OpenRaiser/Intern-Atlas huggingface dataset:
Bidirectional PageRank Estimation: From Average-Case to Worst-Case
Venue: arXiv cs.DS 2015
Year: 2015

We present a new algorithm for estimating the Personalized PageRank (PPR) between a source and target node on undirected graphs, with sublinear running-time guarantees over the worst-case choice of source and target nodes. Our work builds on a recent line of work on bidirectional estimators for PPR, which obtained sublinear running-time guarantees but in an average-case sense, for a uniformly random choice of target node. Crucially, we show how the reversibility of random walks on undirected networks can be exploited to convert average-case to worst-case guarantees. While past bidirectional methods combine forward random walks with reverse local pushes, our algorithm combines forward local pushes with reverse random walks. We also discuss how to modify our methods to estimate random-walk probabilities for any length distribution, thereby obtaining fast algorithms for estimating general graph diffusions, including the heat kernel, on undirected networks.
Keeping up with the e-Joneses: Do online social networks raise social comparisons?
Venue: arXiv cs.CY 2015
Year: 2015

Online social networks such as Facebook disclose an unprecedented volume of personal information amplifying the occasions for social comparisons. We test the hypothesis that the use of social networking sites (SNS) increases people's dissatisfaction with their income. After addressing endogeneity issues, our results suggest that SNS users have a higher probability to compare their achievements with those of others. This effect seems stronger than the one exerted by TV watching, it is particularly strong for younger people, and it affects men and women in a similar way.
Finding Consensus in Multi-Agent Networks Using Heat Kernel Pagerank
Venue: arXiv cs.SY 2015
Year: 2015

We present a new and efficient algorithm for determining a consensus value for a network of agents. Different from existing algorithms, our algorithm evaluates the consensus value for very large networks using heat kernel pagerank. We consider two frameworks for the consensus problem, a weighted average consensus among all agents, and consensus in a leader-following formation. Using a heat kernel pagerank approximation, we give consensus algorithms that run in time sublinear in the size of the network, and provide quantitative analysis of the tradeoff between performance guarantees and error estimates.
An Intelligent Approach to Software Cost Prediction
Venue: arXiv cs.SE 2015
Year: 2015

Good software cost prediction is important for effective project management such as budgeting, project planning and control. In this paper, we present an intelligent approach to software cost prediction. By integrating the neuro-fuzzy technique with the well-accepted COCOMO model, our approach can make the best use of both expert knowledge and historical project data. Its major advantages include learning ability, good interpretability, and robustness to imprecise and uncertain inputs. The validation using industry project data shows that the model greatly improves prediction accuracy in comparison with the COCOMO model.
Neuro-Fuzzy Algorithmic (NFA) Models and Tools for Estimation
Venue: arXiv cs.SE 2015
Year: 2015

Accurate estimation such as cost estimation, quality estimation and risk analysis is a major issue in management. We propose a patent pending soft computing framework to tackle this challenging problem. Our generic framework is independent of the nature and type of estimation. It consists of neural network, fuzzy logic, and an algorithmic estimation model. We made use of the Constructive Cost Model (COCOMO), Analysis of Variance (ANOVA), and Function Point Analysis as the algorithmic models and validated the accuracy of the Neuro-Fuzzy Algorithmic (NFA) Model in software cost estimation using industrial project data. Our model produces more accurate estimation than using an algorithmic model alone. We also discuss the prototypes of our tools that implement the NFA Model. We conclude with our roadmap and direction to enrich the model in tackling different estimation challenges.
A Novel Cooperative Strategy for Wireless Multihop Backhaul Networks
Venue: arXiv cs.IT 2015
Year: 2015

The 5G wireless network architecture will bring dense deployments of base stations called {\em small cells} for both outdoors and indoors traffic. The feasibility of their dense deployments depends on the existence of a high data-rate transport network that can provide high-data backhaul from an aggregation node where data traffic originates and terminates, to every such small cell. Due to the limited range of radio signals in the high frequency bands, multihop wireless connection may need to be established between each access node and an aggregation node. In this paper, we present a novel transmission scheme for wireless multihop backhaul for 5G networks. The scheme consists of 1) {\em group successive relaying} that established a relay schedule to efficiently exploit half-duplex relays and 2) an optimized quantize-map-and-forward (QMF) coding scheme that improves the performance of QMF and reduces the decoding complexity and the delay. We derive an achievable rate region of the proposed scheme and attain a closed-form expression in the asymptotic case for several network models of interests. It is shown that the proposed scheme provides a significant gain over multihop routing (based on decode-and-forward), which is a solution currently proposed for wireless multihop backhaul network. Furthermore, the performance gap increases as a network becomes denser. For the proposed scheme, we then develop energy-efficient routing that determines {\em groups} of participating relays for every hop. To reflect the metric used in the routing algorithm, we refer to it as {\em interference-harnessing} routing. By turning interference into a useful signal, each relay requires a lower transmission power to achieve a desired performance compared to other routing schemes. Finally, we present a low-complexity successive decoder, which makes it feasible to use the proposed scheme in practice.
The Interactive Effects of Operators and Parameters to GA Performance Under Different Problem Sizes
Venue: arXiv cs.NE 2015
Year: 2015

The complex effect of genetic algorithm's (GA) operators and parameters to its performance has been studied extensively by researchers in the past but none studied their interactive effects while the GA is under different problem sizes. In this paper, We present the use of experimental model (1)~to investigate whether the genetic operators and their parameters interact to affect the offline performance of GA, (2)~to find what combination of genetic operators and parameter settings will provide the optimum performance for GA, and (3)~to investigate whether these operator-parameter combination is dependent on the problem size. We designed a GA to optimize a family of traveling salesman problems (TSP), with their optimal solutions known for convenient benchmarking. Our GA was set to use different algorithms in simulating selection ($Ω_s$), different algorithms ($Ω_c$) and parameters ($p_c$) in simulating crossover, and different parameters ($p_m$) in simulating mutation. We used several $n$-city TSPs ($n=\{5, 7, 10, 100, 1000\}$) to represent the different problem sizes (i.e., size of the resulting search space as represented by GA schemata). Using analysis of variance of 3-factor factorial experiments, we found out that GA performance is affected by $Ω_s$ at small problem size (5-city TSP) where the algorithm Partially Matched Crossover significantly outperforms Cycle Crossover at $95\%$ confidence level.
Efficient handoff for mass transit connectivity using IEEE 802.11
Venue: arXiv cs.NI 2015
Year: 2015

In the recent years, WiFi standard has been used to develop different kind of wireless networks due to its flexibility and the availability of cheap off the shelf hardware. Even if the standard itself lacks mobility support, it has been used in networks with mobile nodes. When mobility is involved, a fast handoff is of paramount importance, especially with multimedia applications. The current IEEE 802.11 standard does not provide any specification about how the handoff should take place. In this paper we propose a handoff scheme optimized for networks providing wireless connection to mass transit vehicles. The structure of the procedure is redesigned to minimize delay as well as assure reliability. A reliable handoff triggering mechanism is also designed exploiting the results obtained from a set of preliminary outdoor experiments. The proposed scheme was implemented and deployed in an experimental testbed, and several indoor tests were run in order to demonstrate its reliability and efficiency.
Resilient Backhaul Network Design Using Hybrid Radio/Free-Space Optical Technology
Venue: arXiv cs.IT 2015
Year: 2015

The radio-frequency (RF) technology is a scalable solution for the backhaul planning. However, its performance is limited in terms of data rate and latency. Free Space Optical (FSO) backhaul, on the other hand, offers a higher data rate but is sensitive to weather conditions. To combine the advantages of RF and FSO backhauls, this paper proposes a cost-efficient backhaul network using the hybrid RF/FSO technology. To ensure a resilient backhaul, the paper imposes a given degree of redundancy by connecting each node through $K$ link-disjoint paths so as to cope with potential link failures. Hence, the network planning problem considered in this paper is the one of minimizing the total deployment cost by choosing the appropriate link type, i.e., either hybrid RF/FSO or optical fiber (OF), between each couple of base-stations while guaranteeing $K$ link-disjoint connections, a data rate target, and a reliability threshold. The paper solves the problem using graph theory techniques. It reformulates the problem as a maximum weight clique problem in the planning graph, under a specified realistic assumption about the cost of OF and hybrid RF/FSO links. Simulation results show the cost of the different planning and suggest that the proposed heuristic solution has a close-to-optimal performance for a significant gain in computation complexity.
Quantitative evaluation of the performance of discrete-time reservoir computers in the forecasting, filtering, and reconstruction of stochastic stationary signals
Venue: arXiv cs.ET 2015
Year: 2015

This paper extends the notion of information processing capacity for non-independent input signals in the context of reservoir computing (RC). The presence of input autocorrelation makes worthwhile the treatment of forecasting and filtering problems for which we explicitly compute this generalized capacity as a function of the reservoir parameter values using a streamlined model. The reservoir model leading to these developments is used to show that, whenever that approximation is valid, this computational paradigm satisfies the so called separation and fading memory properties that are usually associated with good information processing performances. We show that several standard memory, forecasting, and filtering problems that appear in the parametric stochastic time series context can be readily formulated and tackled via RC which, as we show, significantly outperforms standard techniques in some instances.
Comparison and evaluation of digital signature schemes employed in ndn network
Venue: arXiv cs.CR 2015
Year: 2015

Named Data networking ensure data integrity so that every important data has to be signed by its owner in order to send it safely inside the network. Similarly, in NDN we have to assure that none could open the data except authorized users. Since only the endpoints have the right to sign the data or check its validity during the verification process, we have considered that the data could be requested from various types of devices used by different people, these devices could be anything like a smartphone, PC, sensor node with a different CPU descriptions, parameters, and memory sizes, however their ability to check the high traffic of a data during the key generation and verification period is definitely a hard task and it could exhaust the systems with low computational resources. RSA and ECDSA as digital signature algorithms have proven their efficiency against cyber attacks, they are characterized by their speed to encrypt and decrypt data, in addition to their competence at checking the data integrity. The main purpose of our research was to find the optimal algorithm that avoids the systems overhead and offers the best time during the signature scheme
Class Vectors: Embedding representation of Document Classes
Venue: arXiv cs.CL 2015
Year: 2015

Distributed representations of words and paragraphs as semantic embeddings in high dimensional data are used across a number of Natural Language Understanding tasks such as retrieval, translation, and classification. In this work, we propose "Class Vectors" - a framework for learning a vector per class in the same embedding space as the word and paragraph embeddings. Similarity between these class vectors and word vectors are used as features to classify a document to a class. In experiment on several sentiment analysis tasks such as Yelp reviews and Amazon electronic product reviews, class vectors have shown better or comparable results in classification while learning very meaningful class embeddings.
Evolution of Social Networks: A Microfounded Model
Venue: arXiv cs.SI 2015
Year: 2015

Many societies are organized in networks that are formed by people who meet and interact over time. In this paper, we present a first model to capture the micro-foundations of social networks evolution, where boundedly rational agents of different types join the network; meet other agents stochastically over time; and consequently decide to form social ties. A basic premise of our model is that in real-world networks, agents form links by reasoning about the benefits that agents they meet over time can bestow. We study the evolution of the emerging networks in terms of friendship and popularity acquisition given the following exogenous parameters: structural opportunism, type distribution, homophily, and social gregariousness. We show that the time needed for an agent to find "friends" is influenced by the exogenous parameters: agents who are more gregarious, more homophilic, less opportunistic, or belong to a type "minority" spend a longer time on average searching for friendships. Moreover, we show that preferential attachment is a consequence of an emerging doubly preferential meeting process: a process that guides agents of a certain type to meet more popular similar-type agents with a higher probability, thereby creating asymmetries in the popularity evolution of different types of agents.
On double truncated (interval) WCRE and WCE
Venue: arXiv cs.IT 2015
Year: 2015

Measure of the weighted cumulative entropy about the predictability of failure time of a system have been introduced in [3]. Referring properties of doubly truncated (interval) cumulative residual and past entropy, several bounds and assertions are proposed in weighted version.
Optimal Radio Frequency Energy Harvesting with Limited Energy Arrival Knowledge
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we develop optimal policies for deciding when a wireless node with radio frequency (RF) energy harvesting (EH) capabilities should try and harvest ambient RF energy. While the idea of RF-EH is appealing, it is not always beneficial to attempt to harvest energy; in environments where the ambient energy is low, nodes could consume more energy being awake with their harvesting circuits turned on than what they can extract from the ambient radio signals; it is then better to enter a sleep mode until the ambient RF energy increases. Towards this end, we consider a scenario with intermittent energy arrivals and a wireless node that wakes up for a period of time (herein called the time-slot) and harvests energy. If enough energy is harvested during the time-slot, then the harvesting is successful and excess energy is stored; however, if there does not exist enough energy the harvesting is unsuccessful and energy is lost. We assume that the ambient energy level is constant during the time-slot, and changes at slot boundaries. The energy level dynamics are described by a two-state Gilbert-Elliott Markov chain model, where the state of the Markov chain can only be observed during the harvesting action, and not when in sleep mode. Two scenarios are studied under this model. In the first scenario, we assume that we have knowledge of the transition probabilities of the Markov chain and formulate the problem as a Partially Observable Markov Decision Process (POMDP), where we find a threshold-based optimal policy. In the second scenario, we assume that we don't have any knowledge about these parameters and formulate the problem as a Bayesian adaptive POMDP; to reduce the complexity of the computations we also propose a heuristic posterior sampling algorithm. The performance of our approaches is demonstrated via numerical examples.
When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective
Venue: arXiv cs.SI 2015
Year: 2015

Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications. This article investigates the structure of mobile sensing schemes and introduces crowdsourcing methods for mobile sensing. Inspired by social network, one can establish trust among participatory agents to leverage the wisdom of crowds for mobile sensing. A prototype of social network inspired mobile multimedia and sensing application is presented for illustrative purpose. Numerical experiments on real-world datasets show improved performance of mobile sensing via crowdsourcing. Challenges for mobile sensing with respect to Internet layers are discussed.
Identifying Emotion from Natural Walking
Venue: arXiv cs.CV 2015
Year: 2015

Emotion identification from gait aims to automatically determine persons affective state, it has attracted a great deal of interests and offered immense potential value in action tendency, health care, psychological detection and human-computer(robot) interaction.In this paper, we propose a new method of identifying emotion from natural walking, and analyze the relevance between the traits of walking and affective states. After obtaining the pure acceleration data of wrist and ankle, we set a moving average filter window with different sizes w, then extract 114 features including time-domain, frequency-domain, power and distribution features from each data slice, and run principal component analysis (PCA) to reduce dimension. In experiments, we train SVM, Decision Tree, multilayerperception, Random Tree and Random Forest classification models, and compare the classification accuracy on data of wrist and ankle with respect to different w. The performance of emotion identification on acceleration data of ankle is better than wrist.Comparing different classification models' results, SVM has best accuracy of identifying anger and happy could achieve 90:31% and 89:76% respectively, and identification ratio of anger-happy is 87:10%.The anger-neutral-happy classification reaches 85%-78%-78%.The results show that it is capable of identifying personal emotional states through the gait of walking.
Spin Glass Models of Syntax and Language Evolution
Venue: arXiv cs.CL 2015
Year: 2015

Using the SSWL database of syntactic parameters of world languages, and the MIT Media Lab data on language interactions, we construct a spin glass model of language evolution. We treat binary syntactic parameters as spin states, with languages as vertices of a graph, and assigned interaction energies along the edges. We study a rough model of syntax evolution, under the assumption that a strong interaction energy tends to cause parameters to align, as in the case of ferromagnetic materials. We also study how the spin glass model needs to be modified to account for entailment relations between syntactic parameters. This modification leads naturally to a generalization of Potts models with external magnetic field, which consists of a coupling at the vertices of an Ising model and a Potts model with q=3, that have the same edge interactions. We describe the results of simulations of the dynamics of these models, in different temperature and energy regimes. We discuss the linguistic interpretation of the parameters of the physical model.
Maintaining prediction quality under the condition of a growing knowledge space
Venue: arXiv cs.AI 2015
Year: 2015

Intelligence can be understood as an agent's ability to predict its environment's dynamic by a level of precision which allows it to effectively foresee opportunities and threats. Under the assumption that such intelligence relies on a knowledge space any effective reasoning would benefit from a maximum portion of useful and a minimum portion of misleading knowledge fragments. It begs the question of how the quality of such knowledge space can be kept high as the amount of knowledge keeps growing. This article proposes a mathematical model to describe general principles of how quality of a growing knowledge space evolves depending on error rate, error propagation and countermeasures. There is also shown to which extend the quality of a knowledge space collapses as removal of low quality knowledge fragments occurs too slowly for a given knowledge space's growth rate.
Popular Edges and Dominant Matchings
Venue: arXiv cs.DM 2015
Year: 2015

Given a bipartite graph G = (A u B, E) with strict preference lists and and edge e*, we ask if there exists a popular matching in G that contains the edge e*. We call this the popular edge problem. A matching M is popular if there is no matching M' such that the vertices that prefer M' to M outnumber those that prefer M to M'. It is known that every stable matching is popular; however G may have no stable matching with the edge e* in it. In this paper we identify another natural subclass of popular matchings called "dominant matchings" and show that if there is a popular matching that contains the edge e*, then there is either a stable matching that contains e* or a dominant matching that contains e*. This allows us to design a linear time algorithm for the popular edge problem. We also use dominant matchings to efficiently test if every popular matching in G is stable or not.
A Discussion on Developing Multihop Routing Metrics Sensitive to Node Mobility
Venue: arXiv cs.NI 2015
Year: 2015

This paper is focused on a discussion of parameters and heuristics that are expected to assist multihop routing in becoming more sensitive to node mobility. We provide a discussion concerning existing and a few proposed parameters. Moreover, the work also discusses two new heuristics based on the notion of link duration. The heuristics are compared based on a meaningful set of scenarios that attain different mobility aspects.
Robust and Sensitive Method of Lyapunov Exponent for Heart Rate Variability
Venue: arXiv cs.SY 2015
Year: 2015

Heart Rate Variability (HRV) plays an important role for reporting several cardiological and non-cardiological diseases. Also, the HRV has a prognostic value and is therefore quite important in modelling the cardiac risk. The nature of the HRV is chaotic, stochastic and it remains highly controversial. Because the HRV has utmost importance, it needs a sensitive tool to analyze the variability. In previous work, Rosenstein and Wolf had used the Lyapunov exponent as a quantitative measure for HRV detection sensitivity. However, the two methods diverge in determining the HRV sensitivity. This paper introduces a modification to both the Rosenstein and Wolf methods to overcome their drawbacks. The introduced Mazhar-Eslam algorithm increases the sensitivity to HRV detection with better accuracy.