Query: Region-based CNN for Logo Detection

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The Pascal Visual Object Classes (VOC) Challenge <=SEP=> The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection. This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.

Visualizing and Understanding Convolutional Networks <=SEP=> Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark (Krizhevsky et al., 2012). However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we address both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. Used in a diagnostic role, these visualizations allow us to find model architectures that outperform Krizhevsky et al. on the ImageNet classification benchmark. We also perform an ablation study to discover the performance contribution from different model layers. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets.

Vehicle Logo Recognition Using a SIFT-Based Enhanced Matching Scheme <=SEP=> In this paper, a new algorithm for vehicle logo recognition on the basis of an enhanced scale-invariant feature transform (SIFT)-based feature-matching scheme is proposed. This algorithm is assessed on a set of 1200 logo images that belong to ten distinctive vehicle manufacturers. A series of experiments are conducted, splitting the 1200 images to a training set and a testing set, respectively. It is shown that the enhanced matching approach proposed in this paper boosts the recognition accuracy compared with the standard SIFT-based feature-matching method. The reported results indicate a high recognition rate in vehicle logos and a fast processing time, making it suitable for real-time applications.

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks <=SEP=> State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet <xref ref-type="bibr" rid="ref1">[1]</xref> and Fast R-CNN <xref ref-type="bibr" rid="ref2">[2]</xref> have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a <italic>Region Proposal Network</italic> (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features—using the recently popular terminology of neural networks with ’attention’ mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model <xref ref-type="bibr" rid="ref3">[3]</xref> , our detection system has a frame rate of 5 fps (<italic>including all steps</italic>) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.

Scalable triangulation-based logo recognition <=SEP=> We propose a scalable logo recognition approach that extends the common bag-of-words model and incorporates local geometry in the indexing process. Given a query image and a large logo database, the goal is to recognize the logo contained in the query, if any. We locally group features in triples using multi-scale Delaunay triangulation and represent triangles by signatures capturing both visual appearance and local geometry. Each class is represented by the union of such signatures over all instances in the class. We see large scale recognition as a sub-linear search problem where signatures of the query image are looked up in an inverted index structure of the class models. We evaluate our approach on a large-scale logo recognition dataset with more than four thousand classes.

Algorithm 777: HOMPACK90: A Suite of Fortran 90 Codes for Globally Convergent Homotopy Algorithms <=SEP=> HOMPACK90 is a Fortran 90 version of the Fortran 77 package HOMPACK (Algorithm 652), a collection of codes for finding zeros or fixed points of nonlinear systems using globally convergent probability-one homotopy algorithms. Three qualitatively different algorithms— ordinary differential equation based, normal flow, quasi-Newton augmented Jacobian matrix—are provided for tracking homotopy zero curves, as well as separate routines for dense and sparse Jacobian matrices. A high level driver for the special case of polynomial systems is also provided. Changes to HOMPACK include numerous minor improvements, simpler and more elegant interfaces, use of modules, new end games, support for several sparse matrix data structures, and new iterative algorithms for large sparse Jacobian matrices.

Evaluation of the Spiritual Well-Being Scale in a Sample of Korean Adults <=SEP=> This study explored the psychometric qualities and construct validity of the Spiritual Well-Being Scale (SWBS; Ellison in J Psychol Theol 11:330–340, 1983) using a sample of 470 Korean adults. Two factor analyses, exploratory factor analysis and confirmatory factor analysis, were conducted in order to test the validity of the SWBS. The results of the factor analyses supported the original two-dimensional structure of the SWBS—religious well-being (RWB) and existential well-being (EWB) with method effects associated with negatively worded items. By controlling for method effects, the evaluation of the two-factor structure of SWBS is confirmed with clarity. Further, the differential pattern and magnitude of correlations between the SWB subscales and the religious and psychological variables suggested that two factors of the SWBS were valid for Protestant, Catholic, and religiously unaffiliated groups except Buddhists. The Protestant group scored higher in RWB compared to the Buddhist, Catholic, and unaffiliated groups. The Protestant group scored higher in EWB compared to the unaffiliated groups. Future studies may need to include more Buddhist samples to gain solid evidence for validity of the SWBS on a non-Western religious tradition.

Fuzzy Set Theory ― and Its Applications <=SEP=> Spend your time even for only few minutes to read a book. Reading a book will never reduce and waste your time to be useless. Reading, for some people become a need that is to do every day such as spending time for eating. Now, what about you? Do you like to read a book? Now, we will show you a new book enPDFd fuzzy set theory and its applications that can be a new way to explore the knowledge. When reading this book, you can get one thing to always remember in every reading time, even step by step.

Towards Text Generation with Adversarially Learned Neural Outlines <=SEP=> Recent progress in deep generative models has been fueled by two paradigms – autoregressive and adversarial models. We propose a combination of both approaches with the goal of learning generative models of text. Our method first produces a high-level sentence outline and then generates words sequentially, conditioning on both the outline and the previous outputs. We generate outlines with an adversarial model trained to approximate the distribution of sentences in a latent space induced by general-purpose sentence encoders. This provides strong, informative conditioning for the autoregressive stage. Our quantitative evaluations suggests that conditioning information from generated outlines is able to guide the autoregressive model to produce realistic samples, comparable to maximum-likelihood trained language models, even at high temperatures with multinomial sampling. Qualitative results also demonstrate that this generative procedure yields natural-looking sentences and interpolations.

Service-oriented Communities: Models and Concepts Towards Fractal Social Organizations <=SEP=> It is described an abstract model for the definition and the dynamic evolution of "communities of actants" originating from a given reference society of roles. Multiple representations are provided, showing how communities evolve with respect to their reference societies. In particular we show how such representations are self-similar and factorisable into "prime" constituents. An operating model is then introduced that describes the life-cycle of the communities of actants. After this a software component is presented -- the service-oriented community -- and its features are described in terms of the above mentioned models. Finally it is shown how such component can constitute the building block of a novel architecture for the design of fractal social organizations.

Distressed Stocks in Distressed Times <=SEP=> Financially distressed stocks do not underperform healthy stocks when the entire economy is in distress. The asset beta and financial leverage of distressed stocks rise significantly after major market downturns, resulting in a dramatic increase in equity beta. Hence, a long/short healthyminus-distressed trading strategy leads to significant losses when the market rebounds. Managing this risk mitigates the severe losses of financial distress strategies, and significantly improves their Sharpe ratios.

Real-time three-dimensional smooth path planning for unmanned aerial vehicles in completely unknown cluttered environments <=SEP=> This paper presents a real-time three-dimensional path planning algorithm for improving autonomous navigation of Unmanned Aerial Vehicles (UAVs) operating in completely unknown cluttered environments. The algorithm generates smooth paths consisting of continuous piecewise Bézier curves in real time. Specifically, a RRT-based waypoint generation algorithm is firstly proposed for the exploration of collision-free waypoints successively during flight. Besides, a novel real-time path smoothing technique is developed to generate continuous collision-free paths that satisfy the motion constraints of UAVs. This is achieved by fitting Bézier curves between consecutive waypoints based on the particle swarm optimization (PSO) algorithm. Lastly, a path selection strategy is also introduced to seek for an optimum path when multiple solutions are available. The simulation results demonstrate the superiority of the proposed real-time three-dimensional smooth path planning algorithm.

Automatic Generation of Visual-Textual Presentation Layout <=SEP=> Visual-textual presentation layout (e.g., digital magazine cover, poster, Power Point slides, and any other rich media), which combines beautiful image and overlaid readable texts, can result in an eye candy touch to attract users’ attention. The designing of visual-textual presentation layout is therefore becoming ubiquitous in both commercially printed publications and online digital magazines. However, handcrafting aesthetically compelling layouts still remains challenging for many small businesses and amateur users. This article presents a system to automatically generate visual-textual presentation layouts by investigating a set of aesthetic design principles, through which an average user can easily create visually appealing layouts. The system is attributed with a set of topic-dependent layout templates and a computational framework integrating high-level aesthetic principles (in a top-down manner) and low-level image features (in a bottom-up manner). The layout templates, designed with prior knowledge from domain experts, define spatial layouts, semantic colors, harmonic color models, and font emotion and size constraints. We formulate the typography as an energy optimization problem by minimizing the cost of text intrusion, the utility of visual space, and the mismatch of information importance in perception and semantics, constrained by the automatically selected template and further preserving color harmonization. We demonstrate that our designs achieve the best reading experience compared with the reimplementation of parts of existing state-of-the-art designs through a series of user studies.

Application of data mining methods for link prediction in social networks <=SEP=> Using social networking services is becoming more popular day by day. Social network analysis views relationships in terms of nodes (people) and edges (links or connections—the relationship between the people). The websites of the social networks such as Facebook currently are among the most popular internet services just after giant portals such as Yahoo, MSN and search engines such as Google. One of the main problems in analyzing these networks is the prediction of relationships between people in the network. It is hard to find one method that can identify relation between people in the social network. The purpose of this paper is to forecast the friendship relation between individuals among a social network, especially the likelihood of a relation between an existing member with a new member. For this purpose, we used a few hypotheses to make the graph of relationships between members of social network, and we used the method of logistic regression to complete the graph. Test data from Flickr website are used to evaluate the proposed method. The results show that the method has achieved 99 % accuracy in prediction of friendship relationships.

Energy Management of Fuel Cell/Battery/Supercapacitor Hybrid Power Sources Using Model Predictive Control <=SEP=> Well known as an efficient and eco-friendly power source, fuel cell, unfortunately, offers slow dynamics. When attached as primary energy source in a vehicle, fuel cell would not be able to respond to abrupt load variations. Supplementing battery and/or supercapacitor to the system will provide a solution to this shortcoming. On the other hand, a current regulation that is vital for lengthening time span of the energy storage system is needed. This can be accomplished by keeping fuel cell's and batteries' current slope in reference to certain values, as well as attaining a stable dc output voltage. For that purpose, a feedback control system for regulating the hybrid of fuel cell, batteries, and supercapacitor was constructed for this study. Output voltage of the studied hybrid power sources (HPS) was administered by assembling three dc-dc converters comprising two bidirectional converters and one boost converter. Current/voltage output of fuel cell was regulated by boost converter, whereas the bidirectional converters regulated battery and supercapacitor. Reference current for each converter was produced using Model Predictive Control (MPC) and subsequently tracked using hysteresis control. These functions were done on a controller board of a dSPACE DS1104. Subsequently, on a test bench made up from 6 V, 4.5 Ah battery and 7.5 V, 120 F supercapacitor together with a fuel cell of 50 W, 10 A, experiment was conducted. Results show that constructing a control system to restrict fuel cell's and batteries' current slope and maintaining dc bus voltage in accordance with the reference values using MPC was feasible and effectively done.

Interventions shown to aid executive function development in children 4 to 12 years old. <=SEP=> To be successful takes creativity, flexibility, self-control, and discipline. Central to all those are executive functions, including mentally playing with ideas, giving a considered rather than an impulsive response, and staying focused. Diverse activities have been shown to improve children's executive functions: computerized training, noncomputerized games, aerobics, martial arts, yoga, mindfulness, and school curricula. All successful programs involve repeated practice and progressively increase the challenge to executive functions. Children with worse executive functions benefit most from these activities; thus, early executive-function training may avert widening achievement gaps later. To improve executive functions, focusing narrowly on them may not be as effective as also addressing emotional and social development (as do curricula that improve executive functions) and physical development (shown by positive effects of aerobics, martial arts, and yoga).

Jointly Embedding Entities and Text with Distant Supervision <=SEP=> Learning representations for knowledge base entities and concepts is becoming increasingly important for NLP applications. However, recent entity embedding methods have relied on structured resources that are expensive to create for new domains and corpora. We present a distantly-supervised method for jointly learning embeddings of entities and text from an unnanotated corpus, using only a list of mappings between entities and surface forms. We learn embeddings from open-domain and biomedical corpora, and compare against prior methods that rely on human-annotated text or large knowledge graph structure. Our embeddings capture entity similarity and relatedness better than prior work, both in existing biomedical datasets and a new Wikipedia-based dataset that we release to the community. Results on analogy completion and entity sense disambiguation indicate that entities and words capture complementary information that can be effectively combined for downstream use.

Universal Language Model Fine-tuning for Text Classification <=SEP=> Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 1824% on the majority of datasets. Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100× more data. We opensource our pretrained models and code1.

Normative Social Influence in Persuasive Technology: Intensity versus Effectiveness <=SEP=> Persuasion is a form of social influence that implies intentional but voluntary change of a person’s behaviours, feelings or thoughts about an issue, object or action. Successful persuasion by means of persuasive technology relies on a number of factors and motivators. Recently, the so called social acceptance or rejection motivator has formed the backbone of a new type of persuasion, called Mass Interpersonal Persuasion, or MIP for short. This type of persuasion uses the social influence that is generated on online social networks to persuade people. Though it has been established that normative social influence can be used effectively in persuasive technology, it is unknown if the application of more social pressure also makes it more effective. In order to test the hypothesis that the effectiveness of persuasion increases when the persuasion becomes more intense, a quantitative experiment was conducted on the online social network of Facebook. Although evidence to support the hypothesis was found, it cannot be concluded from this experiment that when utilizing normative social influence in persuasive technology more intense persuasion is also more effective.

Hadoop Oriented Smart Cities Architecture <=SEP=> A smart city implies a consistent use of technology for the benefit of the community. As the city develops over time, components and subsystems such as smart grids, smart water management, smart traffic and transportation systems, smart waste management systems, smart security systems, or e-governance are added. These components ingest and generate a multitude of structured, semi-structured or unstructured data that may be processed using a variety of algorithms in batches, micro batches or in real-time. The ICT architecture must be able to handle the increased storage and processing needs. When vertical scaling is no longer a viable solution, Hadoop can offer efficient linear horizontal scaling, solving storage, processing, and data analyses problems in many ways. This enables architects and developers to choose a stack according to their needs and skill-levels. In this paper, we propose a Hadoop-based architectural stack that can provide the ICT backbone for efficiently managing a smart city. On the one hand, Hadoop, together with Spark and the plethora of NoSQL databases and accompanying Apache projects, is a mature ecosystem. This is one of the reasons why it is an attractive option for a Smart City architecture. On the other hand, it is also very dynamic; things can change very quickly, and many new frameworks, products and options continue to emerge as others decline. To construct an optimized, modern architecture, we discuss and compare various products and engines based on a process that takes into consideration how the products perform and scale, as well as the reusability of the code, innovations, features, and support and interest in online communities.

Customer Churn Prediction Modelling Based on Behavioural Patterns Analysis using Deep Learning <=SEP=> Customer churn refers to when a customer ceases their relationship with a company. A churn rate, used to estimate growth, is now considered as important a metric as financial profit. With growing competition in the market, companies are desperate to keep the churn rate as low as possible. Thus, churn prediction has gained critical importance, not just for existing customers, but also for predicting trends of future customers. This paper demonstrates prediction of churn on a Telco dataset using a Deep Learning Approach. A multilayered Neural Network was designed to build a non-linear classification model. The churn prediction model works on customer features, support features, usage features and contextual features. The possibility of churn as well as the determining factors are predicted. The trained model then applies the final weights on these features and predict the possibility of churn for that customer. An accuracy of 80.03% was achieved. Since the model also provides the churn factors, it can be used by companies to analyze the reasons for these factors and take steps to eliminate them.

' s personal copy Comments and Controversies Ten ironic rules for non-statistical reviewers <=SEP=> In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t a r t i c l e i n f o Keywords: Statistical testing Sample-size Effect size Power Classical inference As an expert reviewer, it is sometimes necessary to ensure a paper is rejected. This can sometimes be achieved by highlighting improper statistical practice. This technical note provides guidance on how to critique the statistical analysis of neuroimaging studies to maximise the chance that the paper will be declined. We will review a series of critiques that can be applied universally to any neuroimaging paper and consider responses to potential rebuttals that reviewers might encounter from authors or editors. Introduction This technical note is written for reviewers who may not have sufficient statistical expertise to provide an informed critique during the peer-reviewed process, but would like to recommend rejection on the basis of inappropriate or invalid statistical analysis. This guidance follows the 10 simple rules format and hopes to provide useful tips and criticisms for reviewers who find themselves in this difficult position. These rules are presented for reviewers in an ironic way 1 that makes it easier (and hopefully more entertaining) to discuss the issues from the point of view of both the reviewer and author — and to caricature both sides of the arguments. Some key issues are presented more formally in (non-ironic) appendices. There is a perceived need to reject peer-reviewed papers with the advent of open access publishing and the large number of journals available to authors. Clearly, there may be idiosyncratic reasons to block a paper – to ensure your precedence in the literature, personal rivalry etc. – however, we will assume that there is an imperative to reject papers for the good of the community: handling editors are often happy to receive recommendations to decline a paper. This is because they are placed under pressure to maintain a high rejection rate. This pressure is usually exerted by the editorial board (and publishers) and enforced by circulating quantitative information about their rejection rates (i.e., naming and shaming lenient editors). All journals want to maximise rejection rates, because this increases the quality of submissions, increases their impact factor …

From Visual Data Exploration to Visual Data Mining: A Survey <=SEP=> We survey work on the different uses of graphical mapping and interaction techniques for visual data mining of large data sets represented as table data. Basic terminology related to data mining, data sets, and visualization is introduced. Previous work on information visualization is reviewed in light of different categorizations of techniques and systems. The role of interaction techniques is discussed, in addition to work addressing the question of selecting and evaluating visualization techniques. We review some representative work on the use of information visualization techniques in the context of mining data. This includes both visual data exploration and visually expressing the outcome of specific mining algorithms. We also review recent innovative approaches that attempt to integrate visualization into the DM/KDD process, using it to enhance user interaction and comprehension.

LaserStroke: Mid-air Tactile Experiences on Contours Using Indirect Laser Radiation <=SEP=> This demonstration presents a novel form of mid-air tactile display, LaserStroke, that makes use of a laser irradiated on the elastic medium attached to the skin. LaserStroke extends a laser device with an orientation control platform and a magnetic tracker so that it can elicit tapping and stroking sensations to a user's palm from a distance. LaserStroke offers unique tactile experiences while a user freely moves his/her hand in midair.

Cannabis - from cultivar to chemovar. <=SEP=> The medicinal use of Cannabis is increasing as countries worldwide are setting up official programs to provide patients with access to safe sources of medicinal-grade Cannabis. An important question that remains to be answered is which of the many varieties of Cannabis should be made available for medicinal use. Drug varieties of Cannabis are commonly distinguished through the use of popular names, with a major distinction being made between Indica and Sativa types. Although more than 700 different cultivars have already been described, it is unclear whether such classification reflects any relevant differences in chemical composition. Some attempts have been made to classify Cannabis varieties based on chemical composition, but they have mainly been useful for forensic applications, distinguishing drug varieties, with high THC content, from the non-drug hemp varieties. The biologically active terpenoids have not been included in these approaches. For a clearer understanding of the medicinal properties of the Cannabis plant, a better classification system, based on a range of potentially active constituents, is needed. The cannabinoids and terpenoids, present in high concentrations in Cannabis flowers, are the main candidates. In this study, we compared cultivars obtained from multiple sources. Based on the analysis of 28 major compounds present in these samples, followed by principal component analysis (PCA) of the quantitative data, we were able to identify the Cannabis constituents that defined the samples into distinct chemovar groups. The study indicates the usefulness of a PCA approach for chemotaxonomic classification of Cannabis varieties.

Shunt active filter for harmonic and reactive power compensation using p-q theory <=SEP=> Now-a-days, decadence of power quality is foremost issue of electrical society. In practice, utility of switching device is increased in industrial as well as in domestic applications. Non-linearity causes adverse effects on system efficiency, utility of the power supply, power factor, etc. As reduction in power factor increases reactive power which does not have any contribution in energy transfer so its compensation is needed. So efforts are made to upgrade power quality, concept of Filter is in demand. In contrast to passive filter, active filters are popular due to its smaller size and weight. In this paper, work is done on shunt active power filter (SAPF) using P-Q theory for current harmonic mitigation and reactive power compensation. Here the simulation as well as its parameters, with or without active filter is well presented. We commence the paper with power quality issues then better understanding of PQ theory, simulation circuits, results, comparison and finally ended with conclusion. An effort is made to achieve THD value of source current below 5% to meet the required IEEE standards.

Generating timeline summaries with social media attention <=SEP=> Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of one given topic. Previous methods simply split the time span into fixed, equal time intervals without studying the role of the evolutionary patterns of the underlying topic in timeline generation. In addition, few of these methods take users’ collective interests into considerations to generate timelines. We consider utilizing social media attention to address these two problems due to the facts: 1) social media is an important pool of real users’ collective interests; 2) the information cascades generated in it might be good indicators for boundaries of topic phases. Employing Twitter as a basis, we propose to incorporate topic phases and user’s collective interests which are learnt from social media into a unified timeline generation algorithm.We construct both one informativeness-oriented and three interestingness-oriented evaluation sets over five topics.We demonstrate that it is very effective to generate both informative and interesting timelines. In addition, our idea naturally leads to a novel presentation of timelines, i.e., phase based timelines, which can potentially improve user experience.

THE EVOLUTION OF ACCOUNTING INFORMATION SYSTEMS <=SEP=> Technological evolution becomes more and more a daily reality for businesses and individuals who use information systems as for supporting their operational activities. This article focuses on the way technological evolution changes the accounting practices, starting from the analysis of the traditional model and trying to determine future trends and arising challenges to face. From data input to consolidation and reporting, accountants’ function and operations are dissected in order to identify to what extent the development of new concepts, such as cloud computing, cloud accounting, real-time accounting or mobile accounting may affect the financial-accounting process, as well as the challenges that arise from the changing environment. SEA Practical Application of Science Volume III, Issue 1 (7) / 2015 92 Introduction Technology evolves rapidly as for responding to customers demand (Weiß and Leimeister, 2012). From a business perspective, more and more companies acknowledge the fact that technology may support process optimisation in terms of costs, lead time and involved resources. The actual market context is driving companies to continuously search for new ways to optimise their processes and increase their financial indicators (Christauskas and Miseviciene, 2012). The company’s efficiency is in direct relation with the objective and timely financial information provided by the accounting system, the main purpose of which is to collect and record information regarding transactions or events with economic impact upon the company, as well as to process and provide relevant, fair information to stakeholders (both internal and external) (Girdzijauskas, et al., 2008; Kundeliene, 2011). The accounting system is thus essential in providing reliable, relevant, significant and useful information for the users of financial data (Kalcinskaite, 2009). From the deployment of basic accounting program or the use of large ERP systems (Enterprise Resource Planning), to the creation of World-Wide Web and development of web-based communication, technology increased its development in speed and implications. Today, concepts such as cloud computing, cloud accounting or real-time reporting are more and more a daily reality among companies. But as technology evolves at an incredible speed, it is necessary for companies to adapt their processes and practices to the new trends. However, that could not be possible without the decision factors to completely acknowledge the implications of the new technology, how it can be used to better manage their businesses and what challenges it also brings along. The present study is based on the theoretical and empirical analysis of accounting process from the appearance of the first accounting information systems up to nowadays’ services and techniques available for supporting the accounting function of a company. The research comprised of analysing accounting operations, activities and practices as they followed the technological evolution for over than 20 years, focusing on a general level, applicable to companies of all sizes and business sectors. Although the geographic extent was limited to the European Union, the study may as well be considered globally applicable, considering the internationality of today’s technology (e.g. cloud computing services may be used by a company in Japan while the server is physically located in the USA). Traditional practices and future trends The accounting systems may be seen as aiming to support businesses in collecting, understanding and analysing the financial data (Chytilova et al., 2011). The evolution of software accounting generation may be split, according toPhillips (2012) into three major categories: ▪ 90’s era – marked by the apparition of the first accounting software programs under what is known as ‘the Windows age’; applications were solid, but only supporting basic accounting operations. ▪ 00’s era – ‘integration’ and ‘SaaS’ concepts took birth, bringing along more developed systems that would allow more complex accounting operations and data processing, as well as concurrent access to files and programs. ▪ 2010 – on-going – ‘Mobile’ accounting era, marked by real-time accounting, financial dashboards and other mobile applications supporting financial processing and reporting. The same author outlines the evolution of communication – if the traditional accounting model was based on e-mail or .ftp files communication, the technological evolution now allows sharing and concurrent access to data, through virtual platforms provided by cloud computing technology. Based on the types of accounting services available on the market, three major categories may be defined: ▪ On-premises accounting: a dedicated accounting software program is purchased by the company and installed using its own infrastructure. Investment in the software and hardware equipment is required for such programs. ▪ Hosted solutions: the logical access is remotely performed through the company’s installed programs; however the data centre is physically located in a different place, managed by a dedicated third party. Infrastructure costs are reduced for the company, as hardware is administered and maintained by the service provider. ▪ Cloud computing: the service could prove even more cost efficient for companies, as the data is managed through virtual platforms, and administered by a dedicated third party, allowing multi-tenancy of services in order to split fixed infrastructure costs between companies. Traditional accounting practices used to focus on bookkeeping and financial reporting, having as a final purpose the preparation and presentation of financial statements. The activities were driven by the need of financial information users (both internal and external) to gain a ‘fair view’ of the company. The objective was often SEA Practical Application of Science Volume III, Issue 1 (7) / 2015 93 reached through the use of small, atomic systems meant to support the reporting; nevertheless, collection of documents, processing of data and operations, posting of journal entries, as well as consolidation and final reporting operations were mostly manual, and manual controls (reconciliations, validations, etc.) were performed as systems did not communicate through automated interfaces. In early 1920s, the first outsourcing agreement was signed by British Petroleum with Accenture. Ever since, the accounting started changing its meaning within the companies, turning from the bookkeeping function to the management strategic and decision-making support function. The technological evolution gave birth in the late 80s to ERP (Enterprise Resource Planning) systems, used to incorporate and connect various organisational functions (accounting, asset management, operations, procurement, human resources, etc.) (Ziemba and Oblak, 2013). Ustasüleyman and Percin (2010) define the ERP systems as ‘software packages enabling the integration of business processes throughout an organisation’, while Salmeron andLopez (2010) see the ERP system as ‘a single software system allowing complete integration of information flow from all functional areas in companies by means of a single database, and accessible through a unified interface and communication channel’. The ERP systems became of common use among large companies who managed to reduce the process lead time and involved resources by automation of data transfer between ERP modules, processes within the ERP system, as well as validation and reconciliation controls. From an accounting perspective, the deployment of ERP systems represented a major change, offering support in bookkeeping (the operations performed through different modules would generate automated journal entries posting), processing and transfer of data (through automated interfaces between the ERP modules), as well as consolidation and reporting. This progress took the accountants one step away from the traditional accounting’s bookkeeping practices, and one step closer towards today’s role more close to management strategy and decision support. Further on, as a consequence of the financial-economic crisis started in 2008, the role of accountants within the company changed drastically from bookkeeping and reporting to playing an active and essential role in the management strategy and decision-making process. Thus, it was technology’s role to take in the traditional tasks and operations. More and more companies implemented automated controls for processing data, posting journal entries, consolidation and final reporting under the financial statements and other internal management reports. Soon after automating the accounting operations, technology evolved into also automating document collection and management, through development of concepts such as einvoicing, e-archiving, e-payments, etc. (Burinskiene&Burinskas, 2010). Technology proved once again responsive to the market’s demand, and thus accounting software easily customisable for each client’s particularities regarding the activity profile, accounting practices and chart of accounts, were built as for supporting the automation of accounting process. With the automation of the process, implementation of certain controls was also required as for ensuring the correctness and completeness of reported information. Technology also took into account the need for robust, transparent processes, and it was only a matter of time until cloud computing, cloud accounting or real-time reporting concepts became a daily reality among companies of all sizes, activity sectors or region/state. The access to financial information previously physically limited to the company’s premises (where the network and infrastructure would be located) was fairly improved through cloud computing to an extent where internet connection was the only condition users needed to respect in order to gain access to the financial programs and data. Cloud computing start

Interactive Topic Modeling for Exploring Asynchronous Online Conversations: Design and Evaluation of ConVisIT <=SEP=> Since the mid-2000s, there has been exponential growth of asynchronous online conversations, thanks to the rise of social media. Analyzing and gaining insights from such conversations can be quite challenging for a user, especially when the discussion becomes very long. A promising solution to this problem is topic modeling, since it may help the user to understand quickly what was discussed in a long conversation and to explore the comments of interest. However, the results of topic modeling can be noisy, and they may not match the user’s current information needs. To address this problem, we propose a novel topic modeling system for asynchronous conversations that revises the model on the fly on the basis of users’ feedback. We then integrate this system with interactive visualization techniques to support the user in exploring long conversations, as well as in revising the topic model when the current results are not adequate to fulfill the user’s information needs. Finally, we report on an evaluation with real users that compared the resulting system with both a traditional interface and an interactive visual interface that does not support human-in-the-loop topic modeling. Both the quantitative results and the subjective feedback from the participants illustrate the potential benefits of our interactive topic modeling approach for exploring conversations, relative to its counterparts.

ACCEPTANCE AND COMMITMENT THERAPY ( ACT ) IN PANIC DISORDER WITH AGORAPHOBIA : A CASE STUDY <=SEP=>