Miracle-dz/UniFAR-FTUs huggingface dataset:
NEvAr – The Assessment of an Evolutionary Art Tool

The use of Evolutionary Computation approaches to create images has reached a great popularity, leading to the appearance of a new art form – Evolutionary Art – and to the proliferation of Evolutionary Art Tools. In this paper we present and make an asse ssment of one of these tools: NEvAr. We also systematise and describe the work methodology currently used to generate images. When workin g with NEvAr we focus on the reuse of useful individual, which we store in an image database. The size of this database, and the importanc of its role, led us to the development of automatic seeding procedures, which we also describe.
Word Similarity for Document Grouping using Soft Computing

Summary The technology world has provided a more efficient and quicker way of accessing information through the web and databases in organizations that implement information systems in order to achieve a competitive edge. The simplest way of filtering information is to extract keywords in measuring the documents relevance. Nonetheless, getting to the right document is often a problem. Synonymy i.e., two words with the same meaning, for example, taxi and cab is a major problem in information searching. This work uses the soft computing techniques in the area of information retrieval and they encompass both fuzzy set theory and probability theory. We propose an algorithm for computing asymmetric word similarities (AWS) to overcome the synonymy problem. The algorithm is computed using mass assignment based on fuzzy sets of words. A key feature of our algorithm is that it is incremental, i.e. words (and documents) can be added or subtracted without extensive re-computation. AWS produced similarity measures of consistently 10% higher than tf.idf algorithm and performed successful document groupings.
Unsupervised Event Detection with Infinite Poisson Mixture Model

Large amount of time series data generated by sensors and Web users is great source of contextual information. Detecting outliers with unusually high values in time series data is crucial for inferring about any events in the real world. In this work, we describe an infinite Poisson mixture model to detect events by identifying outliers in time series of count data. This unsupervised technique estimates the probability densities of count data which have an unknown Poisson mixture while it simultaneously detects outliers in the data. The advantage of our model is that outliers are mapped to mixture components discovered by infinite mixture model and thus inference can be drawn on the different 'types' of outliers and their proportions in the data. This lets us identify and categorize events based on magnitude of outlier data. We have analysed the performance of our model against a well known event detection technique based on Markov Modulated Poisson Process (MMPP) using synthetic and real world data. Results show that our approach to detecting events is more appropriate in analysing periodic count data as compared to the MMPP baseline. The experiments demonstrate that the presented model provides robust, detailed, and interpretable results for the analysis of outliers to detect events.
OpenCL caffe: Accelerating and enabling a cross platform machine learning framework

Deep neural networks (DNN) achieved significant breakthrough in vision recognition in 2012 and quickly became the leading machine learning algorithm in Big Data based large scale object recognition applications. The successful deployment of DNN based applications pose challenges for a cross platform software framework that enable multiple user scenarios, including offline model training on HPC clusters and online recognition in embedded environments. Existing DNN frameworks are mostly focused on a closed format CUDA implementations, which is limiting of deploy breadth of DNN hardware systems. This paper presents OpenCL™ caffe, which targets in transforming the popular CUDA based framework caffe [1] into open standard OpenCL backend. The goal is to enable a heterogeneous platform compatible DNN framework and achieve competitive performance based on OpenCL tool chain. Due to DNN models' high complexity, we use a two-phase strategy. First we introduce the OpenCL porting strategies that guarantee algorithm convergence; then we analyze OpenCL's performance bottlenecks in DNN domain and propose a few optimization techniques including batched manner data layout and multiple command queues to better map the problem size into existing BLAS library, improve hardware resources utilization and boost OpenCL runtime efficiency. We verify OpenCL caffe's successful offline training and online recognition on both server-end and consumer-end GPUs. Experimental results show that the phase-two's optimized OpenCL caffe achieved a 4.5x speedup without modifying BLAS library. The user can directly run mainstream DNN models and achieves the best performance for a specific processors by choosing the optimal batch number depending on H/W properties and input data size.
FPGA and GPU implementation of large scale SpMV

Sparse matrix-vector multiplication (SpMV) is a fundamental operation for many applications. Many studies have been done to implement the SpMV on different platforms, while few work focused on the very large scale datasets with millions of dimensions. This paper addresses the challenges of implementing large scale SpMV with FPGA and GPU in the application of web link graph analysis. In the FPGA implementation, we designed the task partition and memory hierarchy according to the analysis of datasets scale and their access pattern. In the GPU implementation, we designed a fast and scalable SpMV routine with three passes, using a modified Compressed Sparse Row format. Results show that FPGA and GPU implementation achieves about 29x and 30x speedup on a StratixII EP2S180 FPGA and Radeon 5870 Graphic Card respectively compared with a Phenom 9550 CPU.
The Optimum Quantity of Money''in The Optimum Quantity of Money and other essays

A process of polymerizing conjugated diolefinic monomers containing from 4 to about 12 carbon atoms to high molecular weight polymers by bringing said monomers into contact with a catalyst system consisting of (1) an iron-containing compound, (2) an organometallic reducing agent from Groups I and III of the Periodic Table, and (3) a sulfur oxygen containing ligand.
Interpolated PLSI for Learning Plausible Verb Arguments

Learning Plausible Verb Arguments allows to automatically learn what kind of activities, where and how, are performed by classes of entities from sparse argument co%occurrences with a verb; this informati on it is useful for sentence reconstruction tasks. Calvo et al. (2009b) propose a non language%dependent model based on the Word Space Model for calculating the plausibility of candidate arguments given one verb and one argument, and compare with the single latent variable PLSI algorithm method, outperforming it. In this work we replicate their experiments with a different corpus, and explore variants to the PLSI method in order to explore further capabilities of this latter widely used technique. Particularly, we propose using an interpolated PLSI scheme that allows the combination of multiple latent semantic variables, and validate it in a task of identifying the real dependency%pair triple with re gard to an artificially created one, obtaining up to 83% recall.
Designing Mobile Applications for Emergency Response: Citizens Acting as Human Sensors

When an emergency occurs, citizens can be a helpful support for the operation centers involved in the response activities. As witnesses to a crisis, they initially can share updated and detailed information about what is going on. Moreover, thanks to the current technological evolution people are able to quickly and easily gather rich information and transmit it through different communication channels. Indeed, modern mobile devices embed several sensors such as GPS receivers, Wi-Fi, accelerometers or cameras that can transform users into well-equipped human sensors. For these reasons, emergency organizations and small and medium enterprises have demonstrated a growing interest in developing smart applications for reporting any exceptional circumstances. In this paper, we present a practical study about this kind of applications for identifying both limitations and common features. Based on a study of relevant existent contributions in this area and our personal direct experience in developing and evaluating emergency management solutions, our aim is to propose several findings about how to design effective and efficient mobile emergency notification applications. For this purpose we have exploited the basic sensors of modern mobile devices and the users’ aptitude for using them. The evaluation consists of a practical and a theoretical part. In the practical part, we have simulated a traffic accident as closely as possible to a real scenario, with a victim lying on the ground near a car in the middle of a street. For the theoretical part, we have interviewed some emergency experts for collecting their opinions about the utility of the proposed solution. Results from this evaluation phase confirm the positive impact that EN application have for both operators’ and citizens’ perspective. Moreover, we collected several findings useful for future design challenges in the same area, as shown in the final redesign of the proposed application.
Subpixel-precise extraction of watersheds

An approach to extract watersheds and watercourses, as well as their corresponding valleys and hills, from images with subpixel precision is proposed. The critical points of the terrain are essential as the starting points for the construction of these separatrices. They are extracted efficiently with subpixel precision using an approach based on derivatives of Gaussian filters. The separatrices are extracted by integrating their defining differential equation. Finally, the hills and valleys are constructed by an efficient graph search algorithm. Examples show the quality of the results that can be achieved with the proposed approach.
Keeping Track of How Users use Client Devices - An Asynchronous Client-Side Event Logger Model

Keywords: Event logger, usage logger, client-side logger. Abstract: Web Usage Mining usually considers server logs as a data source for collecting patterns of usage data. This solution presents limitations when the goal is to represent how users interact with specific user interface elements, since this approach may not have detailed information about users’ actions. This paper presents a model for logging client-side events and an implementation of it as a websites evaluation tool. By using the model presented here, miner systems can capture detailed Web usage data, making possible a fine-grained examination of Web pages usage. In addition, the model can help Human-Computer Interaction practitioners to log client-side events of mobile devices, set-top boxes, Web pages, among other artefacts.
Special Thematic Section on " Societal Change " Insights from Societal Psychology : The Contextual Politics of Change

In this paper we demonstrate that societal psychology makes a unique contribution to the study of change through its focus on the 'contextual politics' of change, examining the different interests at stake within any social context. Societal psychology explores the contexts which promote or inhibit social and societal change and can be seen as a bridge between social and political psychology. It focuses on how the context shapes the ways in which societal change is understood, supported or resisted. To understand the intellectual rationale of societal psychology, and how it aims to foster societal change, we first consider the history of the discipline. Second, we consider what is meant by 'context', as understanding the environment of change is the hallmark of societal psychology. Third, we lay out three distinct features of a societal psychological approach to change: the politics of change; interventions and planned change; emergent change processes. Finally, the paper examines possible future developments of societal psychology and its role in understanding and creating societal change, alongside its place within the wider canon of social and political psychology.
Bare and indefinite NPs in predicative position in French

This paper proposes a new analysis of the use of bare nouns vs. indefinite NPs in predicative position in French. We distinguish between predicational sentences (with the bare noun version) and equative sentences (with the indefinite version). We argue that bare nouns ascribe permanent properties to aspects of entities. As for the indefinites, we claim that they exhibit their specific reading and introduce an individual in a new situation, which is identified with the referent of the subject.
Window-based Stereo Matching Algorithm Using a Weighted Average of Costs Aggregated with Window Size Reduction

In the intensity-based stereo matching algorithm, window-based and coarse-to-fine algorithms are known as typical approaches. The window-based algorithm matches intensity values within windows between two stereo images. The conventional window-based algorithms, however, face a trade-off between error rates of the disparity map in disparity continuity and discontinuity regions due to the window size dependence. To solve the problem, the coarse-to-fine algorithm has been proposed [2-3]. The algorithm starts the matching process by the largest window size and gradually decreases the window size with narrowing a rage of candidates. The algorithm, however, often cannot find true disparities due to a limitation of candidates of disparity. To solve these issues, we proposed a new algorithm of the window-based stereo matching.
Preliminary Localization Results With An RFID Based Indoor Guiding System

This paper reports preliminary work with an RFID based local positioning system (LPS) designed for location and guidance of people and autonomous vehicles in indoor environments. The system consists of an RF reader carried by the mobile user, and a number of active RFID tags, disseminated at known positions in the displacement region, which regularly emit RF signals with an identification code. Upon reception of a signal, the range of the user to the corresponding tag is estimated indirectly from the received signal strength (RSSI), using a previously obtained statistical model. A computationally efficient Bayesian localization method (particle filter) is used to process the measurements and produce an estimation of the user's position. The RFID-LPS is tested empirically in a displacement region comprised of three adjacent rooms, with a total area of 250 m2, in which there are placed 21 tags. Our first results show a typical mean positioning error of 3.25 m, which compares favorably with other systems reported in the literature.
The Price Impact of Borrowing and Short-Sale Constraints

This study explores how trading constraints such as borrowing and short-sale constraints affect asset prices in the presence of asymmetric information. In a symmetric information environment, borrowing and short-sale constraints exacerbate downward and upward price movements, respectively. However, in the presence of information asymmetry, the price impact of each constraint is different. In an asymmetric information environment, prices play an important role in shaping uninformed investor expectation. Uninformed investors are uncertain whether trading constraints restrict informed investors from transmitting information to prices, and thus they demand an information-disadvantaged premium in holding stocks. This creates a large price decline. Hence, information asymmetry combined with short-sale constraints dampens the upward price movement while information asymmetry combined with borrowing constraints intensifies the downward price movement. The model also generates the following empirical predictions: 1) prices at extreme tails are less informative of the asset fundamental; 2) bad news creates greater return volatility than good news; 3) crashes are more likely than bubbles; and 4) the skewness in returns is more pronounced in stocks with severe information asymmetry and with a greater percentage of constrained informed investors. Journal of Economic Literature Classification Codes: D82, D84, G14.
Finite-Size Effects in the Dependency Networks of Free and Open-Source Software

We propose a continuum model for the degree distribution of directed networks in free and open-source software. The degree distributions of links in both the in-directed and out-directed dependency networks follow Zipf's law for the intermediate nodes, but the heavily linked nodes and the poorly linked nodes deviate from this trend and exhibit finite-size effects. The finite-size parameters make a quantitative distinction between the in-directed and out-directed networks. For the out-degree distribution, the initial condition for a dynamic evolution corresponds to the limiting count of the most heavily liked nodes that the out-directed network can finally have. The number of nodes contributing out-directed links grows with every generation of software release, but this growth ultimately saturates towards a terminal value due to the finiteness of semantic possibilities in the network.
Improvement of Collaborative Filtering with the Simple Bayesian Classifier 1

Collaborative-filtering-enabled Web sites that recommend books, CDs, movies, and so on, have become very popular on the Internet. Such sites recommend items to a user on the basis of the opinions of other users with similar tastes. In this paper, we discuss an approach to collaborative filtering based on the Simple Bayesian Classifier, and apply our model to two variants of the collaborative filtering. One is user-based collaborative filtering, which makes predictions based on the users’ similarities. The other is item-based collaborative filtering, which makes predictions based on the items’ similarities. In our approach, the similarity between users or items is calculated from negative ratings and positive ratings separately. To evaluate our algorithms, we used a database of movie recommendations. Our empirical results show that our proposed Bayesian approaches outperform typical correlation-based collaborative filtering algorithms. We also discuss an approach that combines user-based and item-based collaborative filtering with the Simple Bayesian Classifier to improve the performance of the predictions. After the user-item rating matrix has been filled out with pseudo-scores generated by the item-based filter, the user-based recommendation is applied to the matrix. We show that the combined method performs better than the single collaborative recommendation method.
Online User Location Inference Exploiting Spatiotemporal Correlations in Social Streams

The location profiles of social media users are valuable for various applications, such as marketing and real-world analysis. As most users do not disclose their home locations, the problem of inferring home locations has been well studied in recent years. In fact, most existing methods perform batch inference using static (i.e., pre-stored) social media contents. However, social media contents are generated and delivered in real-time as social streams. In this situation, it is important to continuously update current inference results based on the newly arriving contents to improve the results over time. Moreover, it is effective for location inference to use the spatiotemporal correlation between contents and locations. The main idea of this paper is that we can infer the locations of users who simultaneously post about a local event (e.g., earthquakes). Hence, in this paper, we propose an online location inference method over social streams that exploits the spatiotemporal correlation, achieving 1) continuous updates with low computational and storage costs, and 2) better inference accuracy than that of existing methods. The experimental results using a Twitter dataset show that our method reduces the inference error to less than 68% of existing methods. The results also show that the proposed method can update inference results in constant time regardless of the amount of accumulated contents.
Obtaining information Engaging in deliberation Participating in political action Pre-requisite for Desirable prior to

This paper discusses the potential of information and communication technologies (ICTs) to help engage people in all parts of the political process: obtaining information, engaging in deliberation and participating in decision-making. It also discusses limitations or barriers to using ICTs in these ways. Despite these limitations ICTs are likely to be increasingly tried in the political process. It is therefore important that we educate our young people for participation in an e-enabled political process. The paper therefore reports on an educational project that demonstrated using ICTs in the political process and introduced some 13-14 year olds to citizenship and electronic democracy, concentrating on a local mayoral election. The responses of the participants raise interesting issues about how to use ICTs in education and the desirability, or otherwise, of electronic electioneering. The paper contributes to our understanding and experience of citizenship education, e-democracy and the use of ICTs in the political process.
Accounting for coalescent stochasticity in testing phylogeographical hypotheses: modelling Pleistocene population structure in the Idaho giant salamander Dicamptodon aterrimus

Several theoretical studies have demonstrated the importance of accounting for coalescent stochasticity in phylogeographical studies, however, there are few empirical examples that do so in the context of explicit hypothesis testing. Here, we provide an example from the Idaho giant salamander (Dicamptodon aterrimus) using 118 mtDNA sequences, nearly 2 kb in length. This species is endemic to mesic forests in northern and central Idaho, and several a priori hypotheses have been erected based both on palaeoclimatic grounds and from phylogeographical studies of codistributed amphibians. Phylogenetic analysis of the D. aterrimus data suggests an expansion from a single refugium south of the Salmon River, whereas the inference from nested clade analysis is one of expansion from a single refugium in the Clearwater drainage. Explicit testing of these hypotheses, using geographically structured coalescent simulations to erect null distributions, indicates we can reject expansion from the Clearwater drainage (pCLW = 0.089), but not expansion from the South Fork of the Salmon drainage (pSAL = 0.329). Furthermore, data from codistributed amphibians suggest that there may have been two refugia, and an amova shows that most of the molecular variance partitioned between the Clearwater and the Salmon drainages (54.40%; P < 0.001) and within drainages (43.61%; P < 0.001). As a result, we also tested three a priori hypotheses which predicted that both the Clearwater and Salmon drainages functioned as refugia during the late Pleistocene; we could reject (PCORD = 0.019) divergence dates during the Cordilleran glacial maxima [c. 20 000 years before present (ybp)], during the Sangamon interglacial (c. 35 000 ybp; pSANG = 0.032), as well as pre‐Pleistocene divergence (c. 1.7 Ma; ppP < 0.001). Mismatch distributions and Tajima's D within the individual drainages provide further support to recent population expansion. This work demonstrates coalescent stochasticity is an important phenomenon to consider in testing phylogeographical hypotheses, and suggests that analytical methods which fail to sufficiently quantify this uncertainty can lead to false confidence in the conclusions drawn from these methods.