Information, Volume 10, Issue 2 (February 2019) – 48 articles

At present, the identification of haze levels mostly relies on traditional measurement methods, the real-time operation and convenience of these methods are poor. This paper aims to realize the identification of haze levels based on the method of haze images processing. Therefore, this paper divides the haze images into five levels, and obtains the high-quality haze images in each level by the brightness correction of the optimization solution and the color correction of the feature matching. At the same time, in order to reduce the noise of the haze images, this article improved the Butterworth filter. Finally, based on the processed haze images, this paper uses the Faster R-CNN network to identify the haze levels. The results of multiple sets of comparison experiments demonstrate the accuracy of the study. Full article

Interval Pythagorean fuzzy set (IPFS), which can handle imprecise and ambiguous information, has attracted considerable attention in both theory and practice. However, one of the main difficulties under IPFSs is the comparison between interval numbers. To overcome this shortcoming, connection number theory is first introduced, and interval numbers are transformed into connection numbers in the operating process. Considering that similarity measures play an important role in assessing the degree between ideal and proposal alternatives in the decision making process, this paper aims to develop new similarity measures with IPFSs and apply them to multi-criteria decision making (MCDM) problems. The main contributions of this paper are as follows: (1) introduction of a comparison method through transforming interval numbers into connection numbers; (2) development of three new similarity measures with IPFSs based on the minimum and maximum operators, and investigation of their properties; (3) calculation of the similarity measures considering weights of membership and non-membership degrees; (4) establishment of an interval Pythagorean fuzzy decision making method applying the presented similarity measures. A case study on selecting a project delivery system is made to show the applicability of the proposed approach. Full article

The implementation of ubiquitous learning (u-learning) is a good opportunity to address the affordability, accessibility, and quality issues of the higher education sector that are present in many countries. However, its broad application is still limited within the instructionist paradigm. Improving its implementation performance is a challenge for researchers and practitioners. This study aimed to understand the readiness and success status of u-learning implementation in Indonesia and to assess factors influencing the status based on stakeholder perspectives. A mixed methods inquiry with the sequential nested strategy was carried out by focusing on a quantitative analysis. The findings revealed that the readiness constructs influence u-learning performance. The study may contribute, theoretically and practically, to future studies, especially u-learning implementation studies, from the perspective of a developing country. Full article

Formal concept analysis has proven to be a very effective method for data analysis and rule extraction, but how to build formal concept lattices is a difficult and hot topic. In this paper, an efficient and rapid incremental concept lattice construction algorithm is proposed. The algorithm, named FastAddExtent, is seen as a modification of AddIntent in which we improve two fundamental procedures, including fixing the covering relation and searching the canonical generator. The proposed algorithm can locate the desired concept quickly by adding data fields to every concept. The algorithm is depicted in detail, using a formal context to show how the new algorithm works and discussing time and space complexity issues. We also present an experimental evaluation of its performance and comparison with AddExtent. Experimental results show that the FastAddExtent algorithm can improve efficiency compared with the primitive AddExtent algorithm. Full article

In recent years positioning sensors have become ubiquitous, and there has been tremendous growth in the amount of trajectory data. It is a huge challenge to efficiently store and query massive trajectory data. Among the typical operation over trajectories, similarity query is an important yet complicated operator. It is useful in navigation systems, transportation optimizations, and so on. However, most existing studies have focused on handling the problem on a centralized system, while with a single machine it is difficult to satisfy the storage and processing requirements of mass data. A distributed framework for the similarity query of massive trajectory data is urgently needed. In this research, we propose DFTHR (distributed framework based on HBase and Redis) to support the similarity query using Hausdorff distance. DFTHR utilizes a segment-based data model with a number of optimizations for storing, indexing and pruning to ensure efficient querying capability. Furthermore, it adopts a bulk-based method to alleviate the cost for adjusting partitions, so that the incremental dataset can be efficiently supported. Additionally, DFTHR introduces a co-location-based distributed strategy and a node-locality-based parallel query algorithm to reduce the inter-worker cost overhead. Experiments show that DFTHR significantly outperforms other schemes. Full article

Parametric granular computing classification algorithms lead to difficulties in terms of parameter selection, the multiple performance times of algorithms, and increased algorithm complexity in comparison with nonparametric algorithms. We present nonparametric hyperbox granular computing classification algorithms (NPHBGrCs). Firstly, the granule has a hyperbox form, with the beginning point and the endpoint induced by any two vectors in N-dimensional (N-D) space. Secondly, the novel distance between the atomic hyperbox and the hyperbox granule is defined to determine the joining process between the atomic hyperbox and the hyperbox. Thirdly, classification problems are used to verify the designed NPHBGrC. The feasibility and superiority of NPHBGrC are demonstrated by the benchmark datasets compared with parametric algorithms such as HBGrC. Full article

By introducing an easy knapsack-type problem, a probabilistic knapsack-type public key cryptosystem (PKCHD) is proposed. It uses a Chinese remainder theorem to disguise the easy knapsack sequence. Thence, to recover the trapdoor information, the implicit attacker has to solve at least two hard number-theoretic problems, namely integer factorization and simultaneous Diophantine approximation problems. In PKCHD, the encryption function is nonlinear about the message vector. Under the re-linearization attack model, PKCHD obtains a high density and is secure against the low-density subset sum attacks, and the success probability for an attacker to recover the message vector with a single call to a lattice oracle is negligible. The infeasibilities of other attacks on the proposed PKCHD are also investigated. Meanwhile, it can use the hardest knapsack vector as the public key if its density evaluates the hardness of a knapsack instance. Furthermore, PKCHD only performs quadratic bit operations which confirms the efficiency of encrypting a message and deciphering a given cipher-text. Full article

In brain magnetic resonance (MR) images, image quality is often degraded due to the influence of noise and outliers, which brings some difficulties for doctors to segment and extract brain tissue accurately. In this paper, a modified robust fuzzy c-means (MRFCM) algorithm for brain MR image segmentation is proposed. According to the gray level information of the pixels in the local neighborhood, the deviation values of each adjacent pixel are calculated in kernel space based on their median value, and the normalized adaptive weighted measure of each pixel is obtained. Both impulse noise and Gaussian noise in the image can be effectively suppressed, and the detail and edge information of the brain MR image can be better preserved. At the same time, the gray histogram is used to replace single pixel during the clustering process. The results of segmentation of MRFCM are compared with the state-of-the-art algorithms based on fuzzy clustering, and the proposed algorithm has the stronger anti-noise property, better robustness to various noises and higher segmentation accuracy. Full article

We address the question of whether AI, and in particular the Singularity—the notion that AI-based computers can exceed human intelligence—is a fallacy or a great opportunity. We have invited a group of scholars to address this question, whose positions on the Singularity range from advocates to skeptics. No conclusion can be reached as the development of artificial intelligence is still in its infancy, and there is much wishful thinking and imagination in this issue rather than trustworthy data. The reader will find a cogent summary of the issues faced by researchers who are working to develop the field of artificial intelligence and in particular artificial general intelligence. The only conclusion that can be reached is that there exists a variety of well-argued positions as to where AI research is headed. Full article

One of the most desired features of autonomous robotic systems is their ability to accomplish complex tasks with a minimum amount of sensory information. Often, however, the limited amount of information (simplicity of sensors) should be compensated by more precise and complex control. An optimal tradeoff between the simplicity of sensors and control would result in robots featuring better robustness, higher throughput of production and lower production costs, reduced energy consumption, and the potential to be implemented at very small scales. In our work we focus on a society of very simple robots (modeled as agents in a multi-agent system) that feature an “extreme simplicity” of both sensors and control. The agents have a single line-of-sight sensor, two wheels in a differential drive configuration as effectors, and a controller that does not involve any computing, but rather—a direct mapping of the currently perceived environmental state into a pair of velocities of the two wheels. Also, we applied genetic algorithms to evolve a mapping that results in effective behavior of the team of predator agents, towards the goal of capturing the prey in the predator-prey pursuit problem (PPPP), and demonstrated that the simple agents featuring the canonical (straightforward) sensory morphology could hardly solve the PPPP. To enhance the performance of the evolved system of predator agents, we propose an asymmetric morphology featuring an angular offset of the sensor, relative to the longitudinal axis. The experimental results show that this change brings a considerable improvement of both the efficiency of evolution and the effectiveness of the evolved capturing behavior of agents. Finally, we verified that some of the best-evolved behaviors of predators with sensor offset of 20° are both (i) general in that they successfully resolve most of the additionally introduced, unforeseen initial situations, and (ii) robust to perception noise in that they show a limited degradation of the number of successfully solved initial situations. Full article

Based on comparative studies on correlation coefficient theory and utility theory, a series of rules that utility functions on dual hesitant fuzzy rough sets (DHFRSs) should satisfy, and a kind of novel utility function on DHFRSs are proposed. The characteristic of the introduced utility function is a parameter, which is determined by decision-makers according to their experiences. By using the proposed utility function on DHFRSs, a novel dual hesitant fuzzy rough pattern recognition method is also proposed. Furthermore, this study also points out that the classical dual tool is suitable to cope with dynamic data in exploratory data analysis situations, while the newly proposed one is suitable to cope with static data in confirmatory data analysis situations. Finally, a medical diagnosis and a traffic engineering example are introduced to reveal the effectiveness of the newly proposed utility functions on DHFRSs. Full article

With the large-scale growth of mobile terminal users, exploring their continuance use behavior attracts wide attention. However, there is insufficient research on user psychological states in different hierarchy models towards continuance use of a mobile terminal. This study advanced a mediation model of continuance use of a mobile terminal with a moderator based on expectation confirmation theory from the perspective of customer value dimension. Data were collected in China, and 311 samples were used to examine the hypotheses provided from the research model with the partial least squares structural equation modeling method. The empirical results indicate that perceived usefulness positively influences continuance use of a mobile terminal, and perceived usefulness positively influences satisfaction and in turn impacts on continuance use of a mobile terminal. Specially, there is a significant mediating effect of attitude loyalty between satisfaction and continuance use of a mobile terminal. Moreover, the results also verify that mobility negatively moderates the relationship between attitude loyalty and continuance use of a mobile terminal. Our findings suggest that user psychological states are in different levels to reflect mobile terminal user value during the process of continuance use of a mobile terminal. Moreover, our findings indicate the boundary condition in continuance use of a mobile terminal. Full article

The use of computers to simulate facial aging or rejuvenation has long been a hot research topic in the field of computer vision, and this technology can be applied in many fields, such as customs security, public places, and business entertainment. With the rapid increase in computing speeds, complex neural network algorithms can be implemented in an acceptable amount of time. In this paper, an optimized face-aging method based on a Deep Convolutional Generative Adversarial Network (DCGAN) is proposed. In this method, an original face image is initially mapped to a personal latent vector by an encoder, and then the personal potential vector is combined with the age condition vector and the gender condition vector through a connector. The output of the connector is the input of the generator. A stable and photo-realistic facial image is then generated by maintaining personalized facial features and changing age conditions. With regard to the objective function, the single adversarial loss of the Generated Adversarial Network (GAN) with the perceptual similarity loss is replaced by the perceptual similarity loss function, which is the weighted sum of adversarial loss, feature space loss, pixel space loss, and age loss. The experimental results show that the proposed method can synthesize an aging face with rich texture and visual reality and outperform similar work. Full article

In recent years, a great deal of interest has been shown toward big data. Much of the work on big data has focused on volume and velocity in order to consider dataset size. Indeed, the problems of variety, velocity, and veracity are equally important in dealing with the heterogeneity, diversity, and complexity of data, where semantic technologies can be explored to deal with these issues. This Special Issue aims at discussing emerging approaches from academic and industrial stakeholders for disseminating innovative solutions that explore how big data can leverage semantics, for example, by examining the challenges and opportunities arising from adapting and transferring semantic technologies to the big data context. Full article

Three-dimensional models have been extensively used in several applications including computer-aided design (CAD), video games, medical imaging due to the processing capability improvement of computers, and the development of network bandwidth. Therefore, the necessity of implementing 3D mesh watermarking schemes aiming to protect copyright has increased considerably. In this paper, a blind robust 3D mesh watermarking method based on mesh saliency and wavelet transform for copyright protection is proposed. The watermark is inserted by quantifying the wavelet coefficients using quantization index modulation (QIM) according to the mesh saliency of the 3D semiregular mesh. The synchronizing primitive is the distance between the mesh center and salient points in the descending order. The experimental results show the high imperceptibility of the proposed scheme while ensuring a good robustness against a wide range of attacks including smoothing, additive noise, element reordering, similarity transformations, etc. Full article

Open software repositories make large amounts of source code publicly available. Potentially, this source code could be used as training data to develop new, machine learning-based programming tools. For many applications, however, raw code scraped from online repositories does not constitute an adequate training dataset. Building on the recent and rapid improvements in machine translation (MT), one possibly very interesting application is code generation from natural language descriptions. One of the bottlenecks in developing these MT-inspired systems is the acquisition of parallel text-code corpora required for training code-generative models. This paper addresses the problem of automatically synthetizing parallel text-code corpora in the software testing domain. Our approach is based on the observation that self-documentation through descriptive method names is widely adopted in test automation, in particular for unit testing. Therefore, we propose synthesizing parallel corpora comprised of parsed test function names serving as code descriptions, aligned with the corresponding function bodies. We present the results of applying one of the state-of-the-art MT methods on such a generated dataset. Our experiments show that a neural MT model trained on our dataset can generate syntactically correct and semantically relevant short Java functions from quasi-natural language descriptions of functionality. Full article

This paper investigates the performance of two different adaptive control schemes for controlling the angular position of an electronic throttle (ET) plate. The adaptive backstepping controller and adaptive sliding mode backstepping controller are the controllers under consideration. The control design based on these adaptive controllers is firstly addressed and the stability analysis of each controller has been presented and the convergence of both position and estimation errors for both controllers have been proved. A comparison study of the performance of both controllers has been conducted in terms of system transient characteristics and the behavior of their associated adaptive gain. The simulation has been implemented within the environment of the MATLAB package. Full article

The blind recognition of the frame parameter plays a crucial role in frame synchronization in the background of a non-cooperation communication system. This paper proposes an algorithm based on self-correlation on the foundation of existed cumulative filtering algorithm. To overcome high BER, the peak-to-average ratio (PAR) is calculated to improve the algorithm. The simulation results proved that the performance of the algorithm has been improved. Full article

Human agents in technical customer support provide users with instructional answers to solve a task that would otherwise require a lot of time, money, energy, physical costs. Developing a dialogue system in this domain is challenging due to the broad variety of user questions. Moreover, user questions are noisy (for example, spelling mistakes), redundant and have various natural language expressions. In this work, we introduce a conversational system, MOLI (the name of our dialogue system), to solve customer questions by providing instructional answers from a knowledge base. Our approach combines models for question type and intent category classification with slot filling and a back-end knowledge base for filtering and ranking answers, and uses a dialog framework to actively query the user for missing information. For answer-ranking we find that sequential matching networks and neural multi-perspective sentence similarity networks clearly outperform baseline models, achieving a 43% error reduction. The end-to-end P@1(Precision at top 1) of MOLI was 0.69 and the customers’ satisfaction was 0.73. Full article

Public speaking anxiety is commonly treated using cognitive behavioral therapy. During the therapy session, the patient is either asked to vividly imagine and describe the feared stimulus or is confronted with it in the real world. Sometimes, however, it can be hard to imagine the object of fear or to create a controllable environment that contains this stimulus. Virtual reality exposure therapy (VRET) can help solve these problems by placing the patient in a simulated 3D environment. While standalone VRET applications have been investigated for more than 25 years, we are analyzing the viability of a cloud-based VRET system. In this paper, we discuss the architectural and technical choices made in order to create a mobile and lightweight solution that can be easily adapted by any psychology clinic. Moreover, we are analyzing data gathered from 30 participants who have undergone a VRET session for public speaking anxiety. Finally, the collected psychophysiological signals including galvanic skin response (GSR) and skin temperature are processed and investigated in order to evaluate our cloud-based VRET system. Full article

A labeled graph is a special structure with node identification capability, which is often used in information networks, biological networks, and other fields. The subgraph query is widely used as an important means of graph data analysis. As the size of the labeled graph increases and changes dynamically, users tend to focus on the high-match results that are of interest to them, and they want to take advantage of the relationship and number of results to get the results of the query quickly. For this reason, we consider the individual needs of users and propose a dynamic Top-K interesting subgraph query. This method establishes a novel graph topology feature index (GTSF index) including a node topology feature index (NTF index) and an edge feature index (EF index), which can effectively prune and filter the invalid nodes and edges that do not meet the restricted condition. The multi-factor candidate set filtering strategy is proposed based on the GTSF index, which can be further pruned to obtain fewer candidate sets. Then, we propose a dynamic Top-K interesting subgraph query method based on the idea of the sliding window to realize the dynamic modification of the matching results of the subgraph in the dynamic evolution of the label graph, to ensure real-time and accurate results of the query. In addition, considering the factors, such as frequent Input/Output (I/O) and network communication overheads, the optimization mechanism of the graph changes and an incremental maintenance strategy for the index are proposed to reduce the huge cost of redundant operation and global updates. The experimental results show that the proposed method can effectively deal with a dynamic Top-K interesting subgraph query on a large-scale labeled graph, at the same time the optimization mechanism of graph changes and the incremental maintenance strategy of the index can effectively reduce the maintenance overheads. Full article

The ever-increasing complexity in manufacturing environments has caused delay and uncertainty for on-site personnel when retrieving critical information. Currently, information associated with manufacturing environments is created and stored in centralized terminals and even paper-based logbooks. Any delays in accessing this information can hinder critical decision-making processes that are essential to ensure maximum efficiency and productivity. To increase the competitiveness of manufacturing companies, the authors present a user-oriented markerless augmented reality authoring solution for the manufacturing environment utilizing the capability of a modern smartphone or head-mounted display. With a one-time setup and calibration, the end user is able to augment a physical workspace with a digital layer and annotate any stationary object with application data including maintenance records, service tickets, and usage logs. By directly mapping the data onto a relevant physical object, it allows more efficient reuse and better contextualization of manufacturing information. The backend expandable data structure also enhances extensibility so that the users can modify the digital layer with ease when the physical environment changes. This annotation tool defines a novel way of managing manufacturing information to help companies work more efficiently and effectively. Full article

Three-way decision is a class of effective ways and heuristics commonly used in human problem solving and information processing. As an application of three-way decision in clustering, three-way clustering uses core region and fringe region to represent a cluster. The identified elements are assigned into the core region and the uncertain elements are assigned into the fringe region in order to reduce decision risk. In this paper, we propose a three-way clustering algorithm based on the ideas of cluster ensemble and three-way decision. In the proposed method, we use hard clustering methods to produce different clustering results and labels matching to align all clustering results to a given order. The intersection of the clusters with the same labels are regarded as the core region. The difference between the union and the intersection of the clusters with the same labels are regarded as the fringe region of the specific cluster. Therefore, a three-way clustering is naturally formed. The results on UCI data sets show that such a strategy is effective in improving the structure of clustering results. Full article

The Wellbeing Game uses game design elements to promote wellbeing. Players document their daily activities in the game and categorize them to one or more of five wellbeing-related factors. The users join teams and can create team events to work together and improve their wellbeing status. The present study aims to review the application and the theoretical base of ‘The Wellbeing Game’, to adapt it to the German context, and to evaluate its health effects in different settings. Additional aims are to analyze the current state of research regarding the links between health, wellbeing, and gamification and to identify crucial game design elements that have to be implemented in the application in order to address the needs of competence, autonomy, and social relatedness according to the self-determination theory. Full article

The adaptability of an in-progress business process is an essential requirement for any business process management system in dynamic business process environments. Over the last two decades, the artifact-centric approach for business process management has been evidenced to have higher level of flexibility. However, the adaptation of a model of an artifact-centric business process instance is still inevitable and pervasive due to the complex and ever-changing business environments. Almost all works of artifact-centric business process neglect this issue. To fill this gap, we propose a special business rule called adaptation rule to address the dynamic adaptation problem and describe the adaptation by a global adaptation model. Moreover, we provide a validation mechanism over our proposed adaptation rule of the global adaptation model to guarantee the behavior correctness of the adaptation. Through this validation approach, computing the lifecycle of the global adaptation model can be avoided. Full article

The purpose of this paper was to create service models for cognitively stimulating mobile games and incorporate them into Finnish and Chinese elderly care. The implementation involved the use of two different mobile games as part of the everyday lives of older adults in care homes in Finland (3 months) and China (6 months). Although a large number of publications examine serious games in elderly care, there are rather few publications related to the practical implementation within the elderly care processes. In general, rehabilitation orientated games should incorporate entertainment (motivation) and relevant therapeutic content (rehabilitation) in order to be effective. Regardless of the game design, successful implementation of the games in elderly care is paramount to benefit the end user. In this paper, two mobile games were investigated as a case study. To investigate the therapeutic content of the games, the game outcomes (game scores and time stamps) were automatically recorded to facilitate analysis of the participant’s progress during the trial. To investigate motivation, user feedback was collected through observation of the game trials and by interviewing the nursing staff and the participants (test group). The gaming service implementation was designed in collaboration with the nursing staff and researchers, according to an experimentation-driven approach, in which the service model ideas were tested by the professionals before piloting. In both countries, the players and the nursing staff found the games showed potential as self-managed rehabilitation tools. Other significant effects of gameplay were enhanced recreation and self-managed activity level. Despite cultural differences, the gaming experience was amazingly similar and improvements in game scores were also observed during the trial in both countries. The biggest difference between the pilots was the implementation process, which led to the development of two different service models that are reported in this paper. In Finland, the games were embedded into the care practices and the nursing staff were responsible for the piloting. In China, the games were independent of the care process and an external service provider (the researcher) managed the piloting. The findings imply that service design in different cultures should be carefully considered when implementing new digital services. Full article

Solving programming exercises is the best way to promote practice in computer programming courses and, hence, to learn a programming language. Meanwhile, programming courses continue to have an high rate of failures and dropouts. The main reasons are related with the inherent domain complexity, the teaching methodologies, and the absence of automatic systems with features such as intelligent authoring, profile-based exercise sequencing, content adaptation, and automatic evaluation on the student’s resolution. At the same time, gamification is being used as an approach to engage learners’ motivations. Despite its success, its implementation is still complex and based on ad-hoc and proprietary solutions. This paper presents PROud as a framework to inject gamification features in computer programming learning environments based on the usage data from programming exercises. This data can be divided into two categories: generic data produced by the learning environment—such as, the number of attempts and the duration that the students took to solve a specific exercise—or code-specific data produced by the assessment tool—such as, code size, use memory, or keyword detection. The data is gathered in cloud storage and can be consumed by the learning environment through the use of a client library that communicates with the server through an established Application Programming Interface (API). With the fetched data, the learning environment can generate new gamification assets (e.g., leaderboards, quests, levels) or enrich content adaptations and recommendations in the inner components such as the sequencing tools. The framework is evaluated on its usefulness in the creation of a gamification asset to present dynamic statistics on specific exercises. Full article

The paper discusses the relation between the “non-statement view” of the structuralist program in philosophy of science and agent-based simulation and the use of this relation for a deeper understanding of the verification and the validation of simulation models. To this end it uses the history of the gender desegregation process in German schools in the second half of the 20th century and two simulation models trying to explain and understand this historical process. The relation between the two simulation models on one hand and the structuralist reconstruction of the mental and verbal theory of the observed phenomenon is depicted step by step, showing the verification of the more recent simulation model along the lines of the formal definition of this theory. Finally, the simulation model is used to make two unobservable parameters measurable with the help of the formalised theory, which allows new insights into the historical process. Full article