OpenRaiser/Intern-Atlas huggingface dataset:
Very Simple Classifier: a Concept Binary Classifier toInvestigate Features Based on Subsampling and Localility
Venue: arXiv cs.LG 2016
Year: 2016

We propose Very Simple Classifier (VSC) a novel method designed to incorporate the concepts of subsampling and locality in the definition of features to be used as the input of a perceptron. The rationale is that locality theoretically guarantees a bound on the generalization error. Each feature in VSC is a max-margin classifier built on randomly-selected pairs of samples. The locality in VSC is achieved by multiplying the value of the feature by a confidence measure that can be characterized in terms of the Chebichev inequality. The output of the layer is then fed in a output layer of neurons. The weights of the output layer are then determined by a regularized pseudoinverse. Extensive comparison of VSC against 9 competitors in the task of binary classification is carried out. Results on 22 benchmark datasets with fixed parameters show that VSC is competitive with the Multi Layer Perceptron (MLP) and outperforms the other competitors. An exploration of the parameter space shows VSC can outperform MLP.
A Tighter Relation between Sensitivity and Certificate Complexity
Venue: arXiv cs.CC 2016
Year: 2016

The sensitivity conjecture which claims that the sensitivity complexity is polynomially related to block sensitivity complexity, is one of the most important and challenging problem in decision tree complexity theory. Despite of a lot of efforts, the best known upper bound of block sensitivity, as well as the certificate complexity, are still exponential in terms of sensitivity: $bs(f)\leq C(f)\leq\max\{2^{s(f)-1}(s(f)-\frac{1}{3}),s(f)\}$. In this paper, we give a better upper bound of $bs(f)\leq C(f)\leq(\frac{8}{9} + o(1))s(f)2^{s(f) - 1}$. The proof is based on a deep investigation on the structure of the sensitivity graph. We also provide a tighter relationship between $C_0(f)$ and $s_0(f)$ for functions with $s_1(f)=2$.
A Multi-Service Oriented Multiple-Access Scheme For M2M Support in Future LTE
Venue: arXiv cs.IT 2016
Year: 2016

We propose a novel multiple-access technique to overcome the shortcomings of the current proposals for the future releases of Long-Term Evolution (LTE). We provide a unified radio access system that efficiently and flexibly integrates both traditional cellular services and machine-to-machine (M2M) connections arising from Internet-of-Things (IoT) applications. The proposed solution, referred to as multi-service oriented multiple access (MOMA), is based on a) establishing separate classes of users using relevant criteria that go beyond the simple handheld-IoT device split, b) service-dependent hierarchical spreading of the data signals and c) a mix of multiuser and single-user detection schemes at the receiver. Signal spreading in MOMA allows to handle densely connected devices with different quality-of-service (QoS) profiles and at the same time its flexible receiver structure allows to allocate the receiver computational resources to the connections that need it most. This yields a scalable and efficient use of the available radio resources and a better service integration. While providing significant advantages for key future communications scenarios, MOMA can be incorporated into LTE with a limited impact on the protocol structure and the signaling overhead.
Enabling Soft Vertical Handover for MIPv6 in OMNeT++
Venue: arXiv cs.NI 2016
Year: 2016

Switching connectivity over multiple wireless interfaces of a mobile node is essential when performing handover between heterogeneous networks. In such a communication scenario, session continuity as experienced by the user can be enhanced if soft handover is enabled through the concept of make-before-break. However, some of the available networking protocols need to be modified to support this feature. This work gives an insight into the supplementary modules we implemented and the modification conducted in MIPv6 model of OMNeT++ to facilitate soft handover and data offloading for mobile nodes.
Resource Selection for Federated Search on the Web
Venue: arXiv cs.IR 2016
Year: 2016

A publicly available dataset for federated search reflecting a real web environment has long been absent, making it difficult for researchers to test the validity of their federated search algorithms for the web setting. We present several experiments and analyses on resource selection on the web using a recently released test collection containing the results from more than a hundred real search engines, ranging from large general web search engines such as Google, Bing and Yahoo to small domain-specific engines. First, we experiment with estimating the size of uncooperative search engines on the web using query based sampling and propose a new method using the ClueWeb09 dataset. We find the size estimates to be highly effective in resource selection. Second, we show that an optimized federated search system based on smaller web search engines can be an alternative to a system using large web search engines. Third, we provide an empirical comparison of several popular resource selection methods and find that these methods are not readily suitable for resource selection on the web. Challenges include the sparse resource descriptions and extremely skewed sizes of collections.
Structured Dropout for Weak Label and Multi-Instance Learning and Its Application to Score-Informed Source Separation
Venue: arXiv cs.LG 2016
Year: 2016

Many success stories involving deep neural networks are instances of supervised learning, where available labels power gradient-based learning methods. Creating such labels, however, can be expensive and thus there is increasing interest in weak labels which only provide coarse information, with uncertainty regarding time, location or value. Using such labels often leads to considerable challenges for the learning process. Current methods for weak-label training often employ standard supervised approaches that additionally reassign or prune labels during the learning process. The information gain, however, is often limited as only the importance of labels where the network already yields reasonable results is boosted. We propose treating weak-label training as an unsupervised problem and use the labels to guide the representation learning to induce structure. To this end, we propose two autoencoder extensions: class activity penalties and structured dropout. We demonstrate the capabilities of our approach in the context of score-informed source separation of music.
Collective Awareness Platforms and Digital Social Innovation Mediating Consensus Seeking in Problem Situations
Venue: arXiv cs.HC 2016
Year: 2016

In this paper we show the results of our studies carried out in the framework of the European Project SciCafe2.0 in the area of Participatory Engagement models. We present a methodological approach built on participative engagements models and holistic framework for problem situation clarification and solution impacts assessment. Several online platforms for social engagement have been analysed to extract the main patterns of participative engagement. We present our own experiments through the SciCafe2.0 Platform and our insights from requirements elicitation.
War-Algorithm Accountability
Venue: arXiv cs.CY 2016
Year: 2016

In this briefing report, we introduce a new concept (war algorithms) that elevates algorithmically-derived choices and decisions to a, and perhaps the, central concern regarding technical autonomy in war. We thereby aim to shed light on and recast the discussion regarding autonomous weapon systems. We define war algorithm as any algorithm that is expressed in computer code, that is effectuated through a constructed system, and that is capable of operating in relation to armed conflict. In introducing this concept, our foundational technological concern is the capability of a constructed system, without further human intervention, to help make and effectuate a decision or choice of a war algorithm. Distilled, the two core ingredients are an algorithm expressed in computer code and a suitably capable constructed system. Through that lens, we link international law and related accountability architectures to relevant technologies. We sketch a three-part (non-exhaustive) approach that highlights traditional and unconventional accountability avenues. We focus largely on international law because it is the only normative regime that purports, in key respects but with important caveats, to be both universal and uniform. By not limiting our inquiry only to weapon systems, we take an expansive view, showing how the broad concept of war algorithms might be susceptible to regulation, and how those algorithms might already fit within the existing regulatory system established by international law.
Concordance and the Smallest Covering Set of Preference Orderings
Venue: arXiv cs.AI 2016
Year: 2016

Preference orderings are orderings of a set of items according to the preferences (of judges). Such orderings arise in a variety of domains, including group decision making, consumer marketing, voting and machine learning. Measuring the mutual information and extracting the common patterns in a set of preference orderings are key to these areas. In this paper we deal with the representation of sets of preference orderings, the quantification of the degree to which judges agree on their ordering of the items (i.e. the concordance), and the efficient, meaningful description of such sets. We propose to represent the orderings in a subsequence-based feature space and present a new algorithm to calculate the size of the set of all common subsequences - the basis of a quantification of concordance, not only for pairs of orderings but also for sets of orderings. The new algorithm is fast and storage efficient with a time complexity of only $O(Nn^2)$ for the orderings of $n$ items by $N$ judges and a space complexity of only $O(\min\{Nn,n^2\})$. Also, we propose to represent the set of all $N$ orderings through a smallest set of covering preferences and present an algorithm to construct this smallest covering set. The source code for the algorithms is available at https://github.com/zhiweiuu/secs
RALL - Routing-Aware Of Path Length, Link Quality, And Traffic Load For Wireless Sensor Networks
Venue: arXiv cs.NI 2016
Year: 2016

Due to the enormous variety of application scenarios and ubiquity,Internet of Things (IoT) brought a new perspective of applications for the current and future Internet. The Wireless Sensor Networks provide key devices for developing the IoT communication paradigm, such as the sensors collecting various kind of information and the routing and MAC protocols. However, this type of network has strong power consumption and transmission capacity restrictions (low speed wireless links and subject to interference). In this context, it is necessary to develop solutions that enable a more efficient communication based on the optimized utilization of the network resources. This papers aims to present a multi-objective routing algorithm, named Routing-Aware of path Length, Link quality, and traffic Load (RALL), that seeks to balance three objectives: to minimize bottlenecks, to minimize path length, and to avoid links with low quality. RALL results in good performance when taking into consideration delivery rate, overhead, delay, and power consumption.
Energy-efficient Traffic Allocation in SDN-based Backhaul Networks: Theory and Implementation
Venue: arXiv cs.NI 2016
Year: 2016

5G networks are expected to be highly energy efficient, with a 10 times lower consumption than today's systems. An effective way to achieve such a goal is to act on the backhaul network by controlling the nodes operational state and the allocation of traffic flows. To this end, in this paper we formulate energy-efficient flow routing on the backhaul network as an optimization problem. In light of its complexity, which impairs the solution in large-scale scenarios, we then propose a heuristic approach. Our scheme, named EMMA, aims to both turn off idle nodes and concentrate traffic on the smallest possible set of links, which in its turn increases the number of idle nodes. We implement EMMA on top of ONOS and derive experimental results by emulating the network through Mininet. Our results show that EMMA provides excellent energy saving performance, which closely approaches the optimum. In larger network scenarios, the gain in energy consumption that EMMA provides with respect to the simple benchmark where all nodes are active, is extremely high, reaching almost 1 under medium-low traffic load.
Die-out Probability in SIS Epidemic Processes on Networks
Venue: arXiv cs.SI 2016
Year: 2016

An accurate approximate formula of the die-out probability in a SIS epidemic process on a network is proposed. The formula contains only three essential parameters: the largest eigenvalue of the adjacency matrix of the network, the effective infection rate of the virus, and the initial number of infected nodes in the network. The die-out probability formula is compared with the exact die-out probability in complete graphs, Erdős-Rényi graphs, and a power-law graph. Furthermore, as an example, the formula is applied to the $N$-Intertwined Mean-Field Approximation, to explicitly incorporate the die-out.
On Wirelessly Powered Communications with Short Packets
Venue: arXiv cs.IT 2016
Year: 2016

Wireless-powered communications will entail short packets due to naturally small payloads, low latency requirements and/or insufficient energy resources to support longer transmissions. In this paper, a wireless-powered communication system is investigated where an energy harvesting transmitter, charged by a power beacon via wireless energy transfer, attempts to communicate with a receiver over a noisy channel. Leveraging the framework of finite-length information theory, the system performance is analyzed using metrics such as the energy supply probability at the transmitter, and the achievable rate at the receiver. The analysis yields useful insights into the system behavior in terms of key parameters such as the harvest blocklength, the transmit blocklength, the average harvested power and the transmit power. Closed-form expressions are derived for the asymptotically optimal transmit power. Numerical results suggest that power control is essential for improving the achievable rate of the system in the finite blocklength regime.
Ensemble-Based Algorithms to Detect Disjoint and Overlapping Communities in Networks
Venue: arXiv cs.SI 2016
Year: 2016

Given a set ${\cal AL}$ of community detection algorithms and a graph $G$ as inputs, we propose two ensemble methods $\mathtt{EnDisCO}$ and $\mathtt{MeDOC}$ that (respectively) identify disjoint and overlapping communities in $G$. $\mathtt{EnDisCO}$ transforms a graph into a latent feature space by leveraging multiple base solutions and discovers disjoint community structure. $\mathtt{MeDOC}$ groups similar base communities into a meta-community and detects both disjoint and overlapping community structures. Experiments are conducted at different scales on both synthetically generated networks as well as on several real-world networks for which the underlying ground-truth community structure is available. Our extensive experiments show that both algorithms outperform state-of-the-art non-ensemble algorithms by a significant margin. Moreover, we compare $\mathtt{EnDisCO}$ and $\mathtt{MeDOC}$ with a recent ensemble method for disjoint community detection and show that our approaches achieve superior performance. To the best of our knowledge, $\mathtt{MeDOC}$ is the first ensemble approach for overlapping community detection.
Voltage Controlled Memristor Threshold Logic Gates
Venue: arXiv cs.ET 2016
Year: 2016

In this paper, we present a resistive switching memristor cell for implementing universal logic gates. The cell has a weighted control input whose resistance is set based on a control signal that generalizes the operational regime from NAND to NOR functionality. We further show how threshold logic in the voltage-controlled resistive cell can be used to implement a XOR logic. Building on the same principle we implement a half adder and a 4-bit CLA (Carry Look-ahead Adder) and show that in comparison with CMOS-only logic, the proposed system shows significant improvements in terms of device area, power dissipation and leakage power.
CMOS-Memristor Dendrite Threshold Circuits
Venue: arXiv cs.ET 2016
Year: 2016

Non-linear neuron models overcomes the limitations of linear binary models of neurons that have the inability to compute linearly non-separable functions such as XOR. While several biologically plausible models based on dendrite thresholds are reported in the previous studies, the hardware implementation of such non-linear neuron models remain as an open problem. In this paper, we propose a circuit design for implementing logical dendrite non-linearity response of dendrite spike and saturation types. The proposed dendrite cells are used to build XOR circuit and intensity detection circuit that consists of different combinations of dendrite cells with saturating and spiking responses. The dendrite cells are designed using a set of memristors, Zener diodes, and CMOS NOT gates. The circuits are designed, analyzed and verified on circuit boards.
Indicators as judgment devices: Citizen bibliometrics in biomedicine and economics
Venue: arXiv cs.DL 2016
Year: 2016

The number of publications has been a fundamental merit in the competition for academic positions since the late 18th century. Today, the simple counting of publications has been supplemented with a whole range of bibliometric measures, which supposedly not only measures the volume of research but also its impact. In this study, we investigate how bibliometrics are used for evaluating the impact and quality of publications in two specific settings: biomedicine and economics. Our study exposes the extent and type of metrics used in external evaluations of candidates for academic positions at Swedish universities. Moreover, we show how different bibliometric indicators, both explicitly and implicitly, are employed to value and rank candidates. Our findings contribute to a further understanding of bibliometric indicators as judgment devices that are employed in evaluating individuals and their published works within specific fields. We also show how expertise in using bibliometrics for evaluative purposes is negotiated at the interface between domain knowledge and skills in using indicators. In line with these results we propose that the use of metrics in this context is best described as a form of citizen bibliometrics - an underspecified term which we build upon in the paper.
Exploration Potential
Venue: arXiv cs.LG 2016
Year: 2016

We introduce exploration potential, a quantity that measures how much a reinforcement learning agent has explored its environment class. In contrast to information gain, exploration potential takes the problem's reward structure into account. This leads to an exploration criterion that is both necessary and sufficient for asymptotic optimality (learning to act optimally across the entire environment class). Our experiments in multi-armed bandits use exploration potential to illustrate how different algorithms make the tradeoff between exploration and exploitation.
Stamp processing with examplar features
Venue: arXiv cs.CV 2016
Year: 2016

Document digitization is becoming increasingly crucial. In this work, we propose a shape based approach for automatic stamp verification/detection in document images using an unsupervised feature learning. Given a small set of training images, our algorithm learns an appropriate shape representation using an unsupervised clustering. Experimental results demonstrate the effectiveness of our framework in challenging scenarios.
State access patterns in embarrassingly parallel computations
Venue: arXiv cs.DC 2016
Year: 2016

We introduce a set of state access patterns suitable for managing state in embarrassingly parallel computations on streams. The state access patterns are useful to model typical stream parallel applications. We present a classification of the patterns according to the extent and way in which the state is modified. We define precisely the state access patterns and discuss possible implementation schemas, performances and possibilities to manage adaptivity (parallelism degree) in the patterns. We present experimental results relative to an implementations on top of the structured parallel programming framework FastFlow that demonstrate the feasibility and efficiency of the proposed access patterns.
Optimal Channel Shortener Design for Reduced-State Soft-Output Viterbi Equalizer in Single-Carrier Systems
Venue: arXiv cs.IT 2016
Year: 2016

We consider optimal channel shortener design for reduced-state soft-output Viterbi equalizer (RS-SOVE) in single-carrier (SC) systems. To use RS-SOVE, three receiver filters need to be designed: a prefilter, a target response and a feedback filter. The collection of these three filters are commonly referred to as the \lq\lq{}channel shortener\rq\rq{}. Conventionally, the channel shortener is designed to transform an intersymbol interference (ISI) channel into an equivalent minimum-phase equivalent form. In this paper, we design the channel shortener to maximize a mutual information lower bound (MILB) based on a mismatched detection model. By taking the decision-feedback quality in the RS-SOVE into consideration, the prefilter and feedback filter are found in closed forms, while the target response is optimized via a gradient-ascending approach with the gradient explicitly derived. The information theoretical properties of the proposed channel shortener are analyzed. Moreover, we show through numerical results that, the proposed channel shortener design achieves superior detection performance compared to previous channel shortener designs at medium and high code-rates.
Online Learning for Offloading and Autoscaling in Renewable-Powered Mobile Edge Computing
Venue: arXiv cs.DC 2016
Year: 2016

Mobile edge computing (a.k.a. fog computing) has recently emerged to enable \emph{in-situ} processing of delay-sensitive applications at the edge of mobile networks. Providing grid power supply in support of mobile edge computing, however, is costly and even infeasible (in certain rugged or under-developed areas), thus mandating on-site renewable energy as a major or even sole power supply in increasingly many scenarios. Nonetheless, the high intermittency and unpredictability of renewable energy make it very challenging to deliver a high quality of service to users in renewable-powered mobile edge computing systems. In this paper, we address the challenge of incorporating renewables into mobile edge computing and propose an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading (to centralized cloud) and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost). Our online learning algorithm uses a decomposition of the (offline) value iteration and (online) reinforcement learning, thus achieving a significant improvement of learning rate and run-time performance when compared to standard reinforcement learning algorithms such as Q-learning.
Intrinsic normalization and extrinsic denormalization of formant data of vowels
Venue: arXiv cs.SD 2016
Year: 2016

Using a known speaker-intrinsic normalization procedure, formant data are scaled by the reciprocal of the geometric mean of the first three formant frequencies. This reduces the influence of the talker but results in a distorted vowel space. The proposed speaker-extrinsic procedure re-scales the normalized values by the mean formant values of vowels. When tested on the formant data of vowels published by Peterson and Barney, the combined approach leads to well separated clusters by reducing the spread due to talkers. The proposed procedure performs better than two top-ranked normalization procedures based on the accuracy of vowel classification as the objective measure.