OpenRaiser/Intern-Atlas huggingface dataset:
Simultaneous Discrimination Prevention and Privacy Protection in Data Publishing and Mining
Venue: arXiv cs.DB 2013
Year: 2013

Data mining is an increasingly important technology for extracting useful knowledge hidden in large collections of data. There are, however, negative social perceptions about data mining, among which potential privacy violation and potential discrimination. Automated data collection and data mining techniques such as classification have paved the way to making automated decisions, like loan granting/denial, insurance premium computation. If the training datasets are biased in what regards discriminatory attributes like gender, race, religion, discriminatory decisions may ensue. In the first part of this thesis, we tackle discrimination prevention in data mining and propose new techniques applicable for direct or indirect discrimination prevention individually or both at the same time. We discuss how to clean training datasets and outsourced datasets in such a way that direct and/or indirect discriminatory decision rules are converted to legitimate (non-discriminatory) classification rules. In the second part of this thesis, we argue that privacy and discrimination risks should be tackled together. We explore the relationship between privacy preserving data mining and discrimination prevention in data mining to design holistic approaches capable of addressing both threats simultaneously during the knowledge discovery process. As part of this effort, we have investigated for the first time the problem of discrimination and privacy aware frequent pattern discovery, i.e. the sanitization of the collection of patterns mined from a transaction database in such a way that neither privacy-violating nor discriminatory inferences can be inferred on the released patterns. Moreover, we investigate the problem of discrimination and privacy aware data publishing, i.e. transforming the data, instead of patterns, in order to simultaneously fulfill privacy preservation and discrimination prevention.
Increasing Compression Ratio in PNG Images by k-Modulus Method for Image Transformation
Venue: arXiv cs.CV 2013
Year: 2013

Image compression is an important filed in image processing. The science welcomes any tinny contribution that may increase the compression ratio by whichever insignificant percentage. Therefore, the essential contribution in this paper is to increase the compression ratio for the well known Portable Network Graphics (PNG) image file format. The contribution starts with converting the original PNG image into k-Modulus Method (k-MM). Practically, taking k equals to ten, and then the pixels in the constructed image will be integers divisible by ten. Since PNG uses Lempel-Ziv compression algorithm, then the ability to reduce file size will increase according to the repetition in pixels in each k-by-k window according to the transformation done by k-MM. Experimental results show that the proposed technique (k-PNG) produces high compression ratio with smaller file size in comparison to the original PNG file.
SAHER: Secure and Efficient Routing in Sensor Networks
Venue: arXiv cs.DC 2013
Year: 2013

As an increasing amount of research is being done on various applications of sensor networks in adversarial environments, ensuring secure routing becomes of critical importance for the success of such deployments. The problem of designing a secure routing protocol for ad hoc networks has been already addressed, yet, there exists no complete solution that meets the specific requirements of sensor networks, where nodes are extremely constrained in terms of both power and computational resources. Thus, we propose a new protocol that is not built solely around security but also has efficiency and simplicity among its main goals. We propose the Secure Ad Hoc Efficient Routing protocol (SAHER) which employs a two-tier architecture based on node clustering. Also, we combine mechanisms like localscale geographic routing, per-node reputation tables, credit based alternate route enforcement and cumulative authentication. Using these techniques we examine ways to efficiently defend against the two most common network layer attacks: selective packet dropping and message flooding. Further, we consider join/leave operations which have not yet been studied in sufficient depth for sensor networks from a security standpoint. We provide a description of the protocol along with comprehensive experimental evaluation under different node distributions, different proportions of non-malicious vs. malicious nodes and different types of activity that malicious nodes could exhibit.
An Assessment Model for Cybercrime Investigation Capacity
Venue: arXiv cs.CY 2013
Year: 2013

Digital technologies are constantly changing, and with it criminals are finding new ways to abuse these technologies. Cybercrime investigators, then, must also keep their skills and knowledge up to date. This work proposes a holistic training development model - specifically focused on cybercrime investigation - that is based on improving investigator capability while also considering the capacity of the investigator or unit. Along with a training development model, a cybercrime investigation capacity assessment framework is given for attempting to measure capacity throughout the education process. First, a training development model is proposed that focuses on the expansion of investigation capability as well as capacity of investigators and units. Next, a capacity assessment model is given to evaluate the effectiveness of the training program. A description of how the proposed model is being applied to the development of training programs for cybercrime investigators in developing countries will then be given, as well as already observed challenges. Finally, concluding remarks as well as proposed future work is discussed.
Digital Data Security and Copyright Protection Using Cellular Automata
Venue: arXiv cs.CR 2013
Year: 2013

The emergence of many challenges and the rapid development of the means of communications and computer networks and the Internet. Digital information revolution has affected a lot on human societies. Data today has become available in digital format (text, image, audio, and video), which led to the emergence of many opportunities for creativity for innovation as well as the emergence of a new kind of challenges
Rational series and asymptotic expansion for linear homogeneous divide-and-conquer recurrences
Venue: arXiv cs.CC 2013
Year: 2013

Among all sequences that satisfy a divide-and-conquer recurrence, the sequences that are rational with respect to a numeration system are certainly the most immediate and most essential. Nevertheless, until recently they have not been studied from the asymptotic standpoint. We show how a mechanical process permits to compute their asymptotic expansion. It is based on linear algebra, with Jordan normal form, joint spectral radius, and dilation equations. The method is compared with the analytic number theory approach, based on Dirichlet series and residues, and new ways to compute the Fourier series of the periodic functions involved in the expansion are developed. The article comes with an extended bibliography.
WebSets: Extracting Sets of Entities from the Web Using Unsupervised Information Extraction
Venue: arXiv cs.LG 2013
Year: 2013

We describe a open-domain information extraction method for extracting concept-instance pairs from an HTML corpus. Most earlier approaches to this problem rely on combining clusters of distributionally similar terms and concept-instance pairs obtained with Hearst patterns. In contrast, our method relies on a novel approach for clustering terms found in HTML tables, and then assigning concept names to these clusters using Hearst patterns. The method can be efficiently applied to a large corpus, and experimental results on several datasets show that our method can accurately extract large numbers of concept-instance pairs.
Utility-maximization Resource Allocation for Device-to-Device Communication Underlaying Cellular Networks
Venue: arXiv cs.IT 2013
Year: 2013

Device-to-device(D2D) underlaying communication brings great benefits to the cellular networks from the improvement of coverage and spectral efficiency at the expense of complicated transceiver design. With frequency spectrum sharing mode, the D2D user generates interference to the existing cellular networks either in downlink or uplink. Thus the resource allocation for D2D pairs should be designed properly in order to reduce possible interference, in particular for uplink. In this paper, we introduce a novel bandwidth allocation scheme to maximize the utilities of both D2D users and cellular users. Since the allocation problem is strongly NP-hard, we apply a relaxation to the association indicators. We propose a low-complexity distributed algorithm and prove the convergence in a static environment. The numerical result shows that the proposed scheme can significant improve the performance in terms of utilities.The performance of D2D communications depends on D2D user locations, the number of D2D users and QoS(Quality of Service) parameters.
The Social Media Genome: Modeling Individual Topic-Specific Behavior in Social Media
Venue: arXiv cs.SI 2013
Year: 2013

Information propagation in social media depends not only on the static follower structure but also on the topic-specific user behavior. Hence novel models incorporating dynamic user behavior are needed. To this end, we propose a model for individual social media users, termed a genotype. The genotype is a per-topic summary of a user's interest, activity and susceptibility to adopt new information. We demonstrate that user genotypes remain invariant within a topic by adopting them for classification of new information spread in large-scale real networks. Furthermore, we extract topic-specific influence backbone structures based on information adoption and show that they differ significantly from the static follower network. When employed for influence prediction of new content spread, our genotype model and influence backbones enable more than $20% improvement, compared to purely structural features. We also demonstrate that knowledge of user genotypes and influence backbones allow for the design of effective strategies for latency minimization of topic-specific information spread.
Algorithms of the LDA model [REPORT]
Venue: arXiv cs.LG 2013
Year: 2013

We review three algorithms for Latent Dirichlet Allocation (LDA). Two of them are variational inference algorithms: Variational Bayesian inference and Online Variational Bayesian inference and one is Markov Chain Monte Carlo (MCMC) algorithm -- Collapsed Gibbs sampling. We compare their time complexity and performance. We find that online variational Bayesian inference is the fastest algorithm and still returns reasonably good results.
Wave-Style Token Machines and Quantum Lambda Calculi (Long Version)
Venue: arXiv cs.LO 2013
Year: 2013

Particle-style token machines are a way to interpret proofs and programs, when the latter are defined according to the principles of linear logic. In this paper, we show that token machines also make sense when the programs at hand are those of a simple linear quantum $λ$-calculus. This, however, requires generalizing the concept of a token machine to one in which more than one particle can possibly travel around the term at the same time. This is intimately related to entanglement and allows to give a simple operational semantics to the calculus coherently with the principles of quantum computation.
Fundamentals of Throughput Maximization with Random Arrivals for M2M Communications
Venue: arXiv cs.IT 2013
Year: 2013

For wireless systems in which randomly arriving devices attempt to transmit a fixed payload to a central receiver, we develop a framework to characterize the system throughput as a function of arrival rate and per-user data rate. The framework considers both coordinated transmission (where devices are scheduled) and uncoordinated transmission (where devices communicate on a random access channel and a provision is made for retransmissions). Our main contribution is a novel characterization of the optimal throughput for the case of uncoordinated transmission and a strategy for achieving this throughput that relies on overlapping transmissions and joint decoding. Simulations for a noise-limited cellular network show that the optimal strategy provides a factor of four improvement in throughput compared to slotted aloha. We apply our framework to evaluate more general system-level designs that account for overhead signaling. We demonstrate that, for small payload sizes relevant for machine-to-machine (M2M) communications (200 bits or less), a one-stage strategy, where identity and data are transmitted optimally over the random access channel, can support at least twice the number of devices compared to a conventional strategy, where identity is established over an initial random-access stage and data transmission is scheduled.
Kleene Algebras and Semimodules for Energy Problems
Venue: arXiv cs.FL 2013
Year: 2013

With the purpose of unifying a number of approaches to energy problems found in the literature, we introduce generalized energy automata. These are finite automata whose edges are labeled with energy functions that define how energy levels evolve during transitions. Uncovering a close connection between energy problems and reachability and Büchi acceptance for semiring-weighted automata, we show that these generalized energy problems are decidable. We also provide complexity results for important special cases.
On the Interaction of Adaptive Video Streaming with Content-Centric Networking
Venue: arXiv cs.NI 2013
Year: 2013

Two main trends in today's internet are of major interest for video streaming services: most content delivery platforms coincide towards using adaptive video streaming over HTTP and new network architectures allowing caching at intermediate points within the network. We investigate one of the most popular streaming service in terms of rate adaptation and opportunistic caching. Our experimental study shows that the streaming client's rate selection trajectory, i.e., the set of selected segments of varied bit rates which constitute a complete video, is not repetitive across separate downloads. Also, the involvement of caching could lead to frequent alternation between cache and server when serving back client's requests for video segments. These observations warrant cautions for rate adaption algorithm design and trigger our analysis to characterize the performance of in-network caching for HTTP streaming. Our analytic results show: (i) a significant degradation of cache hit rate for adaptive streaming, with a typical file popularity distribution in nowadays internet; (ii) as a result of the (usually) higher throughput at the client-cache connection compared to client-server one, cache-server oscillations due to misjudgments of the rate adaptation algorithm occur. Finally, we introduce DASH-INC, a framework for improved video streaming in caching networks including transcoding and multiple throughput estimation.
Novel Factorization Strategies for Higher Order Tensors: Implications for Compression and Recovery of Multi-linear Data
Venue: arXiv cs.IT 2013
Year: 2013

In this paper we propose novel methods for compression and recovery of multilinear data under limited sampling. We exploit the recently proposed tensor- Singular Value Decomposition (t-SVD)[1], which is a group theoretic framework for tensor decomposition. In contrast to popular existing tensor decomposition techniques such as higher-order SVD (HOSVD), t-SVD has optimality properties similar to the truncated SVD for matrices. Based on t-SVD, we first construct novel tensor-rank like measures to characterize informational and structural complexity of multilinear data. Following that we outline a complexity penalized algorithm for tensor completion from missing entries. As an application, 3-D and 4-D (color) video data compression and recovery are considered. We show that videos with linear camera motion can be represented more efficiently using t-SVD compared to traditional approaches based on vectorizing or flattening of the tensors. Application of the proposed tensor completion algorithm for video recovery from missing entries is shown to yield a superior performance over existing methods. In conclusion we point out several research directions and implications to online prediction of multilinear data.
Strong equivalence of reversible circuits is coNP-complete
Venue: arXiv cs.CC 2013
Year: 2013

It is well-known that deciding equivalence of logic circuits is a coNP-complete problem. As a corollary, the problem of deciding weak equivalence of reversible circuits, i.e. ignoring the ancilla bits, is also coNP-complete. The complexity of deciding strong equivalence, including the ancilla bits, is less obvious and may depend on gate set. Here we use Barrington's theorem to show that deciding strong equivalence of reversible circuits built from the Fredkin gate is coNP-complete. This implies coNP-completeness of deciding strong equivalence for other commonly used universal reversible gate sets, including any gate set that includes the Toffoli or Fredkin gate.
Domain Specific Hierarchical Huffman Encoding
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, we revisit the classical data compression problem for domain specific texts. It is well-known that classical Huffman algorithm is optimal with respect to prefix encoding and the compression is done at character level. Since many data transfer are domain specific, for example, downloading of lecture notes, web-blogs, etc., it is natural to think of data compression in larger dimensions (i.e. word level rather than character level). Our framework employs a two-level compression scheme in which the first level identifies frequent patterns in the text using classical frequent pattern algorithms. The identified patterns are replaced with special strings and to acheive a better compression ratio the length of a special string is ensured to be shorter than the length of the corresponding pattern. After this transformation, on the resultant text, we employ classical Huffman data compression algorithm. In short, in the first level compression is done at word level and in the second level it is at character level. Interestingly, this two level compression technique for domain specific text outperforms classical Huffman technique. To support our claim, we have presented both theoretical and simulation results for domain specific texts.
On the bounds and achievability about the ODPC of $\mathcal{GRM}(2,m)^*$ over prime field for increasing message length
Venue: arXiv cs.IT 2013
Year: 2013

The optimum distance profiles of linear block codes were studied for increasing or decreasing message length while keeping the minimum distances as large as possible, especially for Golay codes and the second-order Reed-Muller codes, etc. Cyclic codes have more efficient encoding and decoding algorithms. In this paper, we investigate the optimum distance profiles with respect to the cyclic subcode chains (ODPCs) of the punctured generalized second-order Reed-Muller codes $\mathcal{GRM}(2,m)^*$ which were applied in Power Control in OFDM Modulations in channels with synchronization, and so on. For this, two standards are considered in the inverse dictionary order, i.e., for increasing message length. Four lower bounds and upper bounds on ODPC are presented, where the lower bounds almost achieve the corresponding upper bounds in some sense. The discussions are over nonbinary prime field.
Extending UML for Conceptual Modeling of Annotation of Medical Images
Venue: arXiv cs.CV 2013
Year: 2013

Imaging has occupied a huge role in the management of patients, whether hospitalized or not. Depending on the patients clinical problem, a variety of imaging modalities were available for use. This gave birth of the annotation of medical image process. The annotation is intended to image analysis and solve the problem of semantic gap. The reason for image annotation is due to increase in acquisition of images. Physicians and radiologists feel better while using annotation techniques for faster remedy in surgery and medicine due to the following reasons: giving details to the patients, searching the present and past records from the larger databases, and giving solutions to them in a faster and more accurate way. However, classical conceptual modeling does not incorporate the specificity of medical domain specially the annotation of medical image. The design phase is the most important activity in the successful building of annotation process. For this reason, we focus in this paper on presenting the conceptual modeling of the annotation of medical image by defining a new profile using the StarUML extensibility mechanism.
WoodTouch, a new interaction interface for wooden furniture
Venue: arXiv cs.HC 2013
Year: 2013

If wood has been with us since time immemorial, being part of our environment, housing and tools, now wood has gain momentum, as it is clear that wood improves our life style. Because of the healthiness, resistance, ecology and comfort, wood is important for all of us, no matter what our life style is. WOODTOUCH Project aims to open a completely new market for furniture and interior design companies, enabling touch interaction between the user and wooden furniture surfaces. Why not switch on or dim the lights touching a wooden table? Why not turn on the heating system? Why not use wood as a touch sensitive surface for domotic control? The furniture designed with this novel technology, offers a wooden outer image and has different touch sensitive areas over the ones the user is able to control all sorts of electric appliances touching over a wooden surface.
Wireless sensor network technology for moisture monitoring of wood
Venue: arXiv cs.OH 2013
Year: 2013

Leaks represent a very important hazard for the buildings and they can affect all sorts of building materials and specially wood due to its hygroscopic properties. Excessive moisture content can affect in a negative way building processes such as the installation of wooden floors or the use of wood as a structural material. Moisture meters can provide prompt and non-destructive determination of wood moisture, and as such are among the most useful tools available to wood products manufacturers and scientists. However, a continuous monitoring system is needed in order to avoid excessive moisture content which can damage wooden floors as well as structural wood. Data and procedures are presented in order to develop a suitable monitoring tool based on wireless sensor networks to provide an electronic tool of active security both for the installation of wooden floors and for the proper maintenance of existent buildings which have a timber structure.