Reviewerly/exHarmony huggingface dataset:
Bisimulation as path type for guarded recursive types

In type theory, coinductive types are used to represent processes, and are thus crucial for the formal verification of non-terminating reactive programs in proof assistants based on type theory, such as Coq and Agda. Currently, programming and reasoning about coinductive types is difficult for two reasons: The need for recursive definitions to be productive, and the lack of coincidence of the built-in identity types and the important notion of bisimilarity. Guarded recursion in the sense of Nakano has recently been suggested as a possible approach to dealing with the problem of productivity, allowing this to be encoded in types. Indeed, coinductive types can be encoded using a combination of guarded recursion and universal quantification over clocks. This paper studies the notion of bisimilarity for guarded recursive types in Ticked Cubical Type Theory, an extension of Cubical Type Theory with guarded recursion. We prove that, for any functor, an abstract, category theoretic notion of bisimilarity for the final guarded coalgebra is equivalent (in the sense of homotopy type theory) to path equality (the primitive notion of equality in cubical type theory). As a worked example we study a guarded notion of labelled transition systems, and show that, as a special case of the general theorem, path equality coincides with an adaptation of the usual notion of bisimulation for processes. In particular, this implies that guarded recursion can be used to give simple equational reasoning proofs of bisimilarity. This work should be seen as a step towards obtaining bisimilarity as path equality for coinductive types using the encodings mentioned above.
Data-based analysis of Laplacian Eigenmaps for manifold reduction in supervised Liquid State classifiers

The manuscript introduces a data-driven technique founded on Laplacian Eigenmaps for manifold reduction in bio-inspired Liquid State classifiers. Starting from a preliminary introduction about the algorithm and the need of using manifold reduction methods for data representation, a statistical analysis of hyperparameters involved in the Laplacian Eigenmaps technique is presented and the effects of quantisation on trained weights is discussed with a view to efficiently implement multiple parallel mappings in the digital domain.
Design of estimators for restoration of images degraded by haze using genetic programming

Restoring hazy images is challenging since it must account for several physical factors that are related to the image formation process. Existing analytical methods can only provide partial solutions because they rely on assumptions that may not be valid in practice. This research presents an effective method for restoring hazy images based on genetic programming. Using basic mathematical operators several computer programs that estimate the medium transmission function of hazy scenes are automatically evolved. Afterwards, image restoration is performed using the estimated transmission function in a physics-based restoration model. The proposed estimators are optimized with respect to the mean-absolute-error. Thus, the effects of haze are effectively removed while minimizing overprocessing artifacts. The performance of the evolved GP estimators given in terms of objective metrics and a subjective visual criterion, is evaluated on synthetic and real-life hazy images. Comparisons are carried out with state-of-the-art methods, showing that the evolved estimators can outperform these methods without incurring a loss in efficiency, and in most scenarios achieving improved performance that is statistically significant.
Evaluating students readiness, expectancy, acceptance and effectiveness of augmented reality based construction technology education

Augmented reality (AR) has the potential to enhance the teaching and learning experience in construction technology which involves the learning of construction processes and understanding the construction elements. Augmented reality also provides the ability to change and improve the nature of education. This is due to the possibility of overlaying media onto the real world for content consumption using smartphones and tablets devices, which enables students to access information at anywhere and anytime. However, before implementing a new approach to teaching, the state of whether the students are ready to use AR have to be identified. This also goes toward what the students expect when using AR in learning, how do they accept using AR and effectiveness of using AR in learning. Therefore the purpose of this study is to (1) Identify the readiness of students on using AR in teaching; (2) Identifying what do the students expect when using AR in learning construction technology; (3) Identifying the student’s acceptance of AR in learning; (4) The effectiveness of AR in construction technology learning. A quantitative method of analysis has been implemented measuring the mean score of objective 1-3 based on the student’s responses to the questionnaire. On the other hand, the second phase of the study which is to determine whether using AR is effective in learning was done by comparing pre-test and post-test results. Results from the study shows assuring indicators that students accept the usage of AR in construction technology education, the application also fulfils their expectations on what AR could aid in the learning process and for student’s acceptance, the result shows that students accepted the usage of AR as a learning tool. Meanwhile, the results regarding AR effectiveness on construction technology displayed noticeable improvements regarding student’s pre-test and post-test results with 68% of students display improvements in their scores.
Cryptanalysis of Round-Reduced Fantomas, Robin and iSCREAM

In this work, we focus on LS-design ciphers Fantomas, Robin, and iSCREAM. LS-designs are a family of bitslice ciphers aimed at efficient masked implementations against side-channel analysis. We have analyzed Fantomas and Robin with a technique that previously has not been applied to both algorithms or linear cryptanalysis. The idea behind linear cryptanalysis is to build a linear characteristic that describes the relation between plaintext and ciphertext bits. Such a relationship should hold with probability 0.5 (bias is zero) for a secure cipher. Therefore, we try to find a linear characteristic between plaintext and ciphertext where bias is not equal to zero. This non-random behavior of cipher could be converted to some key-recovery attack. For Fantomas and Robin, we find 5 and 7-round linear characteristics. Using these characteristics, we attack both the ciphers with reduced rounds and recover the key for the same number of rounds. We also apply linear cryptanalysis to the famous CAESAR candidate iSCREAM and the closely related LS-design Robin. For iScream, we apply linear cryptanalysis to the round-reduced cipher and find a 7-round best linear characteristics. Based on those linear characteristics we extend the path in the related-key scenario for a higher number of rounds.
Centroid-Based Clustering with αβ-Divergences

Centroid-based clustering is a widely used technique within unsupervised learning algorithms in many research fields. The success of any centroid-based clustering relies on the choice of the similarity measure under use. In recent years, most studies focused on including several divergence measures in the traditional hard k-means algorithm. In this article, we consider the problem of centroid-based clustering using the family of α β -divergences, which is governed by two parameters, α and β . We propose a new iterative algorithm, α β -k-means, giving closed-form solutions for the computation of the sided centroids. The algorithm can be fine-tuned by means of this pair of values, yielding a wide range of the most frequently used divergences. Moreover, it is guaranteed to converge to local minima for a wide range of values of the pair ( α , β ). Our theoretical contribution has been validated by several experiments performed with synthetic and real data and exploring the ( α , β ) plane. The numerical results obtained confirm the quality of the algorithm and its suitability to be used in several practical applications.
Research on the integrated system of case-based reasoning and Bayesian network

With the arrival of big data era, the integration of Case-Based Reasoning (CBR) and Bayesian Network (BN) has become increasingly a promising technology in implementing the intelligence of engineering application. To further improve the efficiency of the integrated system and make it adapt to the large number of parameter under big data, Within-Cross (WC) algorithm is proposed in this paper to assigned big data to each slave node of Hadoop platform for parallel data processing. The WC algorithm can greatly reduce the time costing of parallel data processing and thus improve the efficiency of the integrated system by fully using the computation resource of Hadoop platform. To further enhance the accuracy of the integrated system, this paper proposes a new method called Weighted Super Parameters of Dirichlet Distribution (WSPDD) algorithm to perform probability learning. The WSPDD algorithm gives a weight to each super parameter of Dirichlet Distribution of all problem parameters to adjust the result of probability statistics and then improve probability learning. Therefore, the accuracy of the integrated system is greatly improved. Finally, an application domain is taken as a case study to validate the proposed method.
ACTL: Adaptive Codebook Transfer Learning for Cross-Domain Recommendation

Collaborative filtering usually suffers from limited performance due to the data sparsity problem.Transfer learning presents an unprecedented opportunity to alleviate this issue through transfer useful knowledge from an auxiliary domain to a target domain.Cluster-level rating patterns transformation models have been widely used due to the loose restriction which does not assume the source overlaps users and items with the target.However, previous researches have never investigated the relationship between the codebook scale in transfer learning and the prediction accuracy in the target domain.Moreover, all existing rating patterns sharing models fix the codebook scale without considering the data features of the source domain.In this paper, we propose a novel model, namely ACTL, to efficiently and automatically discover the appropriate codebook scale, which balances both the computational cost and prediction accuracy and best matches the size and features of the source domain for the cross-domain recommendation.The extensive experiments on real-world datasets demonstrate that our algorithms get knowledge gain from the large source domain and clearly and solidly outperform the state-of-the-art fixed scale codebook transfer learning methods.
We Are Not Your Real Parents: Telling Causal from Confounded using MDL

Given data over variables (X1, …, Xm, Y) we consider the problem of finding out whether X jointly causes Y or whether they are all confounded by an unobserved latent variable Z. To do so, we take an information-theoretic approach based on Kolmogorov complexity. In a nutshell, we follow the postulate that first encoding the true cause, and then the effects given that cause, results in a shorter description than any other encoding of the observed variables.The ideal score is not computable, and hence we have to approximate it. We propose to do so using the Minimum Description Length (MDL) principle. We compare the MDL scores under the models where X causes Y and where there exists a latent variables Z confounding both X and Y and show our scores are consistent. To find potential confounders we propose using latent factor modeling, in particular, probabilistic PCA (PPCA).Empirical evaluation on both synthetic and real-world data shows that our method, CoCa, performs very well—even when the true generating process of the data is far from the assumptions made by the models we use. Moreover, it is robust as its accuracy goes hand in hand with its confidence.
Monotonic Smooth Takagi–Sugeno Fuzzy Systems With Fuzzy Sets With Compact Support

In this paper, sufficient conditions on monotonicity of Takagi-Sugeno (T-S) fuzzy systems with linear consequent parts with smooth membership functions with compact support (i.e., equal to zero outside a compact set) are derived. The presented conditions considerably reduce conservatism of those known for T-S fuzzy systems utilizing membership functions with the support that is not compact. Besides, two illustrative examples will demonstrate that the monotonicity requirement imposed on fuzzy systems may significantly improve their extrapolation capability.
The impact of hyperlinks on reading text

There has been debate about whether blue hyperlinks on the Web cause disruption to reading. A series of eye tracking experiments were conducted to explore if coloured words in black text had any impact on reading behaviour outside and inside a Web environment. Experiment 1 and 2 explored the saliency of coloured words embedded in single sentences and the impact on reading behaviour. In Experiment 3, the effects of coloured words/hyperlinks in passages of text in a Web-like environment was explored. Experiment 1 and 2 showed that multiple coloured words in text had no negative impact on reading behaviour. However, if the sentence featured only a single coloured word, a reduction in skipping rates was observed. This suggests that the visual saliency associated with a single coloured word may signal to the reader that the word is important, whereas this signalling is reduced when multiple words are coloured. In Experiment 3, when reading passages of text containing hyperlinks in a Web environment, participants showed a tendency to re-read sentences that contained hyperlinked, uncommon words compared to hyperlinked, common words. Hyperlinks highlight important information and suggest additional content, which for more difficult concepts, invites rereading of the preceding text.
Social-Aware Based Secure Relay Selection in Relay-Assisted D2D Communications

Relay-assisted transmission could effectively enhance the performance of Device-to-Device (D2D) communications when D2D user equipments (UEs) are too far away from each other or the quality of D2D channel is not good enough f... | Find, read and cite all the research you need on Tech Science Press
The Impact of Mobile Language Learning (WhatsApp) on EFL Context: Outcomes and Perceptions

Seeking to identify the impact of mobile language learning (WhatsApp) on the achievements of EFL learners, a quasi-experimental design study was applied at Al-Baha University in Saudi Arabia. This study examines the impact of mobile language learning in enhancing EFL students&amp;rsquo; English skills ability when learning English as a foreign language context. &#x0D; &#x0D; Particularly, the study intends to investigate the impact of mobile language learning (WhatsApp) in comparison to traditional learning in learning English skills on the achievement of EFL learners. Thus, the participants in this study included 48 male learners, aged 18&amp;ndash;22 years, preparatory year at Al-Baha University.&#x0D; &#x0D; The results highlighted that there are significant differences between the mean scores of the EFL learners who were taught English in the Mobile language learning (WhatsApp), and those who were taught English by using the traditional learning (the control group) in the post-test. This difference was in favour of the experimental group. However, the findings revealed that are not statistically significant differences between the EFL learners in the experimental classes and the students in the control groups in their English achievement test score at the pre-test.&#x0D; &#x0D; Furthermore, the results of this research revealed that most EFL learners claimed that they were enthusiastic to join English lessons through WhatsApp groups and expressed the belief that working in a WhatsApp group can boost their motivation and their academic results. Also, most EFL learners highlighted that using mobile language learning method (WhatsApp) enabled them to increase their social skills, confidence, while helping them to create positive relationships with their colleagues and the teacher.&#x0D; &#x0D; However, there were some obstacles and barriers to join WhatsApp learning groups, such as lack of access to the internet and lack of tendency to share and participate in the WhatsApp group.
Complex-valued Kalman filters based on Gaussian entropy

The conventional complex Kalman filter is based on the well-known mean square error criterion, which is optimal under the circular Gaussian assumption. When a real-world complex signal is involved, the state noise and the observation noise often present non-circular properties to some degree, and thus the conventional complex Kalman filter does not perform well under these circumstances. We propose a new complex Kalman filter in which the Gaussian entropy is adopted as the optimality criterion in place of the mean square error. Performance analysis shows that the steady-state error of the new algorithm decreases with the increase of the degree of non-circularity. Simulations are used to demonstrate the effectiveness of the proposed algorithm.
Graphic User Interface Design Principles for Designing Augmented Reality Applications

The reality is a combination of perception, reconstruction, and interaction.Augmented Reality is the advancement that layer over consistent everyday existence which includes content based interface, voice-based interfaces, voicebased interface and guide based or gesture-based interfaces, so designing augmented reality application interfaces is a difficult task for the maker.Designing a user interface which is not only easy to use and easy to learn but its more interactive and selfexplanatory which have high perceived affordability, perceived usefulness, consistency and high discoverability so that the user could easily recognized and understand the design.For this purpose, a lot of interface design principles such as learnability, Affordance, Simplicity, Memorability, Feedback, Visibility, Flexibly and others are introduced but there no such principles which explain the most appropriate interface design principles for designing an Augmented Reality application interfaces.Therefore, the basic goal of introducing design principles for Augmented Reality application interfaces is to match the user efforts and the computer display ("plot user input onto computer output") using an appropriate interface action symbol ("metaphors") or to make that application easy to use, easy to understand and easy to discover.In this study by observing augmented reality system and interfaces, few of well-known design principle related to GUI ("user-centered design") are identified and through them, few issues are shown which can be determined through the design principles.With the help of multiple studies, our study suggests different interface design principles which make designing Augmented Reality application interface more easier and more helpful for the maker as these principles make the interface more interactive, learnable and more usable.To accomplish and test our finding, Pokémon Go, an Augmented Reality game, was selected and all the suggested principles are implemented and tested on its interface.From the results, our study concludes that our identified principles are most important principles while developing and testing any Augmented Reality application interface.
Sharing Social Networks Using a Novel Differentially Private Graph Model

Online social networks (OSNs) often contain sensitive information about individuals. Therefore, anonymizing social network data before releasing it becomes an important issue. Recent research introduces several graph abstraction models to extract graph features and add sufficient noise to achieve differential privacy.In this paper, we design and analyze a comprehensive differentially private graph model that combines the dK-1, dK-2, and dK-3 series together. The dK-1 series stores the degree frequency, the dK-2 series adds the joint degree frequency, and the dK-3 series contains the linking information between edges. In our scheme, low dimensional data makes the regeneration process more executable and effective, while high dimensional data preserves additional utility of the graph. As the higher dimensional model is more sensitive to the noise, we carefully design the executing sequence. The final released graph increases the graph utility under differential privacy.
An Algorithm for Scene Text Detection Using Multibox and Semantic Segmentation

An outside mutual correction (OMC) algorithm for natural scene text detection using multibox and semantic segmentation was developed. In the OMC algorithm, semantic segmentation and multibox were processed in parallel, and the text detection results were mutually corrected. The mutual correction process was divided into two steps: (1) The semantic segmentation results were employed in the bounding box enhancement module (BEM) to correct the multibox results. (2) The semantic bounding box module (SBM) was used to optimize the adhesion text boundary of the semantic segmentation results. Non-maximum suppression (NMS) was adopted to merge the SBM and BEM results. Our algorithm was evaluated on the ICDAR2013 and SVT datasets. The experimental results show that the developed algorithm had a maximum increase of 13.62% in the F-measure score and the highest F-measure score was 81.38%.
What does the mind learn? A comparison of human and machine learning representations

We present a brief review of modern machine learning techniques and their use in models of human mental representations, detailing three notable branches: spatial methods, logical methods and artificial neural networks. Each of these branches contains an extensive set of systems, and demonstrate accurate emulations of human learning of categories, concepts and language, despite substantial differences in operation. We suggest that continued applications will allow cognitive researchers the ability to model the complex real-world problems where machine learning has recently been successful, providing more complete behavioural descriptions. This will, however, also require careful consideration of appropriate algorithmic constraints alongside these methods in order to find a combination which captures both the strengths and weaknesses of human cognition.
Contextual Action Cues from Camera Sensor for Multi-Stream Action Recognition

In action recognition research, two primary types of information are appearance and motion information that is learned from RGB images through visual sensors. However, depending on the action characteristics, contextual information, such as the existence of specific objects or globally-shared information in the image, becomes vital information to define the action. For example, the existence of the ball is vital information distinguishing “kicking” from “running”. Furthermore, some actions share typical global abstract poses, which can be used as a key to classify actions. Based on these observations, we propose the multi-stream network model, which incorporates spatial, temporal, and contextual cues in the image for action recognition. We experimented on the proposed method using C3D or inflated 3D ConvNet (I3D) as a backbone network, regarding two different action recognition datasets. As a result, we observed overall improvement in accuracy, demonstrating the effectiveness of our proposed method.