Query: The Rise of Emotion-aware Conversational Agents: Threats in Digital Emotions

Relevant passages:
Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory <=SEP=> Perception and expression of emotion are key factors to the success of dialogue systems or conversational agents. However, this problem has not been studied in large-scale conversation generation so far. In this paper, we propose Emotional Chatting Machine (ECM) that can generate appropriate responses not only in content (relevant and grammatical) but also in emotion (emotionally consistent). To the best of our knowledge, this is the first work that addresses the emotion factor in large-scale conversation generation. ECM addresses the factor using three new mechanisms that respectively (1) models the high-level abstraction of emotion expressions by embedding emotion categories, (2) captures the change of implicit internal emotion states, and (3) uses explicit emotion expressions with an external emotion vocabulary. Experiments show that the proposed model can generate responses appropriate not only in content but also in emotion.

Topic Aware Neural Response Generation <=SEP=> We consider incorporating topic information into a sequenceto-sequence framework to generate informative and interesting responses for chatbots. To this end, we propose a topic aware sequence-to-sequence (TA-Seq2Seq) model. The model utilizes topics to simulate prior human knowledge that guides them to form informative and interesting responses in conversation, and leverages topic information in generation by a joint attention mechanism and a biased generation probability. The joint attention mechanism summarizes the hidden vectors of an input message as context vectors by message attention and synthesizes topic vectors by topic attention from the topic words of the message obtained from a pre-trained LDA model, with these vectors jointly affecting the generation of words in decoding. To increase the possibility of topic words appearing in responses, the model modifies the generation probability of topic words by adding an extra probability item to bias the overall distribution. Empirical studies on both automatic evaluation metrics and human annotations show that TA-Seq2Seq can generate more informative and interesting responses, significantly outperforming state-of-theart response generation models.

It's only a computer: Virtual humans increase willingness to disclose <=SEP=> Research has begun to explore the use of virtual humans (VHs) in clinical interviews (Bickmore, Gruber, & Picard, 2005). When designed as supportive and ‘‘safe’’ interaction partners, VHs may improve such screenings by increasing willingness to disclose information (Gratch, Wang, Gerten, & Fast, 2007). In health and mental health contexts, patients are often reluctant to respond honestly. In the context of health-screening interviews, we report a study in which participants interacted with a VH interviewer and were led to believe that the VH was controlled by either humans or automation. As predicted, compared to those who believed they were interacting with a human operator, participants who believed they were interacting with a computer reported lower fear of self-disclosure, lower impression management, displayed their sadness more intensely, and were rated by observers as more willing to disclose. These results suggest that automated VHs can help overcome a significant barrier to obtaining truthful patient information. 2014 Elsevier Ltd. All rights reserved.

Ethical Artificial Intelligence <=SEP=> First Edition Please send typo and error reports, and any other comments, to hibbard@wisc.edu.

A Neural Conversational Model <=SEP=> Conversational modeling is an important task in natural language understanding and machine intelligence. Although previous approaches exist, they are often restricted to specific domains (e.g., booking an airline ticket) and require handcrafted rules. In this paper, we present a simple approach for this task which uses the recently proposed sequence to sequence framework. Our model converses by predicting the next sentence given the previous sentence or sentences in a conversation. The strength of our model is that it can be trained end-to-end and thus requires much fewer hand-crafted rules. We find that this straightforward model can generate simple conversations given a large conversational training dataset. Our preliminary results suggest that, despite optimizing the wrong objective function, the model is able to converse well. It is able extract knowledge from both a domain specific dataset, and from a large, noisy, and general domain dataset of movie subtitles. On a domainspecific IT helpdesk dataset, the model can find a solution to a technical problem via conversations. On a noisy open-domain movie transcript dataset, the model can perform simple forms of common sense reasoning. As expected, we also find that the lack of consistency is a common failure mode of our model.

STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset <=SEP=> In recent years, automatic generation of image descriptions (captions), that is, image captioning, has attracted a great deal of attention. In this paper, we particularly consider generating Japanese captions for images. Since most available caption datasets have been constructed for English language, there are few datasets for Japanese. To tackle this problem, we construct a large-scale Japanese image caption dataset based on images from MS-COCO, which is called STAIR Captions. STAIR Captions consists of 820,310 Japanese captions for 164,062 images. In the experiment, we show that a neural network trained using STAIR Captions can generate more natural and better Japanese captions, compared to those generated using English-Japanese machine translation after generating English captions.

A Wearable Computing Prototype for supporting training activities in Automotive Production <=SEP=> This paper presents the results of the wearable computing prototype supporting trainingand qualification activities at the SKODA production facilities in Czech Republic. The emerged prototype is based upon the first of the 2 main “Variant Production Showcases” (training and assembly-line) which are to be implemented in the WearIT@work project (EC IP 004216). As an introduction, the authors of this paper investigate current training processes at Skoda, and derive the potential benefits and risks of applying wearable computing technology. Accordingly, the approach of creating the wearable prototypes, via usability experiments at the Skoda production site, is explained in detail. As a preliminary result, the first functional prototypes, including a task recognition prototype, based upon the components of the European Wearable Computing Platform, are described. The paper is rounded up by providing a short outlook regarding the second envisaged test case, which is focussed upon selected assembly line operations of blue collar workers.

Towards Internet of Things (IOTS): Integration of Wireless Sensor Network to Cloud Services for Data Collection and Sharing <=SEP=> Cloud computing provides great benefits for applications hosted on the Web that also have special computational and storage requirements. This paper proposes an extensible and flexible architecture for integrating Wireless Sensor Networks with the Cloud. We have used REST based Web services as an interoperable application layer that can be directly integrated into other application domains for remote monitoring such as e-health care services, smart homes, or even vehicular area networks (VAN). For proof of concept, we have implemented a REST based Web services on an IP based low power WSN test bed, which enables data access from anywhere. The alert feature has also been implemented to notify users via email or tweets for monitoring data when they exceed values and events of interest.

Broadband biquad UHF antenna array for DOA <=SEP=> In this contribution a broadband biquad antenna array for DOA applications is investigated. Simulation and measurement results of the single broadband biquad antenna element and results of antenna array configurations for vertical polarisation, horizontal polarisation or polarisation independant direction of arrival estimation using different algorithms are shown.

Dynablock: Dynamic 3D Printing for Instant and Reconstructable Shape Formation <=SEP=> This paper introduces Dynamic 3D Printing, a fast and reconstructable shape formation system. Dynamic 3D Printing can assemble an arbitrary three-dimensional shape from a large number of small physical elements. Also, it can disassemble the shape back to elements and reconstruct a new shape. Dynamic 3D Printing combines the capabilities of 3D printers and shape displays: Like conventional 3D printing, it can generate arbitrary and graspable three-dimensional shapes, while allowing shapes to be rapidly formed and reformed as in a shape display. To demonstrate the idea, we describe the design and implementation of Dynablock, a working prototype of a dynamic 3D printer. Dynablock can form a three-dimensional shape in seconds by assembling 3,000 9 mm blocks, leveraging a 24 x 16 pin-based shape display as a parallel assembler. Dynamic 3D printing is a step toward achieving our long-term vision in which 3D printing becomes an interactive medium, rather than the means for fabrication that it is today. In this paper, we explore possibilities for this vision by illustrating application scenarios that are difficult to achieve with conventional 3D printing or shape display systems.

Design and Fabrication of Integrated Magnetic MEMS Energy Harvester for Low Frequency Applications <=SEP=> An integrated vibration-to-electrical MEMS electromagnetic energy harvester with low resonant frequency is designed, simulated, fabricated and tested. Special structures and magnet arrays of the device are designed and simulated for favorable low frequency performance. Both FEM simulations and system-level simulations are conducted to investigate the induced voltage of the device. Two types of harvester are fabricated with different magnet arrays and the magnetic field of the two harvesters is investigated. With the combination of the microfabricated metal structures and the electroplated CoNiMnP permanent micro magnets, the designed harvesters are of small size (5 mm ×5 mm × 0.53 mm) without manual assembly of conventional bulk magnets. The output power density is 0.03 μW/cm3 with optimized resistance load at 64 Hz. This magnetic harvester is suitable for batch fabrication through MEMS process and can be utilized to drive microelectronic devices, such as micro implantable and portable devices.

Network bandwidth predictor (NBP): a system for online network performance forecasting <=SEP=> The applicability of network-based computing depends on the availability of the underlying network bandwidth. However, network resources are shared and the available network bandwidth varies with time. There is no satisfactory solution available for network performance predictions. In this research, we propose, design, and implement the NBP (network bandwidth predictor) for rapid network performance prediction. NBP is a new system that employs a neural network based approach for network bandwidth forecasting. This system is designed to integrate with most advanced technologies. It employs the NWS (network weather service) monitoring subsystem to measure the network traffic, and provides an improved, more accurate performance prediction than that of NWS, especially with applications with a network usage pattern. The NBP system has been tested on real time data collected by NWS monitoring subsystem and on trace files. Experimental results confirm that NBP has an improved prediction.

A Framework for Multiple-Instance Learning <=SEP=> Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many instances, but a bag is labeled positive even if only one of the instances in it falls within the concept. A bag is labeled negative only if all the instances in it are negative. We describe a new general framework, called Diverse Density, for solving multiple-instance learning problems. We apply this framework to learn a simple description of a person from a series of images (bags) containing that person, to a stock selection problem, and to the drug activity prediction problem.

Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders <=SEP=> The Cancer Genome Atlas (TCGA) has profiled over 10,000 tumors across 33 different cancer-types for many genomic features, including gene expression levels. Gene expression measurements capture substantial information about the state of each tumor. Certain classes of deep neural network models are capable of learning a meaningful latent space. Such a latent space could be used to explore and generate hypothetical gene expression profiles under various types of molecular and genetic perturbation. For example, one might wish to use such a model to predict a tumor's response to specific therapies or to characterize complex gene expression activations existing in differential proportions in different tumors. Variational autoencoders (VAEs) are a deep neural network approach capable of generating meaningful latent spaces for image and text data. In this work, we sought to determine the extent to which a VAE can be trained to model cancer gene expression, and whether or not such a VAE would capture biologically-relevant features. In the following report, we introduce a VAE trained on TCGA pan-cancer RNA-seq data, identify specific patterns in the VAE encoded features, and discuss potential merits of the approach. We name our method "Tybalt" after an instigative, cat-like character who sets a cascading chain of events in motion in Shakespeare's "Romeo and Juliet". From a systems biology perspective, Tybalt could one day aid in cancer stratification or predict specific activated expression patterns that would result from genetic changes or treatment effects.

Review of Design of Speech Recognition and Text Analytics based Digital Banking Customer Interface and Future Directions of Technology Adoption <=SEP=> Banking is one of the most significant adopters of cutting-edge information technologies. Since its modern era beginning in the form of paper based accounting maintained in the branch, adoption of computerized system made it possible to centralize the processing in data centre and improve customer experience by making a more available and efficient system. The latest twist in this evolution is adoption of natural language processing and speech recognition in the user interface between the human and the system and use of machine learning and advanced analytics, in general, for backend processing as well. The paper reviews the progress of technology adoption in the field and comments on the maturity level of solutions involving less studied or lowresource languages like Hindi and also other Indian, regional languages. Furthermore, it also provides an analysis from a prototype built by us. The future directions of this area are also highlighted.

Specialized Support Vector Machines for open-set recognition <=SEP=> Often, when dealing with real-world recognition problems, we do not need, and often cannot have, knowledge of the entire set of possible classes that might appear during operational testing. Sometimes, some of these classes may be ill-sampled, not sampled at all or undefined. In such cases, we need to think of robust classification methods able to deal with the “unknown” and properly reject samples belonging to classes never seen during training. Notwithstanding, almost all existing classifiers to date were mostly developed for the closed-set scenario, i.e., the classification setup in which it is assumed that all test samples belong to one of the classes with which the classifier was trained. In the open-set scenario, however, a test sample can belong to none of the known classes and the classifier must properly reject it by classifying it as unknown. In this work, we extend upon the wellknown Support Vector Machines (SVM) classifier and introduce the Specialized Support Vector Machines (SSVM), which is suitable for recognition in open-set setups. SSVM balances the empirical risk and the risk of the unknown and ensures that the region of the feature space in which a test sample would be classified as known (one of the known classes) is always bounded, ensuring a finite risk of the unknown. The same cannot be guaranteed by the traditional SVM formulation, even when using the Radial Basis Function (RBF) kernel. In this work, we also highlight the properties of the SVM classifier related to the open-set scenario, and provide necessary and sufficient conditions for an RBF SVM to have bounded open-space risk. We also indicate promising directions of investigation of SVM-based methods for open-set scenarios. An extensive set of experiments compares the proposed method with existing solutions in the literature for open-set recognition and the reported results show its effectiveness.

Polysemy in Sentence Comprehension: Effects of Meaning Dominance. <=SEP=> Words like church are polysemous, having two related senses (a building and an organization). Three experiments investigated how polysemous senses are represented and processed during sentence comprehension. On one view, readers retrieve an underspecified, core meaning, which is later specified more fully with contextual information. On another view, readers retrieve one or more specific senses. In a reading task, context that was neutral or biased towards a particular sense preceded a polysemous word. Disambiguating material consistent with only one sense followed, in a second sentence (Experiment 1) or the same sentence (Experiments 2 & 3). Reading the disambiguating material was faster when it was consistent with that context, and dominant senses were committed to more strongly than subordinate senses. Critically, following neutral context, the continuation was read more quickly when it selected the dominant sense, and the degree of sense dominance partially explained the reading time advantage. Similarity of the senses also affected reading times. Across experiments, we found that sense selection may not be completed immediately following a polysemous word but is completed at a sentence boundary. Overall, the results suggest that readers select an individual sense when reading a polysemous word, rather than a core meaning.

Ocular symptom detection using smartphones <=SEP=> It is often said that "the eyes are the windows to the soul". Although this usually metaphorically refers to a person's emotional state, there is literal truth in this statement with reference to a person's biological state. The eyes are dynamic and sensitive organs that are connected to the rest of the body via the circulatory and nervous systems. This means that diseases originating from an organ within the body can indirectly affect the eyes. Although the eyes may not be the origin for many such diseases, they are visible to external observers, making them an accessible point of diagnosis. For instance, broken blood vessels in the sclera could indicate a blood clotting disorder or a vitamin K deficiency. Instead of "windows to the soul", the eyes are "sensors to the body" that can be monitored to screen for certain diseases.

EMBROIDERED FULLY TEXTILE WEARABLE AN- TENNA FOR MEDICAL MONITORING APPLICATIONS <=SEP=> Telecommunication systems integrated within garments and wearable products are such methods by which medical devices are making an impact on enhancing healthcare provisions around the clock. These garments when fully developed will be capable of alerting and demanding attention if and when required along with minimizing hospital resources and labour. Furthermore, they can play a major role in preventative ailments, health irregularities and unforeseen heart or brain disorders in apparently healthy individuals. This work presents the feasibility of investigating an Ultra-WideBand (UWB) antenna made from fully textile materials that were used for the substrate as well as the conducting parts of the designed antenna. Simulated and measured results show that the proposed antenna design meets the requirements of wide working bandwidth and provides 17GHz bandwidth with compact size, washable and flexible materials. Results in terms of return loss, bandwidth, radiation pattern, current distribution as well as gain and efficiency are presented to validate the usefulness of the current manuscript design. The work presented here has profound implications for future studies of a standalone suite that may one day help to provide wearer (patient) with such reliable and comfortable medical monitoring techniques. Received 12 April 2011, Accepted 23 May 2011, Scheduled 10 June 2011 * Corresponding author: Mai A. Rahman Osman (mai.rahman@fkegraduate.utm.my).

Health literacy and public health: A systematic review and integration of definitions and models <=SEP=> BACKGROUND
Health literacy concerns the knowledge and competences of persons to meet the complex demands of health in modern society. Although its importance is increasingly recognised, there is no consensus about the definition of health literacy or about its conceptual dimensions, which limits the possibilities for measurement and comparison. The aim of the study is to review definitions and models on health literacy to develop an integrated definition and conceptual model capturing the most comprehensive evidence-based dimensions of health literacy.


METHODS
A systematic literature review was performed to identify definitions and conceptual frameworks of health literacy. A content analysis of the definitions and conceptual frameworks was carried out to identify the central dimensions of health literacy and develop an integrated model.


RESULTS
The review resulted in 17 definitions of health literacy and 12 conceptual models. Based on the content analysis, an integrative conceptual model was developed containing 12 dimensions referring to the knowledge, motivation and competencies of accessing, understanding, appraising and applying health-related information within the healthcare, disease prevention and health promotion setting, respectively.


CONCLUSIONS
Based upon this review, a model is proposed integrating medical and public health views of health literacy. The model can serve as a basis for developing health literacy enhancing interventions and provide a conceptual basis for the development and validation of measurement tools, capturing the different dimensions of health literacy within the healthcare, disease prevention and health promotion settings.

Tomographic reconstruction of piecewise smooth images <=SEP=> In computed tomography, direct inversion of the Radon transform requires more projections than are practical due to constraints in scan time and image accessibility. Therefore, it is necessary to consider the estimation of reconstructed images when the problem is under-constrained, i.e., when a unique solution does not exist. To resolve ambiguities among solutions, it is necessary to place additional constraints on the reconstructed image. In this paper, we present a surface evolution technique to model the reconstructed image as piecewise smooth. We model the reconstructed image as two regions that are each smoothly varying in intensity and are separated by a smooth surface. We define a cost functional to penalize deviation from piecewise smoothness while ensuring that the projections of the estimated image match the measured projections. From this functional, we derive an evolution for the modeled image intensity and an evolution for the surface, thereby defining a variational tomographic estimation technique. We show example reconstructions to highlight the performance of the proposed method on real medical images.

Clustering and Summarizing Protein-Protein Interaction Networks: A Survey <=SEP=> The increasing availability and significance of large-scale protein-protein interaction (PPI) data has resulted in a flurry of research activity to comprehend the organization, processes, and functioning of cells by analyzing these data at network level. Network clustering, that analyzes the topological and functional properties of a PPI network to identify clusters of interacting proteins, has gained significant popularity in the bioinformatics as well as data mining research communities. Many studies since the last decade have shown that clustering PPI networks is an effective approach for identifying functional modules, revealing functions of unknown proteins, etc. In this paper, we examine this issue by classifying, discussing, and comparing a wide ranging approaches proposed by the bioinformatics community to cluster PPI networks. A pervasive desire of this review is to emphasize the uniqueness of the network clustering problem in the context of PPI networks and highlight why generic network clustering algorithms proposed by the data mining community cannot be directly adopted to address this problem effectively. We also review a closely related problem to PPI network clustering, network summarization, which can enable us to make sense out of the information contained in large PPI networks by generating multi-level functional summaries.

Find me if you can: improving geographical prediction with social and spatial proximity <=SEP=> Geography and social relationships are inextricably intertwined; the people we interact with on a daily basis almost always live near us. As people spend more time online, data regarding these two dimensions -- geography and social relationships -- are becoming increasingly precise, allowing us to build reliable models to describe their interaction. These models have important implications in the design of location-based services, security intrusion detection, and social media supporting local communities.
 Using user-supplied address data and the network of associations between members of the Facebook social network, we can directly observe and measure the relationship between geography and friendship. Using these measurements, we introduce an algorithm that predicts the location of an individual from a sparse set of located users with performance that exceeds IP-based geolocation. This algorithm is efficient and scalable, and could be run on a network containing hundreds of millions of users.

Syntax-Tree Regular Expression Based DFA FormalConstruction <=SEP=> Compiler is a program whose functionality is to translate a computer program written in source language into an equivalent machine code. Compiler construction is an advanced research area because of its size and complexity. The source codes are in higher level languages which are usually complex and, consequently, increase the level of abstraction. Due to such reasons, design and construction of error free compiler is a challenge of the twenty first century. Verification of a source program does not guarantee about correctness of code generated because the bugs in compiler may lead to an incorrect target program. Therefore, verification of compiler is more important than verifying the source programs. Lexical analyzer is a main phase of compiler used for scanning input and grouping into sequence of tokens. In this paper, formal construction of deterministic finite automata (DFA) based on regular expression is presented as a part of lexical analyzer. At first, syntax tree is described based on the augmented regular expression. Then formal description of important operators, checking null-ability and computing first and last positions of internal nodes of the tree is described. In next, the transition diagram is described from the follow positions and converted into deterministic finite automata by defining a relationship among syntax tree, transition diagram and DFA. Formal specification of the procedure is described using Z notation and model analysis is provided using Z/Eves toolset.

Supervised Dictionary Learning by a Variational Bayesian Group Sparse Nonnegative Matrix Factorization <=SEP=> Nonnegative matrix factorization (NMF) with group sparsity constraints is formulated as a probabilistic graphical model and, assuming some observed data have been generated by the model, a feasible variational Bayesian algorithm is derived for learning model parameters. When used in a supervised learning scenario, NMF is most often utilized as an unsupervised feature extractor followed by classification in the obtained feature subspace. Having mapped the class labels to a more general concept of groups which underlie sparsity of the coefficients, what the proposed group sparse NMF model allows is incorporating class label information to find low dimensional label-driven dictionaries which not only aim to represent the data faithfully, but are also suitable for class discrimination. Experiments performed in face recognition and facial expression recognition domains point to advantages of classification in such label-driven feature subspaces over classification in feature subspaces obtained in an unsupervised manner.

Acute Sexual Assault in the Pediatric and Adolescent Population. <=SEP=> Children and adolescents are at high risk for sexual assault. Early medical and mental health evaluation by professionals with advanced training in sexual victimization is imperative to assure appropriate assessment, forensic evidence collection, and follow-up. Moreover, continued research and outreach programs are needed for the development of preventative strategies that focus on this vulnerable population. In this review we highlight key concepts for assessment and include a discussion of risk factors, disclosure, sequelae, follow-up, and prevention.

Listening Comprehension over Argumentative Content <=SEP=> This paper presents a task for machine listening comprehension in the argumentation domain and a corresponding dataset in English. We recorded 200 spontaneous speeches arguing for or against 50 controversial topics. For each speech, we formulated a question, aimed at confirming or rejecting the occurrence of potential arguments in the speech. Labels were collected by listening to the speech and marking which arguments were mentioned by the speaker. We applied baseline methods addressing the task, to be used as a benchmark for future work over this dataset. All data used in this work is freely available for research.

MauveDB: supporting model-based user views in database systems <=SEP=> Real-world data --- especially when generated by distributed measurement infrastructures such as sensor networks --- tends to be incomplete, imprecise, and erroneous, making it impossible to present it to users or feed it directly into applications. The traditional approach to dealing with this problem is to first process the data using statistical or probabilistic models that can provide more robust interpretations of the data. Current database systems, however, do not provide adequate support for applying models to such data, especially when those models need to be frequently updated as new data arrives in the system. Hence, most scientists and engineers who depend on models for managing their data do not use database systems for archival or querying at all; at best, databases serve as a persistent raw data store.In this paper we define a new abstraction called model-based views and present the architecture of MauveDB, the system we are building to support such views. Just as traditional database views provide logical data independence, model-based views provide independence from the details of the underlying data generating mechanism and hide the irregularities of the data by using models to present a consistent view to the users. MauveDB supports a declarative language for defining model-based views, allows declarative querying over such views using SQL, and supports several different materialization strategies and techniques to efficiently maintain them in the face of frequent updates. We have implemented a prototype system that currently supports views based on regression and interpolation, using the Apache Derby open source DBMS, and we present results that show the utility and performance benefits that can be obtained by supporting several different types of model-based views in a database system.

Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora <=SEP=> We introduce (1) a novel stochastic inversion transduction grammar formalism for bilingual language modeling of sentence-pairs, and (2) the concept of bilingual parsing with a variety of parallel corpus analysis applications. Aside from the bilingual orientation, three major features distinguish the formalism from the finite-state transducers more traditionally found in computational linguistics: it skips directly to a context-free rather than finite-state base, it permits a minimal extra degree of ordering flexibility, and its probabilistic formulation admits an efficient maximum-likelihood bilingual parsing algorithm. A convenient normal form is shown to exist. Analysis of the formalism's expressiveness suggests that it is particularly well suited to modeling ordering shifts between languages, balancing needed flexibility against complexity constraints. We discuss a number of examples of how stochastic inversion transduction grammars bring bilingual constraints to bear upon problematic corpus analysis tasks such as segmentation, bracketing, phrasal alignment, and parsing.

Evaluation Techniques and Systems for Answer Set Programming: a Survey <=SEP=> Answer set programming (ASP) is a prominent knowledge representation and reasoning paradigm that found both industrial and scientific applications. The success of ASP is due to the combination of two factors: a rich modeling language and the availability of efficient ASP implementations. In this paper we trace the history of ASP systems, describing the key evaluation techniques and their implementation in actual tools.