2016

Negabent functions as a class of generalized bent functions have attracted a lot of attention recently due to their applications in cryptography and coding theory. In this paper, we consider the constructions of negabent functions over finite fields. First, by using the compositional inverses of certain binomial and trinomial permutations, we present several classes of negabent functions of the form $f(x)=\Tr_1^n(λx^{2^k+1})+\Tr_1^n(ux)\Tr_1^n(vx)$, where $λ\in \F_{2^n}$, $2\leq k\leq n-1$, $(u,v)\in \F^*_{2^n}\times \F^*_{2^n}$, and $\Tr_1^n(\cdot)$ is the trace function from $\F_{2^n}$ to $\F_{2}$. Second, by using Kloosterman sum, we prove that the condition for the cubic monomials given by Zhou and Qu (Cryptogr. Commun., to appear, DOI 10.1007/s12095-015-0167-0.) to be negabent is also necessary. In addition, a conjecture on negabent monomials whose exponents are of Niho type is given.
Evaluation and selection of Medical Tourism sites: A rough AHP based MABAC approach
Venue: arXiv cs.AI 2016
Year: 2016

In this paper, a novel multiple criteria decision making (MCDM) methodology is presented for assessing and prioritizing medical tourism destinations in uncertain environment. A systematic evaluation and assessment method is proposed by integrating rough number based AHP (Analytic Hierarchy Process) and rough number based MABAC (Multi-Attributive Border Approximation area Comparison). Rough number is used to aggregate individual judgments and preferences to deal with vagueness in decision making due to limited data. Rough AHP analyzes the relative importance of criteria based on their preferences given by experts. Rough MABAC evaluates the alternative sites based on the criteria weights. The proposed methodology is explained through a case study considering different cities for healthcare service in India. The validity of the obtained ranking for the given decision making problem is established by testing criteria proposed by Wang and Triantaphyllou (2008) along with further analysis and discussion.
A Distributional Semantics Approach to Implicit Language Learning
Venue: arXiv cs.CL 2016
Year: 2016

In the present paper we show that distributional information is particularly important when considering concept availability under implicit language learning conditions. Based on results from different behavioural experiments we argue that the implicit learnability of semantic regularities depends on the degree to which the relevant concept is reflected in language use. In our simulations, we train a Vector-Space model on either an English or a Chinese corpus and then feed the resulting representations to a feed-forward neural network. The task of the neural network was to find a mapping between the word representations and the novel words. Using datasets from four behavioural experiments, which used different semantic manipulations, we were able to obtain learning patterns very similar to those obtained by humans.
An uplink-downlink duality for cloud radio access network
Venue: arXiv cs.IT 2016
Year: 2016

Uplink-downlink duality refers to the fact that the Gaussian broadcast channel has the same capacity region as the dual Gaussian multiple-access channel under the same sumpower constraint. This paper investigates a similar duality relationship between the uplink and downlink of a cloud radio access network (C-RAN), where a central processor (CP) cooperatively serves multiple mobile users through multiple remote radio heads (RRHs) connected to the CP with finite-capacity fronthaul links. The uplink of such a C-RAN model corresponds to a multipleaccess relay channel; the downlink corresponds to a broadcast relay channel. This paper considers compression-based relay strategies in both uplink and downlink C-RAN, where the quantization noise levels are functions of the fronthaul link capacities. If the fronthaul capacities are infinite, the conventional uplinkdownlink duality applies. The main result of this paper is that even when the fronthaul capacities are finite, duality continues to hold for the case where independent compression is applied across each RRH in the sense that when the transmission and compression designs are jointly optimized, the achievable rate regions of the uplink and downlink remain identical under the same sum-power and individual fronthaul capacity constraints. As an application of the duality result, the power minimization problem in downlink C-RAN can be efficiently solved based on its uplink counterpart.
Next Generation Robotics
Venue: arXiv cs.CY 2016
Year: 2016

The National Robotics Initiative (NRI) was launched 2011 and is about to celebrate its 5 year anniversary. In parallel with the NRI, the robotics community, with support from the Computing Community Consortium, engaged in a series of road mapping exercises. The first version of the roadmap appeared in September 2009; a second updated version appeared in 2013. While not directly aligned with the NRI, these road-mapping documents have provided both a useful charting of the robotics research space, as well as a metric by which to measure progress. This report sets forth a perspective of progress in robotics over the past five years, and provides a set of recommendations for the future. The NRI has in its formulation a strong emphasis on co-robot, i.e., robots that work directly with people. An obvious question is if this should continue to be the focus going forward? To try to assess what are the main trends, what has happened the last 5 years and what may be promising directions for the future a small CCC sponsored study was launched to have two workshops, one in Washington DC (March 5th, 2016) and another in San Francisco, CA (March 11th, 2016). In this report we brief summarize some of the main discussions and observations from those workshops. We will present a variety of background information in Section 2, and outline various issues related to progress over the last 5 years in Section 3. In Section 4 we will outline a number of opportunities for moving forward. Finally, we will summarize the main points in Section 5.
How Many Folders Do You Really Need?
Venue: arXiv cs.AI 2016
Year: 2016

Email classification is still a mostly manual task. Consequently, most Web mail users never define a single folder. Recently however, automatic classification offering the same categories to all users has started to appear in some Web mail clients, such as AOL or Gmail. We adopt this approach, rather than previous (unsuccessful) personalized approaches because of the change in the nature of consumer email traffic, which is now dominated by (non-spam) machine-generated email. We propose here a novel approach for (1) automatically distinguishing between personal and machine-generated email and (2) classifying messages into latent categories, without requiring users to have defined any folder. We report how we have discovered that a set of 6 "latent" categories (one for human- and the others for machine-generated messages) can explain a significant portion of email traffic. We describe in details the steps involved in building a Web-scale email categorization system, from the collection of ground-truth labels, the selection of features to the training of models. Experimental evaluation was performed on more than 500 billion messages received during a period of six months by users of Yahoo mail service, who elected to be part of such research studies. Our system achieved precision and recall rates close to 90% and the latent categories we discovered were shown to cover 70% of both email traffic and email search queries. We believe that these results pave the way for a change of approach in the Web mail industry, and could support the invention of new large-scale email discovery paradigms that had not been possible before.
Zero-Shot Learning with Multi-Battery Factor Analysis
Venue: arXiv cs.CV 2016
Year: 2016

Zero-shot learning (ZSL) extends the conventional image classification technique to a more challenging situation where the test image categories are not seen in the training samples. Most studies on ZSL utilize side information such as attributes or word vectors to bridge the relations between the seen classes and the unseen classes. However, existing approaches on ZSL typically exploit a shared space for each type of side information independently, which cannot make full use of the complementary knowledge of different types of side information. To this end, this paper presents an MBFA-ZSL approach to embed different types of side information as well as the visual feature into one shared space. Specifically, we first develop an algorithm named Multi-Battery Factor Analysis (MBFA) to build a unified semantic space, and then employ multiple types of side information in it to achieve the ZSL. The close-form solution makes MBFA-ZSL simple to implement and efficient to run on large datasets. Extensive experiments on the popular AwA, CUB, and SUN datasets show its significant superiority over the state-of-the-art approaches.