Information, Volume 10, Issue 10 (October 2019) – 39 articles

The security of mobile applications has become a major research field which is associated with a lot of challenges. The high rate of developing mobile applications has resulted in less secure applications. This is due to what is called the “rush to release” as defined by Ponemon Institute. Security testing—which is considered one of the main phases of the development life cycle—is either not performed or given minimal time; hence, there is a need for security testing automation. One of the techniques used is Automated Vulnerability Detection. Vulnerability detection is one of the security tests that aims at pinpointing potential security leaks. Fixing those leaks results in protecting smart-phones and tablet mobile device users against attacks. This paper focuses on building a hybrid approach of static and dynamic analysis for detecting the vulnerabilities of Android applications. This approach is capsuled in a usable platform (web application) to make it easy to use for both public users and professional developers. Static analysis, on one hand, performs code analysis. It does not require running the application to detect vulnerabilities. Dynamic analysis, on the other hand, detects the vulnerabilities that are dependent on the run-time behaviour of the application and cannot be detected using static analysis. The model is evaluated against different applications with different security vulnerabilities. Compared with other detection platforms, our model detects information leaks as well as insecure network requests alongside other commonly detected flaws that harm users’ privacy. The code is available through a GitHub repository for public contribution. Full article

Flexible paper-based physiological sensor electrodes were developed using chemically-modified graphene (CG) and carboxylic-functionalized multiwalled carbon nanotube composites (f@MWCNTs). A solvothermal process with additional treatment was conducted to synthesize CG and f@MWCNTs to make CG-f@MWCNT composites. The composite was sonicated in an appropriate solvent to make a uniform suspension, and then it was drop cast on a nylon membrane in a vacuum filter. A number of batches (0%~35% f@MWCNTs) were prepared to investigate the performance of the physical characteristics. The 25% f@MWCNT-loaded composite showed the best adhesion on the paper substrate. The surface topography and chemical bonding of the proposed CG-f@MWCNT electrodes were characterized by scanning electron microscopy (SEM) and Raman spectroscopy, respectively. The average sheet resistance of the 25% CG-f@MWCNT electrode was determined to be 75 Ω/□, and it showed a skin contact impedance of 45.12 kΩ at 100 Hz. Electrocardiogram (ECG) signals were recorded from the chest and fingertips of healthy adults using the proposed electrodes. The CG-f@MWCNT electrodes demonstrated comfortability and a high sensitivity for electrocardiogram signal detection. Full article

The people in Bangladesh and two states (i.e., Tripura and West Bengal) in India, which is about 230 million of the world population, use Bangla as their first dialect. However, very few numbers of resources and tools are available for this language. This paper presents a Bangla DeConverter to extract Bangla texts from Universal Networking Language (UNL). It explains and illustrates the different phases of the proposed Bangla DeConverter. The syntactic linearization, the implementation of the results of the proposed Bangla DeConverter, and the extraction of a Bangla sentence from UNL expressions are presented in this paper. The Bangla DeConverter has been tested on UNL expressions of 300 Bangla sentences using a Russian and English Language Server. The proposed system generates 90% syntactically and semantically correct Bangla sentences with a UNL Bilingual Evaluation Understudy (BLEU) score of 0.76. Full article

With the high integration of smart grid information and physical systems, the security of information systems must affect the safe and stable operation of physical systems. Risk assessment is an effectual means to objectively evaluate the information security threats of the smart grid. However, the existing risk assessment methods are aim at solving the threat of security risks in communication networks and information systems in the smart grid, but there is no in-depth study on how the spread of information security risks between information systems and physical systems in the smart grid. Therefore, based on the traditional infectious disease transmission theory, the information security risk propagation model based on the Susceptible–Exposed–Infected–Recovered (SEIR) infectious disease model for smart grid (ISRP-SEIRIDM) is proposed in this paper. In ISRP-SEIRIDM, we analyze the information interaction between information collection devices and define the connection of nature and the security risks between the information collection devices in the smart grid. At the same time, we also study the impact of the number of information acquisition devices and information interaction capabilities of these devices on the speed of security risk transmission between information systems and physics systems in the smart grid and the maximum risk range. Experimental results show that the risk propagation range can be significantly reduced by optimizing the data interaction capability and information transmission path between information collection devices in the smart grid; when a probability from a susceptible state to an exposed state reduces by 0.15, the maximum spread and average spread of security risk will be reduced by 7% and 1.96%, respectively. Full article

We implement hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) on Bitcoin/US dollar (BTC/USD) with the aim of market phase detection. We make analogous comparisons to Standard and Poor’s 500 (S and P 500), a benchmark traditional stock index and a protagonist of several studies in finance. Popular labels given to market phases are “bull”, “bear”, “correction”, and “rally”. In the first part, we fit HMMs and HSMMs and look at the evolution of hidden state parameters and state persistence parameters over time to ensure that states are correctly classified in terms of market phase labels. We conclude that our modelling approaches yield positive results in both BTC/USD and the S and P 500, and both are best modelled via four-state HSMMs. However, the two assets show different regime volatility and persistence patterns—BTC/USD has volatile bull and bear states and generally weak state persistence, while the S and P 500 shows lower volatility on the bull states and stronger state persistence. In the second part, we put our models to the test of detecting different market phases by devising investment strategies that aim to be more profitable on unseen data in comparison to a buy-and-hold approach. In both cases, for select investment strategies, four-state HSMMs are also the most profitable and significantly outperform the buy-and-hold strategy. Full article

This paper describes a preliminary experiment concerning the use of advanced Artificial Intelligence/Knowledge Representation techniques to improve the present formalization/digitization procedures of Cultural Heritage assets—with reference, in particular, to all types of Cultural Heritage “iconographic” entities. In this context, in agreement with the recent proposal to characterize the digital description of Cultural Heritage items making use of the notion of “Cultural Heritage Digital Twin”, we are mainly concerned with the possibility to consider not only the external, “physical”, aspects of these iconographic items but also the “message” they convey in a more or less explicit way. For our experiment, some aspects of the Mona Lisa painting by Leonardo da Vinci have been formalized, along with their context, making use of NKRL, the “Narrative Knowledge Representation Language”. NKRL is, in reality, both a Knowledge Representation language and a full Computer Science environment, used to represent/manage in an advanced way “narrative” (in the widest meaning of this word) information. The initial results of the experiment are described in the paper, along with some thoughts about their possible interest and developments. Full article

Persuasive interventions can lose their effectiveness when a person becomes reactant to the persuasive messages—a state identified by feelings of anger and perceived threat to freedom. A person will strive to reestablish their threatened freedom, which is characterized by motivational arousal. Research suggests that the motivational state of psychological reactance can be observed in physiology. Therefore, the assessment of physiological reactions might help to identify reactance to persuasive messages and, thereby, could be an objective approach to personalize persuasive technologies. The current study investigates peripheral psychophysiological reactivity in response to persuasive messages. To manipulate the strength of the reactant response either high- or low-controlling language messages were presented to discourage meat consumption. The high-controlling language condition indeed evoked more psychological reactance, and sympathetic arousal did increase during persuasive messaging in heart rate and heart rate variability, although no clear relationship between physiological reactivity and self-reported psychological reactance was found. However, the evaluation of multiple linear models revealed that variance in self-reported psychological reactance was best explained by initial intentions in combination with cardiovascular reactivity. To conclude, considering physiological reactivity in addition to motivational state can benefit our understanding of psychological reactance. Full article

In the education process, students face problems with understanding due to the complexity, necessity of abstract thinking and concepts. More and more educational centres around the world have started to introduce powerful new technology-based tools that help meet the needs of the diverse student population. Over the last several years, virtual reality (VR) has moved from being the purview of gaming to professional development. It plays an important role in teaching process, providing an interesting and engaging way of acquiring information. What follows is an overview of the big trend, opportunities and concerns associated with VR in education. We present new opportunities in VR and put together the most interesting, recent virtual reality applications used in education in relation to several education areas such as general, engineering and health-related education. Additionally, this survey contributes by presenting methods for creating scenarios and different approaches for testing and validation. Lastly, we conclude and discuss future directions of VR and its potential to improve the learning experience. Full article

Word segmentation is an essential task in automatic language processing for languages where there are no explicit word boundary markers, or where space-delimited orthographic words are too coarse-grained. In this paper we introduce the MiNgMatch Segmenter—a fast word segmentation algorithm, which reduces the problem of identifying word boundaries to finding the shortest sequence of lexical n-grams matching the input text. In order to validate our method in a low-resource scenario involving extremely sparse data, we tested it with a small corpus of text in the critically endangered language of the Ainu people living in northern parts of Japan. Furthermore, we performed a series of experiments comparing our algorithm with systems utilizing state-of-the-art lexical n-gram-based language modelling techniques (namely, Stupid Backoff model and a model with modified Kneser-Ney smoothing), as well as a neural model performing word segmentation as character sequence labelling. The experimental results we obtained demonstrate the high performance of our algorithm, comparable with the other best-performing models. Given its low computational cost and competitive results, we believe that the proposed approach could be extended to other languages, and possibly also to other Natural Language Processing tasks, such as speech recognition. Full article

The big data from various sensors installed on-board for monitoring the status of ship devices is very critical for improving the efficiency and safety of ship operations and reducing the cost of operation and maintenance. However, how to utilize these data is a key issue. The temperature change of the ship propulsion devices can often reflect whether the devices are faulty or not. Therefore, this paper aims to forecast the temperature of the ship propulsion devices by data-driven methods, where potential faults can be further identified automatically. The proposed forecasting process is composed of preprocessing, feature selection, and prediction, including an autoregressive distributed lag time series model (ARDL), stepwise regression (SR) model, neural network (NN) model, and deep neural network (DNN) model. Finally, the proposed forecasting process is applied on a naval ship, and the results show that the ARDL model has higher accuracy than the three other models. Full article

Due to large-scale control problems in 5G access networks, the complexity of radio resource management is expected to increase significantly. Reinforcement learning is seen as a promising solution that can enable intelligent decision-making and reduce the complexity of different optimization problems for radio resource management. The packet scheduler is an important entity of radio resource management that allocates users’ data packets in the frequency domain according to the implemented scheduling rule. In this context, by making use of reinforcement learning, we could actually determine, in each state, the most suitable scheduling rule to be employed that could improve the quality of service provisioning. In this paper, we propose a reinforcement learning-based framework to solve scheduling problems with the main focus on meeting the user fairness requirements. This framework makes use of feed forward neural networks to map momentary states to proper parameterization decisions for the proportional fair scheduler. The simulation results show that our reinforcement learning framework outperforms the conventional adaptive schedulers oriented on fairness objective. Discussions are also raised to determine the best reinforcement learning algorithm to be implemented in the proposed framework based on various scheduler settings. Full article

The current inclusion of agile methodologies in web-oriented projects has been considered on a large-scale by software developers. However, the benefits and limitations go beyond the comforts that project managers delimit when choosing them. Selecting a methodology involves more than only the associated processes or some documentation. Based on the above, we could define as the main concerns the approach with which we identify the methodology, the needs of the company, the size, and qualities of the project, and especially the characteristics of agile development that they possess. However, there are several difficulties in selecting the most appropriate methodology due to the features in common; Will it be suitable for my project? What challenges will be presented in the process? Will my team understand each stage? Will I be able to deliver software that satisfies the client? Project managers create these questions, which seem manageable but have huge effects. This paper presents a systematic literature review based on the analysis of the approaches of six web development methodologies. The aim of the study is to analyze the approaches presented by relevant methodologies, identifying their common agile characteristics and managing to contrast both its benefits and limitations during a project. As a result, we could itemize five common features, which are presented within the processes; (1) flexibility, (2) constant communication of the workgroup, (3) use of UML, (4) the inclusion of the end-user and (5) some documentation. Full article

Failure Mode and Effect Analysis (FMEA) is a useful risk assessment tool used to identify, evaluate, and eliminate potential failure modes in numerous fields to improve security and reliability. Risk evaluation is a crucial step in FMEA and the Risk Priority Number (RPN) is a classical method for risk evaluation. However, the traditional RPN method has deficiencies in evaluation information, risk factor weights, robustness of results, etc. To overcome these shortcomings, this paper aims to develop a new risk evaluation in FMEA method. First, this paper converts linguistic evaluation information into corresponding interval-valued intuitionistic fuzzy numbers (IVIFNs) to effectively address the uncertainty and vagueness of the information. Next, different priorities are assigned to experts using the interval-valued intuitionistic fuzzy priority weight average (IVIFPWA) operator to solve the problem of expert weight. Then, the weights of risk factors are subjectively and objectively determined using the expert evaluation method and the deviation maximization model method. Finally, the paper innovatively introduces the interval-valued intuitionistic fuzzy weighted averaging (IVIFWA) operator, Tchebycheff Metric distance, and the interval-valued intuitionistic fuzzy weighted geometric (IVIFWG) operator into the ratio system, the reference point method, and the full multiplication form of MULTIMOORA sub-methods to optimize the information aggregation process of FMEA. The extended IVIF-MULTIMOORA method is proposed to obtain the risk ranking order of failure modes, which will help in obtaining more reasonable and practical results and in improving the robustness of results. The case of the Middle Route of the South-to-North Water Diversion Project’s operation risk is used to demonstrate the application and effectiveness of the proposed FMEA framework. Full article

In recent years, there has been a significant increase in the use of mobile devices and their applications. Meanwhile, cloud computing has been considered as the latest generation of computing infrastructure. There has also been a transformation in cloud computing ideas and their implementation so as to meet the demand for the latest applications. mobile edge computing (MEC) is a computing paradigm that provides cloud services near to the users at the edge of the network. Given the movement of mobile nodes between different MEC servers, the main aim would be the connection to the best server and at the right time in terms of the load of the server in order to optimize the quality of service (QoS) of the mobile nodes. We tackle the offloading decision making problem by adopting the principles of optimal stopping theory (OST) to minimize the execution delay in a sequential decision manner. A performance evaluation is provided using real world data sets with baseline deterministic and stochastic offloading models. The results show that our approach significantly minimizes the execution delay for task execution and the results are closer to the optimal solution than other offloading methods. Full article

With the rapid development of Internet technology, the social network has gradually become an indispensable platform for users to release information, obtain information, and share information. Users are not only receivers of information, but also publishers and disseminators of information. How to select a certain number of users to use their influence to achieve the maximum dissemination of information has become a hot topic at home and abroad. Rapid and accurate identification of influential nodes in the network is of great practical significance, such as the rapid dissemination, suppression of social network information, and the smooth operation of the network. Therefore, from the perspective of improving computational accuracy and efficiency, we propose an influential node identification method based on effective distance, named KDEC. By quantifying the effective distance between nodes and combining the position of the node in the network and its local structure, the influence of the node in the network is obtained, which is used as an indicator to evaluate the influence of the node. Through experimental analysis of a lot of real-world networks, the results show that the method can quickly and accurately identify the influential nodes in the network, and is better than some classical algorithms and some recently proposed algorithms. Full article

After more than a decade, the supply-driven approach to publishing public (open) data has resulted in an ever-growing number of data silos. Hundreds of thousands of datasets have been catalogued and can be accessed at data portals at different administrative levels. However, usually, users do not think in terms of datasets when they search for information. Instead, they are interested in information that is most likely scattered across several datasets. In the world of proprietary in-company data, organizations invest heavily in connecting data in knowledge graphs and/or store data in data lakes with the intention of having an integrated view of the data for analysis. With the rise of machine learning, it is a common belief that governments can improve their services, for example, by allowing citizens to get answers related to government information from virtual assistants like Alexa or Siri. To provide high-quality answers, these systems need to be fed with knowledge graphs. In this paper, we share our experience of constructing and using the first open government knowledge graph in the Netherlands. Based on the developed demonstrators, we elaborate on the value of having such a graph and demonstrate its use in the context of improved data browsing, multicriteria analysis for urban planning, and the development of location-aware chat bots. Full article

Computer science is a predominantly male field of study. Women face barriers while trying to insert themselves in the study of computer science. Those barriers extend to when women are exposed to the professional area of computer science. Despite decades of social fights for gender equity in Science, Technology, Engineering, and Mathematics (STEM) education and in computer science in general, few women participate in computer science, and some of the reasons include gender bias and lack of support for women when choosing a computer science career. Open source software development has been increasingly used by companies seeking the competitive advantages gained by team diversity. This diversification of the characteristics of team members includes, for example, the age of the participants, the level of experience, education and knowledge in the area, and their gender. In open source software projects women are underrepresented and a series of biases are involved in their participation. This paper conducts a systematic literature review with the objective of finding factors that could assist in increasing women’s interest in contributing to open source communities and software development projects. The main contributions of this paper are: (i) identification of factors that cause women’s lack of interest (engagement), (ii) possible solutions to increase the engagement of this public, (iii) to outline the profile of professional women who are participating in open source software projects and software development projects. The main findings of this research reveal that women are underrepresented in software development projects and in open source software projects. They represent less than 10% of the total developers and the main causes of this underrepresentation may be associated with their workplace conditions, which reflect male gender bias. Full article

We address multimodal product attribute prediction of fashion items based on product images and titles. The product attributes, such as type, sub-type, cut or fit, are in a chain format, with previous attribute values constraining the values of the next attributes. We propose to address this task with a sequential prediction model that can learn to capture the dependencies between the different attribute values in the chain. Our experiments on three product datasets show that the sequential model outperforms two non-sequential baselines on all experimental datasets. Compared to other models, the sequential model is also better able to generate sequences of attribute chains not seen during training. We also measure the contributions of both image and textual input and show that while text-only models always outperform image-only models, only the multimodal sequential model combining both image and text improves over the text-only model on all experimental datasets. Full article

Opportunistic networks are considered as the promising network structures to implement traditional and typical infrastructure-based communication by enabling smart mobile devices in the networks to contact with each other within a fixed communication area. Because of the intermittent and unstable connections between sources and destinations, message routing and forwarding in opportunistic networks have become challenging and troublesome problems recently. In this paper, to improve the data dissemination environment, we propose an improved routing-forwarding strategy utilizing node profile and location prediction for opportunistic networks, which mainly includes three continuous phases: the collecting and updating of routing state information, community detection and optimization and node location prediction. Each mobile node in the networks is able to establish a network routing matrix after the entire process of information collecting and updating. Due to the concentrated population in urban areas and relatively few people in remote areas, the distribution of location prediction roughly presents a type of symmetry in opportunistic networks. Afterwards, the community optimization and location prediction mechanisms could be regarded as an significant foundation for data dissemination in the networks. Ultimately, experimental results demonstrate that the proposed algorithm could slightly enhance the delivery ratio and substantially degrade the network overhead and end-to-end delay as compared with the other four routing strategies. Full article

This paper deals with the Arabic translation taṣawwur in Averroes’ Great Commentary of the term τῶν ἀδιαιρέτων νόησις (“ton adiaireton noesis”, thinking of the indivisibles) in Aristotle’s De anima and the Latin translation from Arabic with (in-)formatio, as quoted by Albertus Magnus [...] Full article

With digital media, not only are media extensions of their human users, as McLuhan posited, but there is a flip or reversal in which the human users of digital media become an extension of those digital media as these media scoop up their data and use them to the advantage of those that control these media. The implications of this loss of privacy as we become “an item in a data bank” are explored and the field of captology is described. The feedback of the users of digital media become the feedforward for those media. Full article

This paper proposes a new collaborative and inclusive model for Knowledge Organization Systems (KOS) for sustaining cultural heritage and language diversity. It is based on contributions of end-users as well as scientific and scholarly communities from across borders, languages, nations, continents, and disciplines. It consists in collecting knowledge about all worldwide translations of one original work and sharing that data through a digital and interactive global knowledge map. Collected translations are processed in order to build multilingual parallel corpora for a large number of under-resourced languages as well as to highlight the transnational circulation of knowledge. Building such corpora is vital in preserving and expanding linguistic and traditional diversity. Our first experiment was conducted on the world-famous and well-traveled American novel Adventures of Huckleberry Finn by the American author Mark Twain. This paper reports on 10 parallel corpora that are now sentence-aligned pairs of English with Basque (an European under-resourced language), Bulgarian, Dutch, Finnish, German, Hungarian, Polish, Portuguese, Russian, and Ukrainian, processed out of 30 collected translations. Full article

Maps are one of the most conventional types of visualization used when conveying information to both inexperienced users and advanced analysts. However, the multivariate representation of data on maps is still considered an unsolved problem. We present a multivariate map that uses geo-space to guide the position of multivariate glyphs and enable users to interact with the map and glyphs, conveying meaningful data at different levels of detail. We develop an algorithm pipeline for this process and demonstrate how the user can adjust the level-of-detail of the resulting imagery. The algorithm features a unique combination of guided glyph placement, level-of-detail, dynamic zooming, and smooth transitions. We present a selection of user options to facilitate the exploration process and provide case studies to support how the application can be used. We also compare our placement algorithm with previous geo-spatial glyph placement algorithms. The result is a novel glyph placement solution to support multi-variate maps. Full article

(1) Background: Subjective well-being (SWB) is an individual’s judgment about their overall well-being. Research has shown that high subjective well-being contributes to overall health. SWB consists of both Affective and Cognitive dimensions. Existing studies on SWB are limited in two major ways: first, they focused mainly on the Affective dimension. Second, most existing studies are focused on individuals from the Western and Asian nations; (2) Methods: To resolve these weaknesses and contribute to research on personalizing persuasive health interventions to promote SWB, we conducted a large-scale study of 732 participants from Nigeria to investigate what factors affect their SWB using both the Affective and Cognitive dimensions and how distinct SWB components relates to different gender and age group. We employed the Structural Equation Model (SEM) and Confirmatory Factor Analysis (CFA) to develop models showing how gender and age relate to the distinct components of SWB; (3) Results: Our study reveals significant differences between gender and age groups. Males are more associated with social well-being and satisfaction with life components while females are more associated with emotional well-being. As regards age, younger adults (under 24) are more associated with social well-being and happiness while older adults (over 65) are more associated with psychological well-being, emotional well-being, and satisfaction with life. (4) Conclusions: The results could inform designers of the appropriate SWB components to target when personalizing persuasive health interventions to promote overall well-being for people belonging to various gender and age groups. We offer design guidelines for tailoring persuasive intervention to increase SWB based on an individual’s age and gender group. Finally, we map SWB components to possible persuasive technology design strategies that can be employed to implement them in persuasive interventions design. Full article

Aiming at granting wide access to their contents, online information providers often choose not to have registered users, and therefore must give up personalization. In this paper, we focus on the case of non-personalized news recommender systems, and explore persuasive techniques that can, nonetheless, be used to enhance recommendation presentation, with the aim of capturing the user’s interest on suggested items leveraging the way news is perceived. We present the results of two evaluations “in the wild”, carried out in the context of a real online magazine and based on data from 16,134 and 20,933 user sessions, respectively, where we empirically assessed the effectiveness of persuasion strategies which exploit logical fallacies and other techniques. Logical fallacies are inferential schemes known since antiquity that, even if formally invalid, appear as plausible and are therefore psychologically persuasive. In particular, our evaluations allowed us to compare three persuasive scenarios based on the Argumentum Ad Populum fallacy, on a modified version of the Argumentum ad Populum fallacy (Group-Ad Populum), and on no fallacy (neutral condition), respectively. Moreover, we studied the effects of the Accent Fallacy (in its visual variant), and of positive vs. negative Framing. Full article

Recently, with the development of big data and 5G networks, the number of intelligent mobile devices has increased dramatically, therefore the data that needs to be transmitted and processed in the networks has grown exponentially. It is difficult for the end-to-end communication mechanism proposed by traditional routing algorithms to implement the massive data transmission between mobile devices. Consequently, opportunistic social networks propose that the effective data transmission process could be implemented by selecting appropriate relay nodes. At present, most existing routing algorithms find suitable next-hop nodes by comparing the similarity degree between nodes. However, when evaluating the similarity between two mobile nodes, these routing algorithms either consider the mobility similarity between nodes, or only consider the social similarity between nodes. To improve the data dissemination environment, this paper proposes an effective data transmission strategy (MSSN) utilizing mobile and social similarities in opportunistic social networks. In our proposed strategy, we first calculate the mobile similarity between neighbor nodes and destination, set a mobile similarity threshold, and compute the social similarity between the nodes whose mobile similarity is greater than the threshold. The nodes with high mobile similarity degree to the destination node are the reliable relay nodes. After simulation experiments and comparison with other existing opportunistic social networks algorithms, the results show that the delivery ratio in the proposed algorithm is 0.80 on average, the average end-to-end delay is 23.1% lower than the FCNS algorithm (A fuzzy routing-forwarding algorithm exploiting comprehensive node similarity in opportunistic social networks), and the overhead on average is 14.9% lower than the Effective Information Transmission Based on Socialization Nodes (EIMST) algorithm. Full article

Communication languages convey information through the use of a set of symbols or units. Typically, this unit is word. When developing language technologies, as words in a language do not have the same prior probability, there may not be sufficient training data for each word to model. Furthermore, the training data may not cover all possible words in the language. Due to these data sparsity and word unit coverage issues, language technologies employ modeling of subword units or subunits, which are based on prior linguistic knowledge. For instance, development of speech technologies such as automatic speech recognition system presume that there exists a phonetic dictionary or at least a writing system for the target language. Such knowledge is not available for all languages in the world. In that direction, this article develops a hidden Markov model-based abstract methodology to extract subword units given only pairwise comparison between utterances (or realizations of words in the mode of communication), i.e., whether two utterances correspond to the same word or not. We validate the proposed methodology through investigations on spoken language and sign language. In the case of spoken language, we demonstrate that the proposed methodology can lead up to discovery of phone set and development of phonetic dictionary. In the case of sign language, we demonstrate how hand movement information can be effectively modeled for sign language processing and synthesized back to gain insight about the derived subunits. Full article

Though the self-portrait has been hailed as the defining artistic genre of modernity, there is not yet a good account of what the self-portrait actually is. This paper provides such an account through the lens of document theory and the philosophy of information. In this paper, the self-portrait is conceptualized as a kind of document, more specifically a kind of self-document, to gain insight into the phenomenon. A self-portrait is shown to be a construction, and not just a representation, of oneself. Creating a self-portrait then is a matter of bringing oneself forth over time—constructing oneself, rather than simply depicting oneself. This account provides grounds to consider whether or how the selfie truly is a form of self-portrait, as is often asserted. In the end, it seems that while both are technologies for self-construction, the self-portrait has the capacity for deep self-construction, whereas the selfie is limited to fewer aspects of the self. This prospect leads into an ethical discussion of the changing concept of identity in the digital age. Full article