OpenRaiser/Intern-Atlas huggingface dataset:
Terminal-Set-Enhanced Community Detection in Social Networks
Venue: arXiv cs.SI 2016
Year: 2016

Community detection aims to reveal the community structure in a social network, which is one of the fundamental problems. In this paper we investigate the community detection problem based on the concept of terminal set. A terminal set is a group of users within which any two users belong to different communities. Although the community detection is hard in general, the terminal set can be very helpful in designing effective community detection algorithms. We first present a 2-approximation algorithm running in polynomial time for the original community detection problem. In the other issue, in order to better support real applications we further consider the case when extra restrictions are imposed on feasible partitions. For such customized community detection problems, we provide two randomized algorithms which are able to find the optimal partition with a high probability. Demonstrated by the experiments performed on benchmark networks the proposed algorithms are able to produce high-quality communities.
Lessons from DEPLOYment
Venue: arXiv cs.SE 2016
Year: 2016

This paper reviews the major lessons learnt during two significant pilot projects by Bosch Research during the DEPLOY project. Principally, the use of a single formalism, even when it comes together with a rigorous refinement methodology like Event-B, cannot offer a complete solution. Unfortunately (but not unexpectedly), we cannot offer a panacea to cover every phase from requirements to code; in fact any specific formalism or language (or tool) should be used only where and when it is really suitable and not necessarily (and somehow forcibly) over the entire lifecycle.
Double-detector for Sparse Signal Detection from One Bit Compressed Sensing Measurements
Venue: arXiv cs.IT 2016
Year: 2016

This letter presents the sparse vector signal detection from one bit compressed sensing measurements, in contrast to the previous works which deal with scalar signal detection. In this letter, available results are extended to the vector case and the GLRT detector and the optimal quantizer design are obtained. Also, a double-detector scheme is introduced in which a sensor level threshold detector is integrated into network level GLRT to improve the performance. The detection criteria of oracle and clairvoyant detectors are also derived. Simulation results show that with careful design of the threshold detector, the overall detection performance of double-detector scheme would be better than the sign-GLRT proposed in [1] and close to oracle and clairvoyant detectors. Also, the proposed detector is applied to spectrum sensing and the results are near the well known energy detector which uses the real valued data while the proposed detector only uses the sign of the data.
Identifying ECUs Using Inimitable Characteristics of Signals in Controller Area Networks
Venue: arXiv cs.CR 2016
Year: 2016

In the last several decades, the automotive industry has come to incorporate the latest Information and Communications (ICT) technology, increasingly replacing mechanical components of vehicles with electronic components. These electronic control units (ECUs) communicate with each other in an in-vehicle network that makes the vehicle both safer and easier to drive. Controller Area Networks (CANs) are the current standard for such high quality in-vehicle communication. Unfortunately, however, CANs do not currently offer protection against security attacks. In particular, they do not allow for message authentication and hence are open to attacks that replay ECU messages for malicious purposes. Applying the classic cryptographic method of message authentication code (MAC) is not feasible since the CAN data frame is not long enough to include a sufficiently long MAC to provide effective authentication. In this paper, we propose a novel identification method, which works in the physical layer of an in-vehicle CAN network. Our method identifies ECUs using inimitable characteristics of signals enabling detection of a compromised or alien ECU being used in a replay attack. Unlike previous attempts to address security issues in the in-vehicle CAN network, our method works by simply adding a monitoring unit to the existing network, making it deployable in current systems and compliant with required CAN standards. Our experimental results show that the bit string and classification algorithm that we utilized yielded more accurate identification of compromised ECUs than any other method proposed to date. The false positive rate is more than 2 times lower than the method proposed by P.-S. Murvay et al. This paper is also the first to identify potential attack models that systems should be able to detect.
An Analysis System for DNA Gel Electrophoresis Images Based on Automatic Thresholding an Enhancement
Venue: arXiv cs.CV 2016
Year: 2016

Gel electrophoresis, a widely used technique to separate DNA according to their size and weight, generates images that can be analyzed automatically. Manual or semiautomatic image processing presents a bottleneck for further development and leads to reproducibility issues. In this paper, we present a fully automated system with high accuracy for analyzing DNA and proteins. The proposed algorithm consists of four main steps: automatic thresholding, shifting, filtering, and data processing. Automatic thresholding, used to equalize the gray values of the gel electrophoresis image background, is one of the novel operations in this algorithm. Enhancement is also used to improve poor quality images that have faint DNA bands. Experimental results show that the proposed technique eliminates defects due to noise for average quality gel electrophoresis images, while it also improves the quality of poor images.
Automatic Techniques for Gridding cDNA Microarray Images
Venue: arXiv cs.CV 2016
Year: 2016

Microarray is considered an important instrument and powerful new technology for large-scale gene sequence and gene expression analysis. One of the major challenges of this technique is the image processing phase. The accuracy of this phase has an important impact on the accuracy and effectiveness of the subsequent gene expression and identification analysis. The processing can be organized mainly into four steps: gridding, spot isolation, segmentation, and quantification. Although several commercial software packages are now available, microarray image analysis still requires some intervention by the user, and thus a certain level of image processing expertise. This paper describes and compares four techniques that perform automatic gridding and spot isolation. The proposed techniques are based on template matching technique, standard deviation, sum, and derivative of these profiles. Experimental results show that the accuracy of the derivative of the sum profile is highly accurate compared to other techniques for good and poor quality microarray images.
Residential Demand Response Targeting Using Machine Learning with Observational Data
Venue: arXiv cs.SY 2016
Year: 2016

The large scale deployment of Advanced Metering Infrastructure among residential energy customers has served as a boon for energy systems research relying on granular consumption data. Residential Demand Response aims to utilize the flexibility of consumers to reduce their energy usage during times when the grid is strained. Suitable incentive mechanisms to encourage customers to deviate from their usual behavior have to be implemented to correctly control the bids into the wholesale electricity market as a Demand Response provider. In this paper, we present a framework for short term load forecasting on an individual user level, and relate nonexperimental estimates of Demand Response efficacy, i.e. the estimated reduction of consumption during Demand Response events, to the variability of user consumption. We apply our framework on a data set from a residential Demand Response program in the Western United States. Our results suggest that users with more variable consumption patterns are more likely to reduce their consumption compared to users with a more regular consumption behavior.
A Coarse-to-Fine Indoor Layout Estimation (CFILE) Method
Venue: arXiv cs.CV 2016
Year: 2016

The task of estimating the spatial layout of cluttered indoor scenes from a single RGB image is addressed in this work. Existing solutions to this problems largely rely on hand-craft features and vanishing lines, and they often fail in highly cluttered indoor rooms. The proposed coarse-to-fine indoor layout estimation (CFILE) method consists of two stages: 1) coarse layout estimation; and 2) fine layout localization. In the first stage, we adopt a fully convolutional neural network (FCN) to obtain a coarse-scale room layout estimate that is close to the ground truth globally. The proposed FCN considers combines the layout contour property and the surface property so as to provide a robust estimate in the presence of cluttered objects. In the second stage, we formulate an optimization framework that enforces several constraints such as layout contour straightness, surface smoothness and geometric constraints for layout detail refinement. Our proposed system offers the state-of-the-art performance on two commonly used benchmark datasets.
Message Routing in Wireless and Mobile Networks using TDMA Technology
Venue: arXiv cs.NI 2016
Year: 2016

In an era where communication has a most important role in modern societies, designing efficient algorithms for data transmission is of the outmost importance. TDMA is a technology used in many communication systems such as satellite, cell phone as well as other wireless or mobile networks. Most 2G cellular systems as well as some 3G are TDMA based. In order to transmit data in such systems we need to cluster them in packages. To achieve a faster transmission we are allowed to preempt the transmission of any packet in order to resume at a later time. Preemption can be used to reduce idleness of some stations. Such preemptions though come with a reconfiguration cost in order to setup for the next transmission. In this paper we propose two algorithms which yield improved transmission scheduling. These two algorithms we call MGA and IMGA (Improved MGA). We have proven an approximation ratio for MGA and ran experiments to establish that it works even better in practice. In order to conclude that MGA will be a very helpful tool in constructing an improved schedule for packet routing using preemtion with a setup cost, we compare its results to two other efficient algorithms designed by researchers in the past: A-PBS(d+1) and GWA. To establish the efficiency of IMGA we ran experiments in comparison to MGA as well as A-PBS(d+1) and GWA. IMGA has proven to produce the most efficient schedule on all counts.
Optimizing the Expected Mean Payoff in Energy Markov Decision Processes
Venue: arXiv cs.LO 2016
Year: 2016

Energy Markov Decision Processes (EMDPs) are finite-state Markov decision processes where each transition is assigned an integer counter update and a rational payoff. An EMDP configuration is a pair s(n), where s is a control state and n is the current counter value. The configurations are changed by performing transitions in the standard way. We consider the problem of computing a safe strategy (i.e., a strategy that keeps the counter non-negative) which maximizes the expected mean payoff.
Can we reach Pareto optimal outcomes using bottom-up approaches?
Venue: arXiv cs.MA 2016
Year: 2016

Traditionally, researchers in decision making have focused on attempting to reach Pareto Optimality using horizontal approaches, where optimality is calculated taking into account every participant at the same time. Sometimes, this may prove to be a difficult task (e.g., conflict, mistrust, no information sharing, etc.). In this paper, we explore the possibility of achieving Pareto Optimal outcomes in a group by using a bottom-up approach: discovering Pareto optimal outcomes by interacting in subgroups. We analytically show that Pareto optimal outcomes in a subgroup are also Pareto optimal in a supergroup of those agents in the case of strict, transitive, and complete preferences. Then, we empirically analyze the prospective usability and practicality of bottom-up approaches in a variety of decision making domains.
An Empirical Evaluation Of Social Influence Metrics
Venue: arXiv cs.SI 2016
Year: 2016

Predicting when an individual will adopt a new behavior is an important problem in application domains such as marketing and public health. This paper examines the perfor- mance of a wide variety of social network based measurements proposed in the literature - which have not been previously compared directly. We study the probability of an individual becoming influenced based on measurements derived from neigh- borhood (i.e. number of influencers, personal network exposure), structural diversity, locality, temporal measures, cascade mea- sures, and metadata. We also examine the ability to predict influence based on choice of classifier and how the ratio of positive to negative samples in both training and testing affect prediction results - further enabling practical use of these concepts for social influence applications.
Retrofitting mutual authentication to GSM using RAND hijacking
Venue: arXiv cs.CR 2016
Year: 2016

As has been widely discussed, the GSM mobile telephony system only offers unilateral authentication of the mobile phone to the network; this limitation permits a range of attacks. While adding support for mutual authentication would be highly beneficial, changing the way GSM serving networks operate is not practical. This paper proposes a novel modification to the relationship between a Subscriber Identity Module (SIM) and its home network which allows mutual authentication without changing any of the existing mobile infrastructure, including the phones; the only necessary changes are to the authentication centres and the SIMs. This enhancement, which could be deployed piecemeal in a completely transparent way, not only addresses a number of serious vulnerabilities in GSM but is also the first proposal for enhancing GSM authentication that possesses such transparency properties.
Corporate system of monitoring network informational resources based on agent-based approach
Venue: arXiv cs.SI 2016
Year: 2016

The paper provides a agent-based model, which describes distribution of informative messages, containing links to informational resources in the Internet. The results of modeling have been confirmed by studying a real network of Twitter microblogs. The paper describes stages of building a corporate system of monitoring network informational resources, the content of which is determined by links in microblogs. The advantages of such approach are set forth.
Citation score normalized by cited references (CSNCR): The introduction of a new citation impact indicator
Venue: arXiv cs.DL 2016
Year: 2016

In this paper, a new field-normalized indicator is introduced, which is rooted in early insights in bibliometrics, and is compared with several established field-normalized indicators (e.g. the mean normalized citation score, MNCS, and indicators based on percentile approaches). Garfield (1979) emphasizes that bare citation counts from different fields cannot be compared for evaluative purposes, because the "citation potential" can vary significantly between the fields. Garfield (1979) suggests that "the most accurate measure of citation potential is the average number of references per paper published in a given field". Based on this suggestion, the new indicator is basically defined as follows: the citation count of a focal paper is divided by the mean number of cited references in a field to normalize citations. The new indicator is called citation score normalized by cited references (CSNCR). The theoretical analysis of the CSNCR shows that it has the properties of consistency and homogeneous normalization. The close relation of the new indicator to the MNCS is discussed. The empirical comparison of the CSNCR with other field-normalized indicators shows that it is slightly poorer able to field-normalize citation counts than other cited-side normalized indicators (e.g. the MNCS), but its results are favorable compared to two citing-side indicator variants (SNCS indicators). Taken as a whole, the results of this study confirm the ability of established indicators to field-normalize citations.
Deletion Operations on Deterministic Families of Automata
Venue: arXiv cs.FL 2016
Year: 2016

Many different deletion operations are investigated applied to languages accepted by one-way and two-way deterministic reversal-bounded multicounter machines, deterministic pushdown automata, and finite automata. Operations studied include the prefix, suffix, infix and outfix operations, as well as left and right quotient with languages from different families. It is often expected that language families defined from deterministic machines will not be closed under deletion operations. However, here, it is shown that one-way deterministic reversal-bounded multicounter languages are closed under right quotient with languages from many different language families; even those defined by nondeterministic machines such as the context-free languages. Also, it is shown that when starting with one-way deterministic machines with one counter that makes only one reversal, taking the left quotient with languages from many different language families -- again including those defined by nondeterministic machines such as the context-free languages -- yields only one-way deterministic reversal-bounded multicounter languages (by increasing the number of counters). However, if there are two more reversals on the counter, or a second 1-reversal-bounded counter, taking the left quotient (or even just the suffix operation) yields languages that can neither be accepted by deterministic reversal-bounded multicounter machines, nor by 2-way nondeterministic machines with one reversal-bounded counter.
Can we unify monocular detectors for autonomous driving by using the pixel-wise semantic segmentation of CNNs?
Venue: arXiv cs.CV 2016
Year: 2016

Autonomous driving is a challenging topic that requires complex solutions in perception tasks such as recognition of road, lanes, traffic signs or lights, vehicles and pedestrians. Through years of research, computer vision has grown capable of tackling these tasks with monocular detectors that can provide remarkable detection rates with relatively low processing times. However, the recent appearance of Convolutional Neural Networks (CNNs) has revolutionized the computer vision field and has made possible approaches to perform full pixel-wise semantic segmentation in times close to real time (even on hardware that can be carried on a vehicle). In this paper, we propose to use full image segmentation as an approach to simplify and unify most of the detection tasks required in the perception module of an autonomous vehicle, analyzing major concerns such as computation time and detection performance.
Generic Statistical Relational Entity Resolution in Knowledge Graphs
Venue: arXiv cs.AI 2016
Year: 2016

Entity resolution, the problem of identifying the underlying entity of references found in data, has been researched for many decades in many communities. A common theme in this research has been the importance of incorporating relational features into the resolution process. Relational entity resolution is particularly important in knowledge graphs (KGs), which have a regular structure capturing entities and their interrelationships. We identify three major problems in KG entity resolution: (1) intra-KG reference ambiguity; (2) inter-KG reference ambiguity; and (3) ambiguity when extending KGs with new facts. We implement a framework that generalizes across these three settings and exploits this regular structure of KGs. Our framework has many advantages over custom solutions widely deployed in industry, including collective inference, scalability, and interpretability. We apply our framework to two real-world KG entity resolution problems, ambiguity in NELL and merging data from Freebase and MusicBrainz, demonstrating the importance of relational features.
Affect Intensity Estimation Using Multiple Modalities
Venue: arXiv cs.HC 2016
Year: 2016

One of the challenges in affect recognition is accurate estimation of the emotion intensity level. This research proposes development of an affect intensity estimation model based on a weighted sum of classification confidence levels, displacement of feature points and speed of feature point motion. The parameters of the model were calculated from data captured using multiple modalities such as face, body posture, hand movement and speech. A preliminary study was conducted to compare the accuracy of the model with the annotated intensity levels. An emotion intensity scale ranging from 0 to 1 along the arousal dimension in the emotion space was used. Results indicated speech and hand modality significantly contributed in improving accuracy in emotion intensity estimation using the proposed model.
Aggressive actions and anger detection from multiple modalities using Kinect
Venue: arXiv cs.HC 2016
Year: 2016

Prison facilities, mental correctional institutions, sports bars and places of public protest are prone to sudden violence and conflicts. Surveillance systems play an important role in mitigation of hostile behavior and improvement of security by detecting such provocative and aggressive activities. This research proposed using automatic aggressive behavior and anger detection to improve the effectiveness of the surveillance systems. An emotion and aggression aware component will make the surveillance system highly responsive and capable of alerting the security guards in real time. This research proposed facial expression, head, hand and body movement and speech tracking for detecting anger and aggressive actions. Recognition was achieved using support vector machines and rule based features. The multimodal affect recognition precision rate for anger improved by 15.2% and recall rate improved by 11.7% when behavioral rule based features were used in aggressive action detection.
Power-Efficient Resource Allocation for MC-NOMA with Statistical Channel State Information
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we study the power-efficient resource allocation for multicarrier non-orthogonal multiple access (MC-NOMA) systems. The resource allocation algorithm design is formulated as a non-convex optimization problem which takes into account the statistical channel state information at transmitter and quality of service (QoS) constraints. To strike a balance between system performance and computational complexity, we propose a suboptimal power allocation and user scheduling with low computational complexity to minimize the total power consumption. The proposed design exploits the heterogeneity of QoS requirement to determine the successive interference cancellation decoding order. Simulation results demonstrate that the proposed scheme achieves a close-to-optimal performance and significantly outperforms a conventional orthogonal multiple access (OMA) scheme.
Learning when to trust distant supervision: An application to low-resource POS tagging using cross-lingual projection
Venue: arXiv cs.CL 2016
Year: 2016

Cross lingual projection of linguistic annotation suffers from many sources of bias and noise, leading to unreliable annotations that cannot be used directly. In this paper, we introduce a novel approach to sequence tagging that learns to correct the errors from cross-lingual projection using an explicit debiasing layer. This is framed as joint learning over two corpora, one tagged with gold standard and the other with projected tags. We evaluated with only 1,000 tokens tagged with gold standard tags, along with more plentiful parallel data. Our system equals or exceeds the state-of-the-art on eight simulated low-resource settings, as well as two real low-resource languages, Malagasy and Kinyarwanda.