Reviewerly/exHarmony huggingface dataset:
Towards Consistency Analysis between Formal and Informal Software Architecture Artefacts

Documenting the architecture of a software system is important, especially to capture reasoning and design decisions. A lot of tacit knowledge can easily get lost when the documentation is incomplete, resulting in threats for the software system's success and increased costs. However, software architecture documentation is often missing or outdated. One explanation for this phenomenon is the tedious and costly process of creating documentation in comparison to (perceived) low benefits. In this paper, we first present our long-term vision, where we plan to persist information from any sources, e.g. from whiteboard discussions, to avoid losing crucial information about a system. A core problem in this vision is the possible inconsistency of information from different sources. A major challenge of ensuring consistency is the consistency between formal artefacts, i.e. models, and informal documentation. We plan to address consistency analyses between models and textual natural language artefacts using natural language understanding and plan to include knowledge bases to improve these analyses. After extracting information out of the natural language documents, we plan to create traceability links and check whether statements within the textual documentation are consistent with the software architecture models. In this paper, we also outline our requirements for evaluating our approach with the help of a community-wide infrastructure and how our approach can be used to maintain community-wide case studies.
Multiplayer Virtual Reality Game for Pediatric Patients

Virtual reality (VR) games represent a new world for players and game developers alike. Game developers have made numerous innovations in this field, and they continue to search for new ways to immerse players in the VR gaming world. Many challenges arise in the effort to troubleshoot new problems that come with the development of VR. Consumer satisfaction is extremely important, and game developers constantly work to improve and enhance the player experience. In particular, pediatric patients as a whole face unique challenges, and developers must aim to create specific gaming experiences for these individuals. VR gaming may provide multiple therapeutic effects, such as providing analgesia, relieving anxiety, and rehabilitation.
Efficient Pre-processing and Feature Selection for Clustering of Cancer Tweets

The impact of social media in our daily life cannot be overlooked. Harnessing this rich and varied data for information is a challenging job for the data analysts. As each type of data from social media is unstructured, these data have to be processed, represented and then analysed in different ways suitable to our requirements. Though retail industry and political people are using social media to a great extent to gather feedback and market their new ideas, its significance in other fields related to public like health care and security is not dealt with effectively. Though the information coming from social media may be informal, it contains genuine opinions and experiences which are very much necessary to improve the healthcare service. This work explores analysing the Twitter data related to the most dreaded disease 'cancer'. We have collected over one million tweets related to various types of cancer and summarized the same to a bunch of representative tweets which may give key inputs to healthcare professionals regarding symptoms, diagnosis, treatment and recovery related to cancer. This, when correlated with clinical research and inputs, may provide rich information to provide a holistic treatment to the patients. We have proposed additional pre-processing to the raw data. We have also explored a combination of feature selection methods, two feature extraction methods and a soft clustering algorithm to study the feasibility of the same for our data. The results have proved our intuition right about underlying information and also show that there is a tremendous scope for further research in the area.
Design and Research on Evaluation Model of User Experience on Mobile Terminal Products

With the rapid progress of science and technology, more and more Internet products emerge. User experience is a kind of purely subjective feeling established by users in the process of using the product. Through the analysis of Nielsen’s ten usability principles and five elements of user experience, this paper make the four elements integrated into the three dimensions of content, interaction and vision, which are different perspectives of user perception and evaluation of user experience of Internet products. According to the above, the CIV evaluation model of Internet products mobile terminal product user experience is established. The CIV model proposed in this paper is suitable for the optimization design of online products. According to this evaluation model, products that meet user requirements can be iterated.
The Vision–Brain Hypothesis

In this chapter, the vision–brain hypothesisVision–brain hypothesis, The is illustrated in three steps. First, we hypothesize that vision decides the robots’ attention and the attention could be regulated without brain-inspired objects detection and tracking. This could be highlighted by the difference in attention mechanisms between the manned and unmanned systemsUnmanned systems . Regulated attention in unmanned systems has a significant implication to the robots’ cognition accuracy and response speed. Therefore, the current learning systemsLearning systems must be optimized. Such optimization can be interpreted as an integration of deep learningDeep learning with other hybrid adaptive algorithmsAdaptive algorithms . Second, we hypothesize that if a region of interest had been located by a “vision–brain,” then scene understandingScene understanding and partition could be smoothly carried out, which help cognition systems to reduce the loss from mid-aligned samples addition by employing non-adaptive random projectionsNon-adaptive random projections instead of self-taught learningSelf-taught learning . Third, we hypothesize that cognition rates of the “vision–brain” could approach to 100%, which finally establishes the robustness and efficiency of the vision–brain. A broad learning systemBroad learning system (BLS) was integrated with a decision layerDecision layer (the vision–brain) to address the issue whether face recognitionFace recognition rates can reach 100%. And in the next chapter, we will show that face recognition rates can reach 100% in BLS with the vision–brain, as verified by a challenging AR database with real occlusionOcclusion . BLS performance in face recognition on other bigger databases remains unknown and worthy of further attempts.
Determination of the Growing Season for Dry Land Agriculture Based on Thornthwaite Method in Bulukumba Regency, South Sulawesi Province

Abstract Drought events in a region are strongly related to the surface water availability and low rainfall. Furthermore, drought conditions impact the threat of food security. Bulukumba Regency in South Sulawesi Province is one of the areas that are prone to drought and the risk of crop failure. Therefore it needs anticipatory action to understand the drought index in this region. This study aims to identify the conditions of drought and agitation in the Bulukumba District of South Sulawesi Province. The results of the identification then became the basis for determining the growing season in various types of dryland agriculture cultivation. This study used the Thornthwaite method with a water balance and drought index approach. The sampling method in this study was purposive sampling. Data collection techniques carried out are observation techniques, laboratory analysis techniques, and documentation techniques. Data processing used quantitative techniques and descriptive approaches. The results of the study show that the growing season in dryland agriculture in November, December, and January based on the availability of water in this region. Conversely, the availability of water is very low in May to September and not appropriate to handling the cultivation.
Reinforcement Learning for Active Damping of Harmonically Excited Pendulum with Highly Nonlinear Actuator

Active vibration dampers can reduce or eliminate unwanted vibrations, but determining a good control policy can be challenging for highly nonlinear systems. For these types of systems, reinforcement learning is one method to optimize a control policy with only limited prior knowledge of the system dynamics. An experimental system was constructed by attaching a permanent magnet to the end of a pendulum and positioning an electromagnetic actuator below the resting position of the pendulum. The pendulum was excited with a sinusoidal force applied horizontally at the pivot point, and the control input was the applied voltage across the electromagnet. Due to the geometric arrangement and the strong dependence of magnetic force on distance, the relationship between the position of the pendulum and the actuation torque for any control input was highly nonlinear. A generalized version of the PILCO reinforcement learning algorithm was used to optimize a control policy for the electromagnet with the objective of minimizing the distance between the end of the pendulum and the downward position. After 16 s of interaction with the experimental system, the resulting learned policy was able to substantially reduce the amplitude of oscillation. This experiment illustrates the applicability of reinforcement learning to highly nonlinear active vibration damping problems.
The AREA Algorithm Framework Enabling Location-based Mobile Augmented Reality Applications

The dramatically increased computational capabilities of mobile devices have leveraged the opportunities for mobile application engineers. Respective scenarios, in which these opportunities can be exploited, emerge almost per day. In this context, mobile augmented reality applications play an important role in many business scenarios. In the automotive domain, they are mainly used to provide car customers with new experiences. For example, customers can use their own mobile device to experience the interior of a car by moving the mobile device around. The device’s camera then detects interior parts and shows additional information to the customer within the camera view. Although the computational capabilities have been increased, the realization of such mobile augmented reality applications is still a complex endeavor. In particular, the different mobile operating systems and their peculiarities must be carefully considered. In the AREA (Augmented Reality Engine Application) project, a powerful kernel was realized that enables location-based mobile augmented reality applications. This kernel, in turn, mainly focuses on robustness and performance. In addition, it provides a flexible architecture that fosters the development of individual location-based mobile augmented reality applications. As many aspects have to be considered to implement individual applications based on top of AREA, this paper provides the first comprehensive overview of the entire algorithm framework. Moreover, a recently realized algorithm and new features will be presented. To demonstrate the applicability of the kernel, its features are applied in the context of various mobile applications. As the major lesson learned, powerful mobile augmented reality applications can be efficiently run on present mobile operating systems and be effectively realized by engineers using AREA. We consider such mobile frameworks as being crucial to provide more generic concepts that are able to abstract from the peculiarities of the underlying mobile operating system and to support mobile application developers more properly.
Triggering Probabilistic Neural Networks with Flower Pollination Algorithm

The FlowerKowalski, Piotr A. Pollination Algorithm (FPA) is a modern heuristic technique that is applicable for the purposes of deriving best solution within several optimization tasks. This paper is a example of utilizing thisWadas, Konrad metaheuristic procedure for the Probabilistic Neural Network (PNN) learning process. In this paper, for the purpose of classification, this type of Neural Network is applied to data sets drawn from the UCI Machine Learning Repository. Moreover, we concentrate upon investigating the inertial parameters of FPA, as well as the overfitting aspect.
An Open-Access Platform for Transparent and Reproducible Structured Searching

Knowledge workers such as patent agents, recruiters and legal researchers undertake work tasks in which search forms a core part of their duties. In these instances, the search task often involves formulation of complex queries expressed as Boolean strings. However, creating effective Boolean queries remains an ongoing challenge, often compromised by errors and inefficiencies. In this paper, we demonstrate a new approach to structured searching in which concepts are expressed as objects on a two-dimensional canvas. Interactive query suggestions are provided via an NLP services API, and support is offered for optimising, translating and sharing search strategies as executable artefacts. This eliminates many sources of error, makes the query semantics more transparent, and offers an open-access platform for sharing reproducible search strategies and best practices.
YourMOOC4all: A Recommender System for MOOCs Based on Collaborative Filtering Implementing UDL

YourMOOC4all is a pilot research project to collect feedback requests regarding accessible design for Massive Open Online Courses (MOOCs). In this online application, a specific website offers the possibility for any learner to freely judge if a particular MOOC complies Universal Design for Learning (UDL) principles. User feedback is of great value for the future development of MOOC platforms and MOOC educational resources, as it will help to follow Design for All guidelines. YourMOOC4all is a recommender system which gathers valuable information directly from learners to improve aspects such as the quality, accessibility and usability of this online learning environment. The final objective of collecting user’s feedback is to advice MOOC providers about the missing means for meeting learner needs. This paper describes the pedagogical and technological background of YourMOOC4all and its use cases.
Motion Sickness: Our Evolving Understanding and Problems

Motion sickness can be evoked by nearly all forms of transport including automobiles, ships, trains, spacecraft, as well as by virtual reality systems (cybersickness) and optokinetic stimulation. The introduction of autonomous cars and aircraft and of space tourism will be additional ways of inducing motion sickness. Great progress has been made recently in understanding the neural mechanisms underlying the evocation of motion sickness. Motion sickness encompasses many factors that impair performance and the sense of well-being besides nausea and vomiting. Sex differences in susceptibility are minimal in experimental evaluations.
Temporal Logic Programs with Temporal Description Logic Axioms

In this paper we introduce a combination of Answer Set Programming (ASP) and Description Logics (DL) (in particular, $$\mathcal{ALC}$$ ) on top of a modal temporal basis using connectives from Linear-time Temporal Logic (LTL). On the one hand, for the temporal extension of $$\mathcal{ALC}$$ , we depart from Baader et al.’s proposal $$\mathcal{ALC}$$ -LTL that restricts the use of temporal operators to occur only in front of DL axioms. On the other hand, for the temporal extension of ASP we use its formalization in terms of Temporal (Quantified) Equilibrium Logic (TEL). This choice is convenient since (non-temporal) Equilibrium Logic has been already used to capture the semantics of hybrid theories, that is, combinations of ASP programs with DL axioms. Our proposal, called $$\mathcal{ALC}$$ -TEL, actually interprets $$\mathcal{ALC}$$ axioms in terms of their translation into first order sentences, so that the semantics of TEL is eventually used in the background. The resulting formalism conservatively extends TEL, hybrid theories and $$\mathcal{ALC}$$ -LTL as particular cases.
Computational Phase Transition Signature in Gibbs Sampling

Gibbs sampling is fundamental to a wide range of computer algorithms. Such algorithms are set to be replaced by physics based processors$-$be it quantum or stochastic annealing devices$-$which embed problem instances and evolve a physical system into an ensemble to recover a probability distribution. At a critical constraint to variable ratio, decision problems$-$such as propositional satisfiability$-$appear to statistically exhibit an abrupt transition in required computational resources. This so called, algorithmic or computational phase transition signature, has yet-to-be observed in contemporary physics based processors. We found that the computational phase transition admits a signature in Gibbs' distributions and hence we predict and prescribe the physical observation of this effect. We simulate such an experiment, that when realized experimentally, we believe would represent a milestone in the physical theory of computation.
Efficient Linear Feature Extraction Based on Large Margin Nearest Neighbor

Linear feature extraction methods have become indispensable tools in pattern recognition. The linear dimensionality reduction optimizes some objective to produce a linear transformation and derives the discriminative low-dimensional transformed data wherein the similarly labeled samples cluster tightly and the differently labeled samples keep away from one another. In the past, most of the methods achieve between-class distance by maximizing between-class-center-mean to make the differently labeled samples separated from each other. However, the samples in this study located in the class margin or within the other classes called hard samples often move slowly, which results in a small between-class distance and deteriorates the classification performance. Hence, we utilize the large margin nearest neighbor (LMNN) to quickly push only the hard samples toward the center of the class to get the large class margin instead of between-class, further improving the performance of classification. Combined with the linear discriminant analysis (LDA), a novel linear feature extraction method called LDA-LMNN is proposed. The method can address the limitations of LDA. Furthermore, the dropout is employed to the learning of linear transform matrix to improve the generalization ability and overcome the overfitting. The comparative experiments on various real-world datasets by using the state-of-the-art feature extraction methods demonstrate the effectiveness of the proposed method.
Code Transformation Issues in Move-Instance-Method Refactorings

Refactorings, by definition, preserve the behavior of a target program. Such a strong semantic property is encoded by a set of preconditions for each refactoring. Only if all preconditions are satisfied will a target program be transformed. The code transformation that implements the refactoring follows another set of rules to produce syntactically-correct, refactored code. Consequently, it is easy to believe that most behavior-changing violations in refactorings are induced by incorrect preconditions or lack of required checks. In this paper, however, we show that code transformations for Move-Instance-Method Refactoring available in several popular Java Integrated Development Environments do not preserve program behavior. We report these errors and propose solutions for each identified problem.
Blockchain Technology: An Aid to the Governance of Smart Cities

Blockchain technology has spread its roots from the crypto-currency domain and found a way to handle the processes of data storage, data management, smart contracting, authentication, validation, trust-free transaction handling, and shared economy. In the era of smart cities, the shift from a system that depends upon conventional data management based on ledgers and government records to smarter, immutable and transparent blockchain mode of handling data and its transactions is inevitable. The success of ‘Bitcoin’ can be viewed as a proof of the success of blockchain technology, and thus, it should be seen as a breakthrough to solve various issues faced by digitization of transactions and crucial documents. The decentralized architecture of blockchain can support Internet of Things applications, governance of various smart businesses and smart cities beyond our imagination.
Intrinsic Evaluation for English–Tamil Bilingual Word Embeddings

Despite theSanjanasri, J. P. growthMenon, Vijay Krishna ofRajendran, S. bilingualSoman, K. P. wordAnand Kumar, M. embeddings, there is no work done so far, for directly evaluating them for English–Tamil language pair. In this paper, we present a data resource and evaluation for the English–Tamil bilingual word vector model. In this paper, we present dataset and the evaluation paradigm for English–Tamil bilingual language pair. This dataset contains words that covers a range of concepts that occur in natural language. The dataset is scored based on the similarity rather than association or relatedness. Hence, the word pairs that are associated but not literally similar have a low rating. The measures are quantified further to ensure consistency in the dataset, mimicking the cognitive phenomena. Henceforth, the dataset can be used by non-native speakers, with minimal effort. We also present some inferences and insights into the semantics captured by word vectors and human cognition.
Correcting Polish Bigrams and Diacritical Marks

Medical information in clinical systems is stored in electronic health records, tracking all doctor–patient interactions, describing symptoms, diagnosis, tests results, prescribed medication and procedures etc. Most of this data is written manually, which is the main root of misspellings. For text analytic purposes the quality of documents is very important. The paper presents a proposal of modified approach based on Damerau-Levenshtein method for automatic misspellings correction. The special care is taken for cases of misspelling of Polish special characters, which are the most common type of typing errors. The method was tested on electronic health records in the field of oncology. It obtains 0.96 precision value that allows you to apply the developed method for automatic error correction in medical texts.
The Effect of the Flipped Learning and PBL on Participation in Class, Problem Solving Ability and Personality for College Students

This study aimed to the effect of the flipped leaning and PBL (problem based learning) on participation in class, problem solving ability and personality for college students. The operational test was administered to 81 college students. To achieve the purpose of this research, the research was carried out through the process of preparation, execution, and evaluation. To verify the effect of the flipped learning and PBL on college students, the data were analyzed using t-test. The results of the study were as follows. First, the influence of the flipped learning and PBL on the university students, it was proved that the average of the participations in class’s had increased with statistically meaningful difference. Second, the influence of the flipped learning and PBL on the university students, it was proved that the average of the problem solving ability’s had increased with statistically meaningful difference. Third, the influence of the flipped learning and PBL on the university students, it was proved that the average of the personality’s had increased with statistically meaningful difference. This study concludes with discussion of its implications and limitations.