OpenRaiser/Intern-Atlas huggingface dataset:
Analysis of the Game-Theoretic Modeling of Backscatter Wireless Sensor Networks under Smart Interference
Venue: arXiv cs.NI 2017
Year: 2017

In this paper, we study an interference avoidance scenario in the presence of a smart interferer which can rapidly observe the transmit power of a backscatter wireless sensor network (WSN) and effectively interrupt backscatter signals. We consider a power control with a sub-channel allocation to avoid interference attacks and a time-switching ratio for backscattering and RF energy harvesting in backscatter WSNs. We formulate the problem based on a Stackelberg game theory and compute the optimal transmit power, time-switching ratio, and sub-channel allocation parameter to maximize a utility function against the smart interference. We propose two algorithms for the utility maximization using Lagrangian dual decomposition for the backscatter WSN and the smart interference to prove the existence of the Stackelberg equilibrium. Numerical results show that the proposed algorithms effectively maximize the utility, compared to that of the algorithm based on the Nash game, so as to overcome smart interference in backscatter communications.
NR Wide Bandwidth Operations
Venue: arXiv cs.NI 2017
Year: 2017

The 3rd Generation Partnership Project (3GPP) is in the process of developing the next generation radio access technology, named New Radio (NR), which will be proposed as a candidate technology for IMT-2020. This article outlines the wide bandwidth operation of NR, among other new features being considered, based on the up-to-date discussions and decisions made in 3GPP standardization meetings. The much wider channel bandwidth of NR, compared to LTE, enables more efficient use of resources than the existing carrier aggregation framework at lower control overhead. The support of multiple sub-carrier spacing options allows NR to operate in a wide range of carrier frequency from sub-6 GHz band to mmWave band with appropriate handling of multi-path delay spread and phase noise depending on the carrier frequency. In addition, the introduction of the new bandwidth part concept allows to flexibly and dynamically configure User Equipment's (UE's) operating bandwidth, which will make NR an energy efficient solution despite the support of wide bandwidth. Other NR wideband operation related issues, such as the support of UEs with limited radio frequency (RF) capability and frequency domain resource indexing, are also explained in this article.
When Celebrities Endorse Politicians: Analyzing the Behavior of Celebrity Followers in the 2016 U.S. Presidential Election
Venue: arXiv cs.SI 2017
Year: 2017

Thanks to their name recognition and popularity, celebrities play an important role in American politics. Celebrity endorsements could add to the momentum of a politician's campaign and win the candidate extensive media coverage. There is one caveat though: the political preference of celebrity followers might differ from that of the celebrity. In this paper we explore that possibility. By carefully studying six prominent endorsements to the leading presidential candidates in the 2016 U.S. presidential election and statistically modeling Twitter "follow" behavior, we show (1) followers of all the celebrities with the exception of Lady Gaga are more likely to follow a large number of candidates and (2) the opinion of celebrity followers could systematically differ from that of the celebrity. Our methodology can be generalized to the study of such events as NBA players' refusing to visit the White House and pop singers' meeting with Dalai Lama.
A Probabilistic Analysis on a Lattice Attack against DSA
Venue: arXiv cs.CR 2017
Year: 2017

Analyzing the security of cryptosystems under attacks based on the malicious modification of memory registers is a research topic of high importance. This type of attacks may affect the randomness of the secret parameters by forcing a limited number of bits to a certain value which can be unknown to the attacker. In this context, we revisit the attack on DSA presented by Faugère, Goyet and Renault during the conference SAC 2012: we simplify their method and we provide a probabilistic approach in opposition to the heuristic proposed in the former to measure the limits of the attack. More precisely, the main problem is formulated as the search for a closest vector to a lattice, then we study the distribution of the vectors with bounded norms in a this family of lattices and we apply the result to predict the behavior of the attack. We validated this approach by computational experiments.
Reliable Messaging to Millions of Users with MigratoryData
Venue: arXiv cs.DC 2017
Year: 2017

Web-based notification services are used by a large range of businesses to selectively distribute live updates to customers, following the publish/subscribe (pub/sub) model. Typical deployments can involve millions of subscribers expecting ordering and delivery guarantees together with low latencies. Notification services must be vertically and horizontally scalable, and adopt replication to provide a reliable service. We report our experience building and operating MigratoryData, a highly-scalable notification service. We discuss the typical requirements of MigratoryData customers, and describe the architecture and design of the service, focusing on scalability and fault tolerance. Our evaluation demonstrates the ability of MigratoryData to handle millions of concurrent connections and support a reliable notification service despite server failures and network disconnections.
What do we need to build explainable AI systems for the medical domain?
Venue: arXiv cs.AI 2017
Year: 2017

Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous driving, speech recognition, or recommender systems. Deep learning approaches, trained on extremely large data sets or using reinforcement learning methods have even exceeded human performance in visual tasks, particularly on playing games such as Atari, or mastering the game of Go. Even in the medical domain there are remarkable results. The central problem of such models is that they are regarded as black-box models and even if we understand the underlying mathematical principles, they lack an explicit declarative knowledge representation, hence have difficulty in generating the underlying explanatory structures. This calls for systems enabling to make decisions transparent, understandable and explainable. A huge motivation for our approach are rising legal and privacy aspects. The new European General Data Protection Regulation entering into force on May 25th 2018, will make black-box approaches difficult to use in business. This does not imply a ban on automatic learning approaches or an obligation to explain everything all the time, however, there must be a possibility to make the results re-traceable on demand. In this paper we outline some of our research topics in the context of the relatively new area of explainable-AI with a focus on the application in medicine, which is a very special domain. This is due to the fact that medical professionals are working mostly with distributed heterogeneous and complex sources of data. In this paper we concentrate on three sources: images, *omics data and text. We argue that research in explainable-AI would generally help to facilitate the implementation of AI/ML in the medical domain, and specifically help to facilitate transparency and trust.
On the Challenges of Detecting Rude Conversational Behaviour
Venue: arXiv cs.HC 2017
Year: 2017

In this study, we aim to identify moments of rudeness between two individuals. In particular, we segment all occurrences of rudeness in conversations into three broad, distinct categories and try to identify each. We show how machine learning algorithms can be used to identify rudeness based on acoustic and semantic signals extracted from conversations. Furthermore, we make note of our shortcomings in this task and highlight what makes this problem inherently difficult. Finally, we provide next steps which are needed to ensure further success in identifying rudeness in conversations.
Robust Covariate Shift Prediction with General Losses and Feature Views
Venue: arXiv cs.LG 2017
Year: 2017

Covariate shift relaxes the widely-employed independent and identically distributed (IID) assumption by allowing different training and testing input distributions. Unfortunately, common methods for addressing covariate shift by trying to remove the bias between training and testing distributions using importance weighting often provide poor performance guarantees in theory and unreliable predictions with high variance in practice. Recently developed methods that construct a predictor that is inherently robust to the difficulties of learning under covariate shift are restricted to minimizing logloss and can be too conservative when faced with high-dimensional learning tasks. We address these limitations in two ways: by robustly minimizing various loss functions, including non-convex ones, under the testing distribution; and by separately shaping the influence of covariate shift according to different feature-based views of the relationship between input variables and example labels. These generalizations make robust covariate shift prediction applicable to more task scenarios. We demonstrate the benefits on classification under covariate shift tasks.
Corpus specificity in LSA and Word2vec: the role of out-of-domain documents
Venue: arXiv cs.CL 2017
Year: 2017

Latent Semantic Analysis (LSA) and Word2vec are some of the most widely used word embeddings. Despite the popularity of these techniques, the precise mechanisms by which they acquire new semantic relations between words remain unclear. In the present article we investigate whether LSA and Word2vec capacity to identify relevant semantic dimensions increases with size of corpus. One intuitive hypothesis is that the capacity to identify relevant dimensions should increase as the amount of data increases. However, if corpus size grow in topics which are not specific to the domain of interest, signal to noise ratio may weaken. Here we set to examine and distinguish these alternative hypothesis. To investigate the effect of corpus specificity and size in word-embeddings we study two ways for progressive elimination of documents: the elimination of random documents vs. the elimination of documents unrelated to a specific task. We show that Word2vec can take advantage of all the documents, obtaining its best performance when it is trained with the whole corpus. On the contrary, the specialization (removal of out-of-domain documents) of the training corpus, accompanied by a decrease of dimensionality, can increase LSA word-representation quality while speeding up the processing time. Furthermore, we show that the specialization without the decrease in LSA dimensionality can produce a strong performance reduction in specific tasks. From a cognitive-modeling point of view, we point out that LSA's word-knowledge acquisitions may not be efficiently exploiting higher-order co-occurrences and global relations, whereas Word2vec does.
Metascheduling of HPC Jobs in Day-Ahead Electricity Markets
Venue: arXiv cs.DC 2017
Year: 2017

High performance grid computing is a key enabler of large scale collaborative computational science. With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over time. In order to achieve cost effectiveness in these systems, it is essential for the scheduling algorithms to exploit electricity price variations, both in space and time, that are prevalent in the dynamic electricity price markets. In this paper, we present a metascheduling algorithm to optimize the placement of jobs in a compute grid which consumes electricity from the day-ahead wholesale market. We formulate the scheduling problem as a Minimum Cost Maximum Flow problem and leverage queue waiting time and electricity price predictions to accurately estimate the cost of job execution at a system. Using trace based simulation with real and synthetic workload traces, and real electricity price data sets, we demonstrate our approach on two currently operational grids, XSEDE and NorduGrid. Our experimental setup collectively constitute more than 433K processors spread across 58 compute systems in 17 geographically distributed locations. Experiments show that our approach simultaneously optimizes the total electricity cost and the average response time of the grid, without being unfair to users of the local batch systems.
Threat Modeling Data Analysis in Socio-technical Systems
Venue: arXiv cs.CY 2017
Year: 2017

Our decision-making processes are becoming more data driven, based on data from multiple sources, of different types, processed by a variety of technologies. As technology becomes more relevant for decision processes, the more likely they are to be subjects of attacks aimed at disrupting their execution or changing their outcome. With the increasing complexity and dependencies on technical components, such attempts grow more sophisticated and their impact will be more severe. This is especially important in scenarios with shared goals, which had to be previously agreed to, or decisions with broad social impact. We need to think about our decisions-making and underlying data analysis processes in a systemic way to correctly evaluate benefits and risks of specific solutions and to design them to be resistant to attacks. To reach these goals, we can apply experiences from threat modeling analysis used in software security. We will need to adapt these practices to new types of threats, protecting different assets and operating in socio-technical systems. With these changes, threat modeling can become a foundation for implementing detailed technical, organizational or legal mitigations and making our decisions more reliable and trustworthy.
Optimal Lower Bounds for Sketching Graph Cuts
Venue: arXiv cs.DS 2017
Year: 2017

We study the space complexity of sketching cuts and Laplacian quadratic forms of graphs. We show that any data structure which approximately stores the sizes of all cuts in an undirected graph on $n$ vertices up to a $1+ε$ error must use $Ω(n\log n/ε^2)$ bits of space in the worst case, improving the $Ω(n/ε^2)$ bound of Andoni et al. and matching the best known upper bound achieved by spectral sparsifiers. Our proof is based on a rigidity phenomenon for cut (and spectral) approximation which may be of independent interest: any two $d-$regular graphs which approximate each other's cuts significantly better than a random graph approximates the complete graph must overlap in a constant fraction of their edges.
Online Multicast Traffic Engineering for Software-Defined Networks
Venue: arXiv cs.NI 2017
Year: 2017

Previous research on SDN traffic engineering mostly focuses on static traffic, whereas dynamic traffic, though more practical, has drawn much less attention. Especially, online SDN multicast that supports IETF dynamic group membership (i.e., any user can join or leave at any time) has not been explored. Different from traditional shortest-path trees (SPT) and graph theoretical Steiner trees (ST), which concentrate on routing one tree at any instant, online SDN multicast traffic engineering is more challenging because it needs to support dynamic group membership and optimize a sequence of correlated trees without the knowledge of future join and leave, whereas the scalability of SDN due to limited TCAM is also crucial. In this paper, therefore, we formulate a new optimization problem, named Online Branch-aware Steiner Tree (OBST), to jointly consider the bandwidth consumption, SDN multicast scalability, and rerouting overhead. We prove that OBST is NP-hard and does not have a $|D_{max}|^{1-ε}$-competitive algorithm for any $ε>0$, where $|D_{max}|$ is the largest group size at any time. We design a $|D_{max}|$-competitive algorithm equipped with the notion of the budget, the deposit, and Reference Tree to achieve the tightest bound. The simulations and implementation on real SDNs with YouTube traffic manifest that the total cost can be reduced by at least 25% compared with SPT and ST, and the computation time is small for massive SDN.
Towards co-evolution of fitness predictors and Deep Neural Networks
Venue: arXiv cs.NE 2017
Year: 2017

Deep neural networks proved to be a very useful and powerful tool with many practical applications. They especially excel at learning from large data sets with labeled samples. However, in order to achieve good learning results, the network architecture has to be carefully designed. Creating an optimal topology requires a lot of experience and knowledge. Unfortunately there are no practically applicable algorithms which could help in this situation. Using an evolutionary process to develop new network topologies might solve this problem. The limiting factor in this case is the speed of evaluation of a single specimen (a single network architecture), which includes learning based on the whole large dataset. In this paper we propose to overcome this problem by using a fitness prediction technique: use subsets of the original training set to conduct the training process and use its results as an approximation of specimen's fitness. We discuss the feasibility of this approach in context of the desired fitness predictor features and analyze whether subsets obtained in an evolutionary process can be used to estimate the fitness of the network topology. Finally we draw conclusions from our experiments and outline plans for future work.
Dynamic Interference Steering in Heterogeneous Cellular Networks
Venue: arXiv cs.IT 2017
Year: 2017

With the development of diverse wireless communication technologies, interference has become a key impediment in network performance, thus making effective interference management (IM) essential to accommodate a rapidly increasing number of subscribers with diverse services. Although there have been numerous IM schemes proposed thus far, none of them are free of some form of cost. It is, therefore, important to balance the benefit brought by and cost of each adopted IM scheme by adapting its operating parameters to various network deployments and dynamic channel conditions. We propose a novel IM scheme, called dynamic interference steering (DIS), by recognizing the fact that interference can be not only suppressed or mitigated but also steered in a particular direction. Specifically, DIS exploits both channel state information (CSI) and the data contained in the interfering signal to generate a signal that modifies the spatial feature of the original interference to partially or fully cancel the interference appearing at the victim receiver. By intelligently determining the strength of the steering signal, DIS can steer the interference in an optimal direction to balance the transmitter's power used for IS and the desired signal's transmission. DIS is shown via simulation to be able to make better use of the transmit power, hence enhancing users' spectral efficiency (SE) effectively.
H4-Writer: A Text Entry Method Designed For Gaze Controlled Environment with Low KSPC and Spatial Footprint
Venue: arXiv cs.HC 2017
Year: 2017

This paper presents a new text entry technique, namely H4-Writer, designed for gaze controlled environments and aimed at reducing average KSPC . and spatial footprint. It also presents an empirical evaluation of this proposed system by using three different input devices: mouse, gamepad, and eye tracker. The experiment was conducted using 9 participants and the obtained data were used to compare the entry speeds, efficiency and KSPC of H4-Writer for all the devices. Over three blocks, the average entry speed was 3.54 wpm for the mouse, 3.33 wpm for the gamepad and only 2.11 wpm for the eye tracker. While the eye tracker fared poorly compared to the mouse and the gamepad on entry speed, it showed significant improvement in entry speed over progressing blocks indicating increase in entry speed with practice. A full longitudinal study was conducted to indicate this. The average KSPC of all the three devices over all the text phrases entered was 2.62, which is significantly lower compared to other hand writing recognizing text entry techniques like EdgeWrite. An analysis of the blocks revealed improvement in error rate, efficiency and KSPC values with progressing block numbers as the participants got more acclimatized with the key codes for corresponding characters.
Cyclic group based mutual authentication protocol for RFID system
Venue: arXiv cs.CR 2017
Year: 2017

Widespread deployment of RFID system arises security and privacy concerns of users. There are several proposals are in the literature to avoid these concerns, but most of them provides reasonable privacy at the cost of search complexity on the server side. The search complexity increases linearly with the number of tags in the system. Some schemes use a group based approach to solve the search complexity problem. In this paper, we proposed a group based authentication protocol for RFID system which is based on some characteristics of cyclic groups. The scheme uses only bitwise XOR and mod operation for the computational work. Also, the scheme does not use any pseudo-number generator on the tag-side. We use two benchmark metric based on anonymity set to measure the privacy level of the system when some tags are compromised by an adversary. We present some simulation results which show that the scheme preserves high level of privacy and discloses very less amount of information when some tags are compromised. Furthermore, it's formal and informal analysis shows that our scheme preserves information privacy as well as un-traceability and also withstand against various well known attacks.
On Binary Distributed Hypothesis Testing
Venue: arXiv cs.IT 2017
Year: 2017

We consider the problem of distributed binary hypothesis testing of two sequences that are generated by an i.i.d. doubly-binary symmetric source. Each sequence is observed by a different terminal. The two hypotheses correspond to different levels of correlation between the two source components, i.e., the crossover probability between the two. The terminals communicate with a decision function via rate-limited noiseless links. We analyze the tradeoff between the exponential decay of the two error probabilities associated with the hypothesis test and the communication rates. We first consider the side-information setting where one encoder is allowed to send the full sequence. For this setting, previous work exploits the fact that a decoding error of the source does not necessarily lead to an erroneous decision upon the hypothesis. We provide improved achievability results by carrying out a tighter analysis of the effect of binning error; the results are also more complete as they cover the full exponent tradeoff and all possible correlations. We then turn to the setting of symmetric rates for which we utilize Korner-Marton coding to generalize the results, with little degradation with respect to the performance with a one-sided constraint (side-information setting).