OpenRaiser/Intern-Atlas huggingface dataset:
Some Epistemological Problems with the Knowledge Level in Cognitive Architectures
Venue: arXiv cs.AI 2015
Year: 2015

This article addresses an open problem in the area of cognitive systems and architectures: namely the problem of handling (in terms of processing and reasoning capabilities) complex knowledge structures that can be at least plausibly comparable, both in terms of size and of typology of the encoded information, to the knowledge that humans process daily for executing everyday activities. Handling a huge amount of knowledge, and selectively retrieve it ac- cording to the needs emerging in different situational scenarios, is an important aspect of human intelligence. For this task, in fact, humans adopt a wide range of heuristics (Gigerenzer and Todd) due to their bounded rationality (Simon, 1957). In this perspective, one of the re- quirements that should be considered for the design, the realization and the evaluation of intelligent cognitively inspired systems should be represented by their ability of heuristically identify and retrieve, from the general knowledge stored in their artificial Long Term Memory (LTM), that one which is synthetically and contextually relevant. This require- ment, however, is often neglected. Currently, artificial cognitive systems and architectures are not able, de facto, to deal with complex knowledge structures that can be even slightly comparable to the knowledge heuris- tically managed by humans. In this paper I will argue that this is not only a technological problem but also an epistemological one and I will briefly sketch a proposal for a possible solution.
Shaping Proto-Value Functions via Rewards
Venue: arXiv cs.AI 2015
Year: 2015

In this paper, we combine task-dependent reward shaping and task-independent proto-value functions to obtain reward dependent proto-value functions (RPVFs). In constructing the RPVFs we are making use of the immediate rewards which are available during the sampling phase but are not used in the PVF construction. We show via experiments that learning with an RPVF based representation is better than learning with just reward shaping or PVFs. In particular, when the state space is symmetrical and the rewards are asymmetrical, the RPVF capture the asymmetry better than the PVFs.
Improving Network Monitoring and Security via Visualization
Venue: arXiv cs.NI 2015
Year: 2015

Internet networks are handling increasing volume of traffic than ever before. This data is mainly associated to sensitive, distributed, and multimedia applications. In the past years, much attention has been paid to the way network infrastructure must be designed and developed in order to handle the challenges of delivering high quality services for applications such as VoIP and streaming video.
Psychological Types of Brazilian Software Engineering Students
Venue: arXiv cs.SE 2015
Year: 2015

The aim of this investigation was to establish the personality profile of Brazilian software engineering students according to the MBTI. This study also shows that the software engineering field attracts students of some types more than other types, for instance: Is, Ps, IPs, TPs, and INs are significantly represented in that group as opposed to E, Js, EJs, TJs, ENs.
Applying deep learning to classify pornographic images and videos
Venue: arXiv cs.CV 2015
Year: 2015

It is no secret that pornographic material is now a one-click-away from everyone, including children and minors. General social media networks are striving to isolate adult images and videos from normal ones. Intelligent image analysis methods can help to automatically detect and isolate questionable images in media. Unfortunately, these methods require vast experience to design the classifier including one or more of the popular computer vision feature descriptors. We propose to build a classifier based on one of the recently flourishing deep learning techniques. Convolutional neural networks contain many layers for both automatic features extraction and classification. The benefit is an easier system to build (no need for hand-crafting features and classifiers). Additionally, our experiments show that it is even more accurate than the state of the art methods on the most recent benchmark dataset.
On the storage and retrieval of primes and other random numbers using n-dimensional geometry
Venue: arXiv cs.CG 2015
Year: 2015

We show that if you represent all primes with less than n-digits as points in n-dimensional space, then they can be stored and retrieved conveniently using n-dimensional geometry. Also once you have calculated all the prime numbers less than n digits, it is very easy to find out if a given number having less than n-digits is or is not a prime. We do this by separating all the primes which are represented by points in n-dimension space by planes. It so turns out that the number of planes q, required to separate all the points represented by primes less than n-digit, are very few in number. Thus we obtain a very efficient storage and retrieval system in n-dimensional space. In addition the storage and retieval repository has the property that when new primes are added there is no need to start all over, we can begin where we last left off and add the new primes in the repository and add new planes that separate them as and when necessary. Also we can arrange matters such that the repository can begin to accept larger primes which has more digits say n' where n' > n. The algorithm does not make use of any property of prime numbers or of integers in general,except for the fact that any n-digit integer can be represented as a point in n-dimension space. Therefore the method can serve to be a storage and retrieval repository of any set of given integers, in practical cases they can represent information. Thus the algorithm can be used to devise a very efficient storage and retrieval system for large amounts of digital data.
Sparseness helps: Sparsity Augmented Collaborative Representation for Classification
Venue: arXiv cs.CV 2015
Year: 2015

Many classification approaches first represent a test sample using the training samples of all the classes. This collaborative representation is then used to label the test sample. It was a common belief that sparseness of the representation is the key to success for this classification scheme. However, more recently, it has been claimed that it is the collaboration and not the sparseness that makes the scheme effective. This claim is attractive as it allows to relinquish the computationally expensive sparsity constraint over the representation. In this paper, we first extend the analysis supporting this claim and then show that sparseness explicitly contributes to improved classification, hence it should not be completely ignored for computational gains. Inspired by this result, we augment a dense collaborative representation with a sparse representation and propose an efficient classification method that capitalizes on the resulting representation. The augmented representation and the classification method work together meticulously to achieve higher accuracy and lower computational time compared to state-of-the-art collaborative representation based classification approaches. Experiments on benchmark face, object and action databases show the efficacy of our approach.
A Novel Molecular Communication System Using Acids, Bases and Hydrogen Ions
Venue: arXiv cs.ET 2015
Year: 2015

Concentration modulation, whereby information is encoded in the concentration level of chemicals, is considered. One of the main challenges with such systems is the limited control the transmitter has on the concentration level at the receiver. For example, concentration cannot be directly decreased by the transmitter, and the decrease in concentration over time occurs solely due to transport mechanisms such as diffusion. This can result in inter-symbol interference (ISI), which can have degrading effects on performance. In this work, a new and novel scheme is proposed that uses the transmission of acids, bases, and the concentration of hydrogen ions for carrying information. By employing this technique, the concentration of hydrogen ions at the receiver can be both increased and decreased through the sender's transmissions. This enables novel ISI mitigation schemes as well as the possibility to form a wider array of signal patterns at the receiver.
Machine Learning Sentiment Prediction based on Hybrid Document Representation
Venue: arXiv cs.CL 2015
Year: 2015

Automated sentiment analysis and opinion mining is a complex process concerning the extraction of useful subjective information from text. The explosion of user generated content on the Web, especially the fact that millions of users, on a daily basis, express their opinions on products and services to blogs, wikis, social networks, message boards, etc., render the reliable, automated export of sentiments and opinions from unstructured text crucial for several commercial applications. In this paper, we present a novel hybrid vectorization approach for textual resources that combines a weighted variant of the popular Word2Vec representation (based on Term Frequency-Inverse Document Frequency) representation and with a Bag- of-Words representation and a vector of lexicon-based sentiment values. The proposed text representation approach is assessed through the application of several machine learning classification algorithms on a dataset that is used extensively in literature for sentiment detection. The classification accuracy derived through the proposed hybrid vectorization approach is higher than when its individual components are used for text represenation, and comparable with state-of-the-art sentiment detection methodologies.
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models
Venue: arXiv cs.AI 2015
Year: 2015

This paper addresses the general problem of reinforcement learning (RL) in partially observable environments. In 2013, our large RL recurrent neural networks (RNNs) learned from scratch to drive simulated cars from high-dimensional video input. However, real brains are more powerful in many ways. In particular, they learn a predictive model of their initially unknown environment, and somehow use it for abstract (e.g., hierarchical) planning and reasoning. Guided by algorithmic information theory, we describe RNN-based AIs (RNNAIs) designed to do the same. Such an RNNAI can be trained on never-ending sequences of tasks, some of them provided by the user, others invented by the RNNAI itself in a curious, playful fashion, to improve its RNN-based world model. Unlike our previous model-building RNN-based RL machines dating back to 1990, the RNNAI learns to actively query its model for abstract reasoning and planning and decision making, essentially "learning to think." The basic ideas of this report can be applied to many other cases where one RNN-like system exploits the algorithmic information content of another. They are taken from a grant proposal submitted in Fall 2014, and also explain concepts such as "mirror neurons." Experimental results will be described in separate papers.
An Improved Approximation Guarantee for the Maximum Budgeted Allocation Problem
Venue: arXiv cs.DS 2015
Year: 2015

We study the Maximum Budgeted Allocation problem, which is the problem of assigning indivisible items to players with budget constraints. In its most general form, an instance of the MBA problem might include many different prices for the same item among different players, and different budget constraints for every player. So far, the best approximation algorithms we know for the MBA problem achieve a $3/4$-approximation ratio, and employ a natural LP relaxation, called the Assignment-LP. In this paper, we give an algorithm for MBA, and prove that it achieves a $3/4+c$-approximation ratio, for some constant $c>0$. This algorithm works by rounding solutions to an LP called the Configuration-LP, therefore also showing that the Configuration-LP is strictly stronger than the Assignment-LP (for which we know that the integrality gap is $3/4$) for the MBA problem.
Symbolic Neutrosophic Theory
Venue: arXiv cs.AI 2015
Year: 2015

Symbolic (or Literal) Neutrosophic Theory is referring to the use of abstract symbols (i.e. the letters T, I, F, or their refined indexed letters Tj, Ik, Fl) in neutrosophics. We extend the dialectical triad thesis-antithesis-synthesis to the neutrosophic tetrad thesis-antithesis-neutrothesis-neutrosynthesis. The we introduce the neutrosophic system that is a quasi or (t,i,f) classical system, in the sense that the neutrosophic system deals with quasi-terms (concepts, attributes, etc.). Then the notions of Neutrosophic Axiom, Neutrosophic Deducibility, Degree of Contradiction (Dissimilarity) of Two Neutrosophic Axioms, etc. Afterwards a new type of structures, called (t, i, f) Neutrosophic Structures, and we show particular cases of such structures in geometry and in algebra. Also, a short history of the neutrosophic set, neutrosophic numerical components and neutrosophic literal components, neutrosophic numbers, etc. We construct examples of splitting the literal indeterminacy (I) into literal subindeterminacies (I1, I2, and so on, Ir), and to define a multiplication law of these literal subindeterminacies in order to be able to build refined I neutrosophic algebraic structures. We define three neutrosophic actions and their properties. We then introduce the prevalence order on T,I,F with respect to a given neutrosophic operator. And the refinement of neutrosophic entities A, neutA, and antiA. Then we extend the classical logical operators to neutrosophic literal (symbolic) logical operators and to refined literal (symbolic) logical operators, and we define the refinement neutrosophic literal (symbolic) space. We introduce the neutrosophic quadruple numbers (a+bT+cI+dF) and the refined neutrosophic quadruple numbers. Then we define an absorbance law, based on a prevalence order, in order to multiply the neutrosophic quadruple numbers.
Incident Response Plan for a Small to Medium Sized Hospital
Venue: arXiv cs.CR 2015
Year: 2015

Most small to medium health care organizations do not have the capability to address cyber incidents within the organization. Those that do are poorly trained and ill equipped. These health care organizations are subject to various laws that address privacy concerns, proper handling of financial information, and Personally Identifiable Information. Currently an IT staff handles responses to these incidents in an Ad Hoc manner. A properly trained, staffed, and equipped Cyber Incident Response Team is needed to quickly respond to these incidents to minimize data loss, and provide forensic data for the purpose of notification, disciplinary action, legal action, and to remove the risk vector. This paper will use the proven Incident Command System model used in emergency services to show any sized agency can have an adequate CIRT
Video Surveillance in the Cloud?
Venue: arXiv cs.CR 2015
Year: 2015

A high-resolution video surveillance management system incurs huge amounts of storage and network bandwidth. The current infrastructure required to support a high resolution video surveillance management system (VMS) is expensive and time consuming to plan, implement and maintain. With the recent advances in cloud technologies, opportunity for the utilization of virtualization and the opportunity for distributed computing techniques of cloud storage have been pursued on the basis to find out if the various cloud computing services that are available can support the current requirements to a high resolution video surveillance management system. The research concludes, after investigating and comparing various Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS) cloud computing provides what is possible to architect a VMS using cloud technologies; however, it is more expensive and it will require additional reviews for legal implications, as well as emerging threats and countermeasures associated with using cloud technologies for a video surveillance management system
Complete Network Security Protection for SME's within Limited Resources
Venue: arXiv cs.CR 2015
Year: 2015

The purpose of this paper is to present a comprehensive budget conscious security plan for smaller enterprises that lack security guidelines.The authors believe this paper will assist users to write an individualized security plan. In addition to providing the top ten free or affordable tools get some sort of semblance of security implemented, the paper also provides best practices on the topics of Authentication, Authorization, Auditing, Firewall, Intrusion Detection & Monitoring, and Prevention. The methods employed have been implemented at Company XYZ referenced throughout
Learning Using 1-Local Membership Queries
Venue: arXiv cs.LG 2015
Year: 2015

Classic machine learning algorithms learn from labelled examples. For example, to design a machine translation system, a typical training set will consist of English sentences and their translation. There is a stronger model, in which the algorithm can also query for labels of new examples it creates. E.g, in the translation task, the algorithm can create a new English sentence, and request its translation from the user during training. This combination of examples and queries has been widely studied. Yet, despite many theoretical results, query algorithms are almost never used. One of the main causes for this is a report (Baum and Lang, 1992) on very disappointing empirical performance of a query algorithm. These poor results were mainly attributed to the fact that the algorithm queried for labels of examples that are artificial, and impossible to interpret by humans. In this work we study a new model of local membership queries (Awasthi et al., 2012), which tries to resolve the problem of artificial queries. In this model, the algorithm is only allowed to query the labels of examples which are close to examples from the training set. E.g., in translation, the algorithm can change individual words in a sentence it has already seen, and then ask for the translation. In this model, the examples queried by the algorithm will be close to natural examples and hence, hopefully, will not appear as artificial or random. We focus on 1-local queries (i.e., queries of distance 1 from an example in the training sample). We show that 1-local membership queries are already stronger than the standard learning model. We also present an experiment on a well known NLP task of sentiment analysis. In this experiment, the users were asked to provide more information than merely indicating the label. We present results that illustrate that this extra information is beneficial in practice.
Efficient filtering of adult content using textual information
Venue: arXiv cs.IR 2015
Year: 2015

Nowadays adult content represents a non negligible proportion of the Web content. It is of the utmost importance to protect children from this content. Search engines, as an entry point for Web navigation are ideally placed to deal with this issue. In this paper, we propose a method that builds a safe index i.e. adult-content free for search engines. This method is based on a filter that uses only textual information from the web page and the associated URL.
Quantized Message Passing for LDPC Codes
Venue: arXiv cs.IT 2015
Year: 2015

We propose a quantized decoding algorithm for low- density parity-check codes where the variable node update rule of the standard min-sum algorithm is replaced with a look-up table (LUT) that is designed using an information-theoretic criterion. We show that even with message resolutions as low as 3 bits, the proposed algorithm can achieve better error rates than a floating-point min-sum decoder. Moreover, we study in detail the effect of different decoder design parameters, like the design SNR and the LUT tree structure on the performance of our decoder, and we propose some complexity reduction techniques, such as LUT re-use and message alphabet downsizing.
Placement and Assignment of Servers in Virtualized Radio Access Networks
Venue: arXiv cs.NI 2015
Year: 2015

The virtualization of Radio Access Networks (RANs) has been proposed as one of the important use cases of Network Function Virtualization (NFV). In Virtualized Radio Access Networks (VRANs), some functions from a Base Station (BS), such as those which make up the Base Band Unit (BBU), may be implemented in a shared infrastructure located at either a data center or distributed in network nodes. For the latter option, one challenge is in deciding which subset of the available network nodes can be used to host the physical BBU servers (the placement problem), and then to which of the available physical BBUs each Remote Radio Head (RRH) should be assigned (the assignment problem). These two problems constitute what we refer to as the VRAN Placement and Assignment Problem (VRAN-PAP). In this paper, we start by formally defining the VRAN-PAP before formulating it as a Binary Integer Linear Program (BILP) whose objective is to minimize the server and front haul link setup costs as well as the latency between each RRH and its assigned BBU. Since the BILP could become computationally intractable, we also propose a greedy approximation for larger instances of the VRAN-PAP. We perform simulations to compare both algorithms in terms of solution quality as well as computation time under varying network sizes and setup budgets.
Towards Dropout Training for Convolutional Neural Networks
Venue: arXiv cs.LG 2015
Year: 2015

Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advocate employing our proposed probabilistic weighted pooling, instead of commonly used max-pooling, to act as model averaging at test time. Empirical evidence validates the superiority of probabilistic weighted pooling. We also empirically show that the effect of convolutional dropout is not trivial, despite the dramatically reduced possibility of over-fitting due to the convolutional architecture. Elaborately designing dropout training simultaneously in max-pooling and fully-connected layers, we achieve state-of-the-art performance on MNIST, and very competitive results on CIFAR-10 and CIFAR-100, relative to other approaches without data augmentation. Finally, we compare max-pooling dropout and stochastic pooling, both of which introduce stochasticity based on multinomial distributions at pooling stage.
A Hybrid Intelligent Model for Software Cost Estimation
Venue: arXiv cs.SE 2015
Year: 2015

Accurate software development effort estimation is critical to the success of software projects. Although many techniques and algorithmic models have been developed and implemented by practitioners, accurate software development effort prediction is still a challenging endeavor in the field of software engineering, especially in handling uncertain and imprecise inputs and collinear characteristics. In this paper, a hybrid in-telligent model combining a neural network model integrated with fuzzy model (neuro-fuzzy model) has been used to improve the accuracy of estimating software cost. The performance of the proposed model is assessed by designing and conducting evaluation with published project and industrial data. Results have shown that the proposed model demonstrates the ability of improving the estimation accuracy by 18% based on the Mean Magnitude of Relative Error (MMRE) criterion.