Towards the enhancement of e-democracy: identifying the notion of the 'middleman paradox'

Abstract. The challenge towards e-democracy, through the electronic transformation of political systems, has become increasingly evident within developed economies. It is regarded as an approach for increased and better quality citizen participation in the democratic processes. E-democracy forms a component of overall e-government initiatives where technology adoption and diffusion, to enhance wider access to, and the delivery of, government services, are apparent. However, previous research demonstrates that very few e-democracy proposals survive the stage of formal political decision-making to become substantive e-government projects within national or international agendas. Furthermore, the implementation of e-democracy projects is undertaken at a much slower pace and with dramatically less support than the implementation of other, so-called e-administration, activities in the public sector.
The research in this paper considers the notion of the 'middleman paradox', presenting theoretical and empirical evidence that further investigates the phenomenon associated with potential e-democracy improvements. Specifically, the paper adds a new dimension to existing theories on the hesitant evolution of e-democracy that clearly identifies politicians as an inhibiting factor. Proposals are made for an enhancement of these processes, and suggestions for further applicable research are demonstrated.     

Natural language processing for transparent communication between public administration and citizens

This paper presents two projects concerned with the application of natural language processing technology for improving communication between Public Administration and citizens. The first project, GIST,is concerned with automatic multilingual generation of instructional texts for form-filling. The second project, TAMIC, aims at providing an interface for interactive access to information, centered on natural language processing and supposed to be used by the clerk but with the active participation of the citizen.This revised version was published online in October 2005 with corrections to the Cover Date.     

基于Bi-LSTM的医疗事件识别研究

文本的深度语义分析近年来已经成为自然语言处理研究领域的一个热点,文本的信息抽取及属性识别是文本语义分析的一项重要任务。随着机器学习技术近年来在自然语言处理领域取得了成功,部分学者将该技术推广到了医疗领域的信息抽取任务上面,并且在标准测试集上得到了比传统统计学方法更好的结果,然而这些模型方法仍然存在信息获取不充分等问题。因而,本文在已有工作基础上提出了双向LSTM与MLP集成的深度神经网络模型。在2016年SemEval的医疗事件抽取以及事件属性预测任务中,该模型将医疗文本的词性以及命名实体的描述信息当作附加属性,使用双向LSTM神经网络学习文本的隐藏特征,解决了传统方法通用性不强以及无法捕捉前后文隐含信息的缺点,然后,再使用全连接的方式去判断候选词汇是否属于医疗事件以及识别其相关属性。实验结果表明,本文提出的神经网络模型对医疗文本的抽取效果优于以往学者的方法。     

Advanced human–machine system for intelligent manufacturing

The use of ontologies has gained more and more interest above all for the knowledge management, e.g. the exchange of professional “know-how”, as reported in various previous papers. Under the pressure of a turbulent international market situation enterprises stress the importance of innovation in manufacturing areas. For instance, due to the drastic growing automation degree of manufacturing systems an intuitive interaction form is required, which enables the shop-floor personnel an active participation to the production without specific technical background, as well as to capture and retrieve systematically knowledge contents arising from the interaction process. The following contribution takes this topic into consideration and proposes an innovative ontology- based approach called ontological filtering system (OFS) based on methods and procedures to formalize natural language contents in a systematic way. By means of a so-called ontological network (ON) generic term forms used in the human–machine interaction (HMI) via natural language could be led back to a set of pre-defined terms. Thus, the ON consists, on the one hand, of a large number of generic natural language terms and, on the other hand, of a set of so-called key terms. The generic terms are defined, classified in semantic categories and chained together per semantic relations for a specific use in a particular domain of discourse. The key terms are used to build information on machine level and, therefore, have a formal definition. Through additional syntax roles and application-specific semantic constrains a systematic access and processing of natural language instructions is accomplished computationally. The proposed concepts have been set up and tested within an experimental testbed. The obtained results show a high system performance and encourage the research team to invest further efforts, in order to validate the system operational performances towards its industrial use at shop-floor level. Received: February 2005 / Accepted: January 2006     

Information Needs and Communication Gaps between Citizens and Local Governments Online during Natural Disasters

A growing number of citizens and local governments are using tweets to communicate during natural disasters. A good understanding of the communication contents and behaviors is critical for disaster relief. Previous work has used crisis taxonomies or manually labeling methods to understand the content. However, such methods usually require extra efforts to find insights related to specific events. In this paper, we use a semi-automatic framework to extract specific topics from the communication contents of citizens and local governments, combined with the spatiotemporal information to explore: 1) the spatiotemporal bursts of topics; 2) the change of topics with respect to the severity of disaster; and 3) communication behaviors. We use tweets collected during 18 snowstorms in the State of Maryland, US, as a case to study. The study reveals the communication differences due to the urban-rural divide or to the severity of the snowstorms. The insights suggest that local governments could potentially adapt the context of information delivered to citizens so as to match their needs.     

Enhancing information systems management with natural language processing techniques

Natural language and databases are core components of information systems. They are related to each other because they share the same purpose: the conceptualization aspects of the real world in order to deal with them in some way. Natural language processing (NLP) techniques may substantially enhance most phases of the information system lifecycle, starting with requirements analysis, specification and validation, and going up to conflict resolution, result processing and presentation. Furthermore, natural language based query languages and user interfaces facilitate the access to information for anyone and allow for new paradigms in the usage of computerized services. This paper investigates the use of NLP techniques in the design phase of information systems. Then, it reports on data base querying and information retrieval enhanced with NLP.     

E-government and e-democracy in Latin America

Implementing governmental and democratic processes using electronic systems is the subject of much debate around the world. Electronic voting has grabbed the headlines, but in reality, this constitutes just a small part of the effort to establish electronic communication between citizens and governmental functions (G2C and C2G). The goal is to provide access to information and to open up decision-making processes to citizens (e-participation) to encourage a grass-roots engagement of citizens with democratic processes.     

COMODA: a conversation model for database access

As electronic database technology becomes less expensive, people will want to access information without undergoing special training. These people could use their native language if databases could be accessed through natural language conversations. The approach of the current research is that in order for the computer to be controlled by natural language, the computer does not have to understand it, only respond correctly. The conversation model for database access (COMODA) describes information retrieval as a dialogue. The dialogue is modelled by a series of states, where each state has an utterance that provides some information. The states are linked by transitions that are followed if a parse template matches the input sentence. Provisions are made for backtracking to earlier states, and for changes in topic. A small database of general information about one division of the Federal Government was implemented on an IBM-PC using these principles. When ten untrained people were allowed to converse with this database, 59% of their queries were answered correctly. All but one person said that they would use this type of database if more information was available. It was concluded that it is feasible to create a database of general information which can be accessed with natural language conversations by untrained users.     

The Role of a Natural Language Conversational Interface in Online Sales: A Case Study

This paper describes the evaluation of a natural language dialog-based navigation system (HappyAssistant) that helps users access e-commerce sites to find relevant information about products and services. The prototype system leverages technologies in natural language processing and human-computer interaction to create a faster and more intuitive way of interacting with websites, especially for less experienced users. The result of a comparative study shows that users prefer the natural language-enabled navigation two to one over the menu driven navigation. In addition, the study confirmed the efficiency of using natural language dialog in terms of the number of clicks and the amount of time required to obtain the relevant information. In the case study, as compared to the menu driven system, the average number of clicks used in the natural language system was reduced by 63.2% and the average time was reduced by 33.3%.     

Embedded access points for trusted data and resources access in HPC systems

Biometric authentication systems represent a valid alternative to the conventional username–password based approach for user authentication. However, authentication systems composed of a biometric reader, a smartcard reader, and a networked workstation which perform user authentication via software algorithms have been found to be vulnerable in two areas: firstly in their communication channels between readers and workstation (communication attacks) and secondly through their processing algorithms and/or matching results overriding (replay attacks, confidentiality and integrity threats related to the stored information of the networked workstation). In this paper, a full hardware access point for HPC environments is proposed. The access point is composed of a fingerprint scanner, a smartcard reader, and a hardware core for fingerprint processing and matching. The hardware processing core can be described as a Handel-C algorithmic-like hardware programming language and prototyped via a Field Programmable Gate Array (FPGA) based board. The known indexes False Acceptance Rate (FAR) and False Rejection Rate (FRR) have been used to test the prototype authentication accuracy. Experimental trials conducted on several fingerprint DBs show that the hardware prototype achieves a working point with FAR=1.07% and FRR=8.33% on a proprietary DB which was acquired via a capacitive scanner, a working point with FAR=0.66% and FRR=6.13% on a proprietary DB which was acquired via an optical scanner, and a working point with FAR=1.52% and FRR=9.64% on the official FVC2002_DB2B database. In the best case scenario (depending on fingerprint image size), the execution time of the proposed recognizer is 183.32 ms.     

基于向量空间模型的有导词义消歧

词义消歧一直是自然语言理解中的一个关键问题,该问题解决的好坏直接关系到自然语言处理中诸多应用问题的效果优劣。由于自然语言知识表示的困难,在手工规则的词义消歧难以达到理想效果的情况下,各种有导机器学习方法被应用于词义消歧任务中,借鉴前人的成果引入信息检索领域中空间模型文档词语权重计算技术来解决多义词义项的知识表示问题,并提出了上下文位置权重的计算方法,给出了一种基于向量空间模型的词义消岐有导机器学习方法。该方法将多义词的义项和上下文分别映射到向量空间中,通过计算多义词上下文向量与义项向量的距离,采用k-NN(k=1)方法来确定上下文向量的义项分类。在9个汉语高频多义词的开放和封闭测试中均取得了突出的成绩（封闭测试平均正确率为96．31％,开放测试平均正确率为92．98％）,验证了该方法的有效性。     

A real-time interactive nonverbal communication system through semantic feature extraction as an interlingua

There has been a growing interest in the use of networked virtual environment (NVE) technology to implement telepresence that allows participants to interact with each other in shared cyberspace. In addition, nonverbal language has attracted increased attention because of its association with more natural human communication, and especially sign languages play an important role for the hearing impaired. This paper proposes a novel real-time nonverbal communication system by introducing an artificial intelligence method into the NVE. We extract semantic information as an interlingua from the input text through natural language processing, and then transmit this semantic feature extraction (SFE) to the three-dimensional (3-D) articulated humanoid models prepared for each client in remote locations. Once the SFE is received, the virtual human is animated by the synthesized SFE. Experiments with Japanese and Chinese sign languages show this system makes the real-time animation of avatars available for the participants when chatting with each other. The communication is more natural since it is not just based on text or predefined gesture icons. This proposed system is suitable for sign language distance training as well.     

受控自然语言的应用和处理

自然语言是人类交流最自然的方式。但其复杂性和模糊性常常给有效的交流带来问题。现代社会尤其是当前信息时代面对大量的信息数据,不少工业场景和科研领域以及各种人机交互的应用要求清晰精准、标准化而又较为自然的表达和交流,受控自然语言随着这些需求应运而生。该文讨论受控自然语言及其性质、分类和应用,以及受控自然语言的计算处理方法。该文将以航空工业民用飞机所涉及的英语文本数据为例来阐述受控自然语言在工业场景中的作用和重要性,并且简要讨论受控自然语言更为广泛的意义和价值,涉及其他领域包括当前热门的人工智能等相关的课题。     

通用试飞数据处理查询语言FTSQL

给出了一种通用数据处理用工程数据库查询语言FTSQL。该语言可入高级程序设计语言,提供了工程数据的标准访问接口,采用该语言可使数据处理部件化,支持数据处理软件系统的集成化、规范化以及系统的可重要性要求。     

Reducing Affective Responses to Surgical Images and Videos Through Stylization

We present the first empirical study on using colour manipulation and stylization to make surgery images/videos more palatable. While aversion to such material is natural, it limits many people's ability to satisfy their curiosity, educate themselves and make informed decisions. We selected a diverse set of image processing techniques to test them both on surgeons and lay people. While colour manipulation techniques and many artistic methods were found unusable by surgeons, edge-preserving image smoothing yielded good results both for preserving information (as judged by surgeons) and reducing repulsiveness (as judged by lay people). We then conducted a second set of interview with surgeons to assess whether these methods could also be used on videos and derive good default parameters for information preservation. We provide extensive supplemental material at osf.io/4pfes/ .     

Content-Dependent Question Generation Using LOD for History Learning in Open Learning Space

The objective of this research is to use current linked open data (LOD) to generate questions automatically to support history learning. This paper tries to clarify the potential of LOD as a learning resource. By linking LOD to natural language documents, we created an open learning space where learners have access to machine understandable natural language information about many topics. The learning environment supports learners with content-dependent questions. In this paper, we describe the question generation method that creates natural language questions using LOD. The integrated data is combined to a history domain ontology and a history dependent question ontology to generate content-dependent questions. To prove whether the generated questions have a potential to support learning, a human expert conducted an evaluation comparing our automatically generated questions with questions generated manually. The results of the evaluation showed that the generated questions could cover more than 80% of the questions supporting knowledge acquisition generated by humans. In addition, we confirmed the automatically generated questions have a potential to reinforce learners’ deep historical understanding.     

TRUMP: A transportable language understanding program

Transportability has perpetually been the nemesis of natural language processing systems, in both the research and commercial sectors. During the last 20 years, the technology has not moved much closer to providing robust coverage of everyday language, and has failed to produce commercial successes beyond a few specialized interfaces and application programs. the redesign required for each application has limited the impact of natural language systems. Trump (TRansportable Understanding Mechanism Package) is a natural language analyzer that functions in a variety of domains, in both interfaces and text processing. While other similar efforts have treated transportability as a problem in knowledge engineering, Trump instead relies mainly on a “core” of knowledge about language and a set of techniques for applying that knowledge within a domain. the information about words, word meanings, and linguistic relations in this generic knowledge base guides the conceptual framework of language interpretation in each domain. Turmp uses this core knowledge to piece together a conceptual representation of a natural language input by combining generic and specialized inforamtion. the result has been a language processing system that is capable of performing fairly extensive analysis with a minimum of customization for each application.     

Ask Your Neurons: A Deep Learning Approach to Visual Question Answering

We propose a Deep Learning approach to the visual question answering task, where machines answer to questions about real-world images. By combining latest advances in image representation and natural language processing, we propose Ask Your Neurons, a scalable, jointly trained, end-to-end formulation to this problem. In contrast to previous efforts, we are facing a multi-modal problem where the language output (answer) is conditioned on visual and natural language inputs (image and question). We evaluate our approaches on the DAQUAR as well as the VQA dataset where we also report various baselines, including an analysis how much information is contained in the language part only. To study human consensus, we propose two novel metrics and collect additional answers which extend the original DAQUAR dataset to DAQUAR-Consensus. Finally, we evaluate a rich set of design choices how to encode, combine and decode information in our proposed Deep Learning formulation.