OpenRaiser/Intern-Atlas huggingface dataset:
What is Learning? A primary discussion about information and Representation
Venue: arXiv cs.AI 2015
Year: 2015

Nowadays, represented by Deep Learning techniques, the field of machine learning is experiencing unprecedented prosperity and its influence is demonstrated in academia, industry and civil society. "Intelligent" has become a label which could not be neglected for most applications; celebrities and scientists also warned that the development of full artificial intelligence may spell the end of the human race. It seems that the answer to building a computer system that could automatically improve with experience is right on the next corner. While for AI and machine learning researchers, it is a consensus that we are not anywhere near the core technique which could bring the Terminator, Number 5 or R2D2 into real life, and there is not even a formal definition about what is intelligence, or one of its basic properties: Learning. Therefore, even though researchers know these concerns are not necessary currently, there is no generalized explanation about why these concerns are not necessary, and what properties people should take into account that would make these concerns to be necessary. In this paper, starts from analysing the relation between information and its representation, a necessary condition for a model to be a learning model is proposed. This condition and related future works could be used to verify whether a system is able to learn or not, and enrich our understanding of learning: one important property of Intelligence.
Indirect Rate-Distortion Function of a Binary i.i.d Source
Venue: arXiv cs.IT 2015
Year: 2015

The indirect source-coding problem in which a Bernoulli process is compressed in a lossy manner from its noisy observations is considered. These noisy observations are obtained by passing the source sequence through a The indirect source-coding problem in which a Bernoulli process is compressed in a lossy manner from its noisy observations is considered. These noisy observations are obtained by passing the source sequence through a binary symmetric channel so that the channel crossover probability controls the amount of information available about the source realization at the encoder. We use classic results in rate-distortion theory to compute an expression of the rate-distortion function for this model, where the Bernoulli source is not necessarily symmetric. The indirect rate-distortion function is given in terms of a solution to a simple equation. In addition, we derive an upper bound on the indirect rate-distortion function which is given in a closed. These expressions capture precisely the expected behavior that the noisier the observations, the smaller the return from increasing bit-rate to reduce distortion.
ADQUEX: Adaptive Processing of Federated Queries over Linked Data based on Tuple Routing
Venue: arXiv cs.DC 2015
Year: 2015

Due to the distribution of linked data across the web, the methods that process federated queries through a distributed approach are more attractive to the users and have gained more prosperity. In distributed processing of federated queries, we need methods and procedures to execute the query in an optimal manner. Most of the existing methods perform the optimization task based on some statistical information, whereas the query processor does not have precise statistical information about their properties, since the data sources are autonomous. When precise statistics are not available, the possibility of wrong estimations will highly increase, and may lead to inefficient execution of the query at runtime. Another problem of the existing methods is that in the optimization phase, they assume that runtime conditions of query execution are stable, while the environment in which federated queries are executed over linked data is dynamic and non-predictable. By considering these two problems, there is a great potential for exploiting the federated query processing techniques in an adaptive manner. In this paper, an adaptive method is proposed for processing federated queries over linked data, based on the concept of routing the tuples. The proposed method, named ADQUEX, is able to execute the query effectively without any prior statistical information. This method can change the query execution plan at runtime so that less intermediate results are produced. It can also adapt the execution plan to new situation if unpredicted network latencies arise. Extensive evaluation of our method by running real queries over well-known linked datasets shows very good results especially for complex queries.
A Near-optimal User Ordering Algorithm for Non-iterative Interference Alignment Transceiver Design in MIMO Interfering Broadcast Channels
Venue: arXiv cs.IT 2015
Year: 2015

Interference alignment (IA) has recently emerged as a promising interference mitigation technique for interference networks. In this letter, we focus on the IA non-iterative transceiver design problem in a multiple-input-multiple-output interfering broadcast channel (MIMO-IBC), and observed that there is previously unexploited flexibility in different permutations of user ordering. By choosing a good user ordering for a pre-determined IA inter-channel-interference allocation, an improved transceiver design can be accomplished. In order to achieve a more practical performance-complexity tradeoff, a suboptimal user ordering algorithm is proposed. Simulation shows the proposed suboptimal user ordering algorithm can achieve near-optimal performance compared to the optimal ordering while exhibiting only moderate computational complexity.
High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature Maps
Venue: arXiv cs.CV 2015
Year: 2015

Just like its great success in solving many computer vision problems, the convolutional neural networks (CNN) provided new end-to-end approach to handwritten Chinese character recognition (HCCR) with very promising results in recent years. However, previous CNNs so far proposed for HCCR were neither deep enough nor slim enough. We show in this paper that, a deeper architecture can benefit HCCR a lot to achieve higher performance, meanwhile can be designed with less parameters. We also show that the traditional feature extraction methods, such as Gabor or gradient feature maps, are still useful for enhancing the performance of CNN. We design a streamlined version of GoogLeNet [13], which was original proposed for image classification in recent years with very deep architecture, for HCCR (denoted as HCCR-GoogLeNet). The HCCR-GoogLeNet we used is 19 layers deep but involves with only 7.26 million parameters. Experiments were conducted using the ICDAR 2013 offline HCCR competition dataset. It has been shown that with the proper incorporation with traditional directional feature maps, the proposed single and ensemble HCCR-GoogLeNet models achieve new state of the art recognition accuracy of 96.35% and 96.74%, respectively, outperforming previous best result with significant gap.
The User Behavior in Facebook and its Development from 2009 until 2014
Venue: arXiv cs.SI 2015
Year: 2015

Online Social Networking is a fascinating phenomena, attracting more than one billion people. It supports basic human needs such as communication, socializing with others and reputation building. Thus, an in-depth understanding of user behavior in Online Social Networks (OSNs) can provide major insights into human behavior, and impacts design choices of social platforms and applications. However, researchers have only limited access to behavioral data. As a consequence of this limitation, user behavior in OSNs as well as its development in recent years are still not deeply understood. In this paper, we present a study about user behavior on the most popular OSN, Facebook, with 2071 participants from 46 countries. We elaborate how Facebookers orchestrate the offered functions to achieve individual benefit in 2014 and evaluate user activity changes from 2009 till 2014 to understand the development of user behavior. Inter alia, we focus on the most important functionality, the newsfeed, to understand content sharing amongst users. We (i) yield a better understanding on content sharing and consumption and (ii) refine behavioral assumptions in the literature to improve the performance of alternative social platforms. Furthermore, we (iii) contribute evidence to the discussion of Facebook to be an aging network.
Coexisting Success Probability and Throughput of Multi-RAT Wireless Networks with Unlicensed Band Access
Venue: arXiv cs.NI 2015
Year: 2015

In this letter, the coexisting success probability and throughput of a wireless network consisting of multiple subnetworks of different radio access technologies (RATs) is investigated. The coexisting success probability that is defined as the average of all success probabilities of all subnetworks is found in closed-form and it will be shown to have the concavity over the number of channels in the unlicensed band. The optimal deployment densities of all different RATs access points (APs) that maximize the coexisting success probability are shown to exist and can be found under the derived constraint on network parameters. The coexisting throughput is defined as the per-channel sum of all spectrum efficiencies of all subnetworks and numerical results show that it is significantly higher than the throughput of the unlicensed band only accessed by WiFi APs.
Average-case complexity of a branch-and-bound algorithm for maximum independent set, under the $\mathcal{G}(n,p)$ random model
Venue: arXiv cs.CC 2015
Year: 2015

We study average-case complexity of branch-and-bound for maximum independent set in random graphs under the $\mathcal{G}(n,p)$ distribution. In this model every pair $(u,v)$ of vertices belongs to $E$ with probability $p$ independently on the existence of any other edge. We make a precise case analysis, providing phase transitions between subexponential and exponential complexities depending on the probability $p$ of the random model.
A New Fundamental Evidence of Non-Classical Structure in the Combination of Natural Concepts
Venue: arXiv cs.AI 2015
Year: 2015

We recently performed cognitive experiments on conjunctions and negations of two concepts with the aim of investigating the combination problem of concepts. Our experiments confirmed the deviations (conceptual vagueness, underextension, overextension, etc.) from the rules of classical (fuzzy) logic and probability theory observed by several scholars in concept theory, while our data were successfully modeled in a quantum-theoretic framework developed by ourselves. In this paper, we isolate a new, very stable and systematic pattern of violation of classicality that occurs in concept combinations. In addition, the strength and regularity of this non-classical effect leads us to believe that it occurs at a more fundamental level than the deviations observed up to now. It is our opinion that we have identified a deep non-classical mechanism determining not only how concepts are combined but, rather, how they are formed. We show that this effect can be faithfully modeled in a two-sector Fock space structure, and that it can be exactly explained by assuming that human thought is the supersposition of two processes, a 'logical reasoning', guided by 'logic', and a 'conceptual reasoning' guided by 'emergence', and that the latter generally prevails over the former. All these findings provide a new fundamental support to our quantum-theoretic approach to human cognition.
On the Axiomatizability of Impossible Futures
Venue: arXiv cs.LO 2015
Year: 2015

A general method is established to derive a ground-complete axiomatization for a weak semantics from such an axiomatization for its concrete counterpart, in the context of the process algebra BCCS. This transformation moreover preserves omega-completeness. It is applicable to semantics at least as coarse as impossible futures semantics. As an application, ground- and omega-complete axiomatizations are derived for weak failures, completed trace and trace semantics. We then present a finite, sound, ground-complete axiomatization for the concrete impossible futures preorder, which implies a finite, sound, ground-complete axiomatization for the weak impossible futures preorder. In contrast, we prove that no finite, sound axiomatization for BCCS modulo concrete and weak impossible futures equivalence is ground-complete. If the alphabet of actions is infinite, then the aforementioned ground-complete axiomatizations are shown to be omega-complete. If the alphabet is finite, we prove that the inequational theories of BCCS modulo the concrete and weak impossible futures preorder lack such a finite basis.
Improved Bounds for Beacon-Based Coverage and Routing in Simple Rectilinear Polygons
Venue: arXiv cs.CG 2015
Year: 2015

We establish tight bounds for beacon-based coverage problems, and improve the bounds for beacon-based routing problems in simple rectilinear polygons. Specifically, we show that $\lfloor \frac{n}{6} \rfloor$ beacons are always sufficient and sometimes necessary to cover a simple rectilinear polygon $P$ with $n$ vertices. We also prove tight bounds for the case where $P$ is monotone, and we present an optimal linear-time algorithm that computes the beacon-based kernel of $P$. For the routing problem, we show that $\lfloor \frac{3n-4}{8} \rfloor - 1$ beacons are always sufficient, and $\lceil \frac{n}{4}\rceil-1$ beacons are sometimes necessary to route between all pairs of points in $P$.
Convergence Analysis of Policy Iteration
Venue: arXiv cs.SY 2015
Year: 2015

Adaptive optimal control of nonlinear dynamic systems with deterministic and known dynamics under a known undiscounted infinite-horizon cost function is investigated. Policy iteration scheme initiated using a stabilizing initial control is analyzed in solving the problem. The convergence of the iterations and the optimality of the limit functions, which follows from the established uniqueness of the solution to the Bellman equation, are the main results of this study. Furthermore, a theoretical comparison between the speed of convergence of policy iteration versus value iteration is presented. Finally, the convergence results are extended to the case of multi-step look-ahead policy iteration.
Image aesthetic evaluation using paralleled deep convolution neural network
Venue: arXiv cs.CV 2015
Year: 2015

Image aesthetic evaluation has attracted much attention in recent years. Image aesthetic evaluation methods heavily depend on the effective aesthetic feature. Traditional meth-ods always extract hand-crafted features. However, these hand-crafted features are always designed to adapt particu-lar datasets, and extraction of them needs special design. Rather than extracting hand-crafted features, an automati-cally learn of aesthetic features based on deep convolutional neural network (DCNN) is first adopt in this paper. As we all know, when the training dataset is given, the DCNN architecture with high complexity may meet the over-fitting problem. On the other side, the DCNN architecture with low complexity would not efficiently extract effective features. For these reasons, we further propose a paralleled convolutional neural network (PDCNN) with multi-level structures to automatically adapt to the training dataset. Experimental results show that our proposed PDCNN architecture achieves better performance than other traditional methods.
Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks
Venue: arXiv cs.LG 2015
Year: 2015

We study the optimal rates of convergence for estimating a prior distribution over a VC class from a sequence of independent data sets respectively labeled by independent target functions sampled from the prior. We specifically derive upper and lower bounds on the optimal rates under a smoothness condition on the correct prior, with the number of samples per data set equal the VC dimension. These results have implications for the improvements achievable via transfer learning. We additionally extend this setting to real-valued function, where we establish consistency of an estimator for the prior, and discuss an additional application to a preference elicitation problem in algorithmic economics.
Visual Understanding via Multi-Feature Shared Learning with Global Consistency
Venue: arXiv cs.CV 2015
Year: 2015

Image/video data is usually represented with multiple visual features. Fusion of multi-source information for establishing the attributes has been widely recognized. Multi-feature visual recognition has recently received much attention in multimedia applications. This paper studies visual understanding via a newly proposed l_2-norm based multi-feature shared learning framework, which can simultaneously learn a global label matrix and multiple sub-classifiers with the labeled multi-feature data. Additionally, a group graph manifold regularizer composed of the Laplacian and Hessian graph is proposed for better preserving the manifold structure of each feature, such that the label prediction power is much improved through the semi-supervised learning with global label consistency. For convenience, we call the proposed approach Global-Label-Consistent Classifier (GLCC). The merits of the proposed method include: 1) the manifold structure information of each feature is exploited in learning, resulting in a more faithful classification owing to the global label consistency; 2) a group graph manifold regularizer based on the Laplacian and Hessian regularization is constructed; 3) an efficient alternative optimization method is introduced as a fast solver owing to the convex sub-problems. Experiments on several benchmark visual datasets for multimedia understanding, such as the 17-category Oxford Flower dataset, the challenging 101-category Caltech dataset, the YouTube & Consumer Videos dataset and the large-scale NUS-WIDE dataset, demonstrate that the proposed approach compares favorably with the state-of-the-art algorithms. An extensive experiment on the deep convolutional activation features also show the effectiveness of the proposed approach. The code is available on http://www.escience.cn/people/lei/index.html
Benchmarking KAZE and MCM for Multiclass Classification
Venue: arXiv cs.CV 2015
Year: 2015

In this paper, we propose a novel approach for feature generation by appropriately fusing KAZE and SIFT features. We then use this feature set along with Minimal Complexity Machine(MCM) for object classification. We show that KAZE and SIFT features are complementary. Experimental results indicate that an elementary integration of these techniques can outperform the state-of-the-art approaches.
Integer Valued Betting strategies and Turing Degrees
Venue: arXiv cs.GT 2015
Year: 2015

Betting strategies are often expressed formally as martingales. A martingale is called integer-valued if each bet must be an integer value. Integer-valued strategies correspond to the fact that in most betting situations, there is a minimum amount that a player can bet. According to a well known paradigm, algorithmic randomness can be founded on the notion of betting strategies. A real X is called integer-valued random if no effective integer-valued martingale succeeds on X. It turns out that this notion of randomness has interesting interactions with genericity and the computably enumerable degrees. We investigate the computational power of the integer-valued random reals in terms of standard notions from computability theory.
Unsupervised Neural Network-Naive Bayes Model for Grouping Data Regional Development Results
Venue: arXiv cs.CY 2015
Year: 2015

Determination quadrant development has an important role in order to determine the achievement of the development of a district, in terms of the sector's gross regional domestic product (GDP). The process of determining the quadrant development typically uses Klassen rules based on its sector GDP. This study aims to provide a new approach in the conduct of regional development quadrant clustering using cluster techniques. Clustering is performed based on the average value of the growth and development of a district contribution compared with the average value and contribution of the development of the province based on data in comparison with a year of data to be compared. Testing models of clustering, performed on a dataset of two provinces, namely Banten (as a data testing) and Central Java (as the training data), to see the accuracy of the classification model proposed. The proposed model consists of two learning methods in it, namely unsupervised (Self Organizing Map / SOM-NN) method and supervised (Naive Bayess). SOM-NN method is used as a learning engine to generate training data for the target Class that will be used in the machine learning Naive Bayess. The results showed the clustering accuracy rate of the model was 98.1%, while the clustering accuracy rate of the model results compared to manual analysis shows the accuracy of the typology Klassen smaller, ie 29.63%. On one side, clustering results of the proposed model is influenced by the number and keagaraman data sets used.
Multi-Context Systems for Reactive Reasoning in Dynamic Environments
Venue: arXiv cs.AI 2015
Year: 2015

We show in this paper how managed multi-context systems (mMCSs) can be turned into a reactive formalism suitable for continuous reasoning in dynamic environments. We extend mMCSs with (abstract) sensors and define the notion of a run of the extended systems. We then show how typical problems arising in online reasoning can be addressed: handling potentially inconsistent sensor input, modeling intelligent forms of forgetting, selective integration of knowledge, and controlling the reasoning effort spent by contexts, like setting contexts to an idle mode. We also investigate the complexity of some important related decision problems and discuss different design choices which are given to the knowledge engineer.
On Minimal Change in Evolving Multi-Context Systems (Preliminary Report)
Venue: arXiv cs.AI 2015
Year: 2015

Managed Multi-Context Systems (mMCSs) provide a general framework for integrating knowledge represented in heterogeneous KR formalisms. However, mMCSs are essentially static as they were not designed to run in a dynamic scenario. Some recent approaches, among them evolving Multi-Context Systems (eMCSs), extend mMCSs by allowing not only the ability to integrate knowledge represented in heterogeneous KR formalisms, but at the same time to both react to, and reason in the presence of commonly temporary dynamic observations, and evolve by incorporating new knowledge. The notion of minimal change is a central notion in dynamic scenarios, specially in those that admit several possible alternative evolutions. Since eMCSs combine heterogeneous KR formalisms, each of which may require different notions of minimal change, the study of minimal change in eMCSs is an interesting and highly non-trivial problem. In this paper, we study the notion of minimal change in eMCSs, and discuss some alternative minimal change criteria.