Reviewerly/exHarmony huggingface dataset:
Nonparametric registration to low-dimensional function spaces

Registration aims to decompose amplitude and phase variation of samples of curves. Phase variation is captured by warping functions which monotonically transform the domains. Resulting registered curves should then only exhibit amplitude variation. Most existing methods assume that all sample functions exhibit a typical sequence of shape features like peaks or valleys, and registration focuses on aligning these features. A more general perspective is adopted which goes beyond feature alignment. A registration method is introduced where warping functions are defined in such a way that the resulting registered curves span a low dimensional linear function space. The approach may be used as a tool for analyzing any type of functional data satisfying a structural regularity condition called bounded shape variation. Problems of identifiability are discussed in detail, and connections to established registration procedures are analyzed. The method is applied to real and simulated data.
VR puzzle room for cognitive rehabilitation

The goal of this VR system is to simulate a puzzle / challenge for adaption in cognitive rehabilitation training. Development of the VR system is inspired by a 1st person puzzle-platform game where the player must navigate and complete through a series of puzzle rooms with each room being more difficult than the last. The unique features in this work include the use of “portals” and “portal gun”. The portal gun allows the player to shoot two separate “portals” on walls that will allow anything to be teleported from one portal to the other. Implemented on the Unity engine and SteamVR, the VR Toolkit is employed in modeling and script development. Technical innovations are made in modeling the animated and self-collision detectable spider; upon being collided with a weapon (bullet or blade) it uses a special dissolve shader to give the effect of disappearing gradually from the game. We present the specific tasks of cognitive rehabilitation, conceptualization of VR techniques and the detailed implementation of the system.
A Hybrid Collaborative Filtering Recommendation Algorithm Using Double Neighbor Selection

The traditional collaborative filtering algorithms are more successful used for personalized recommendation. However, the traditional collaborative filtering algorithm usually has issues such as low recommendation accuracy and cold start. Aiming at addressing the above problems, a hybrid collaborative filtering algorithm using double neighbor selection is proposed. Firstly, according to the results of user’s dynamic similarity calculation, the similar interest sets of the target users may be dynamically selected. Analyzing the dynamic similar interest set of the target user, we can divide the users into two categories, one is an active user, and the other is a non-active user. For the active user, by calculating the trust degree of the users with similar interests, we can select the user with the trust degree of TOP-N, and recommend the target user. For the non-active user, the neighbor user may be found according to the similarity of the user on some attributes, and them with high similarity will be recommend to the target user. The experimental results show that the algorithm not only improves the recommending accuracy of the recommendation system, but also effectively solves the problem of data sparseness and user cold start.
CompactRIO Based Real Time Implementation of AES Algorithm for Embedded Applications

For real-time embedded applications, several factors (time, cost, power) that are moving security considerations from a function-centric perspective into a system architecture (hardware/software) design issue. The National Institute of Standards and Technology (NIST) adopts Advanced Encryption Standard (AES) as the most widely used encryption algorithm in many security applications. The AES algorithm specifies 10, 12 and 14 rounds offering different levels of security. Although the number of rounds determines the strength of security, the power consumption issue has risen recently, especially in real-time embedded systems. In this article, the authors present real time implementation of the AES encryption on the compactRIO platform for a different number of AES rounds. The target hardware is NI cRIO-9022 embedded real-time controller from National Instruments (NI). The real time encryption processing has been verified successfully. The power consumption and encryption time experimental results are presented graphically for 10, 12 and 14 rounds of processing.
Informational Linguistics: Computer, Internet, Artificial Intelligence and Language

Modern technological progress is clearly mediated via the informational and communicational conceptualizations, which are first of all of language nature. Interdependence and syncretism of human cognitive activity create unlimited demand for knowledge interpretation of certain semantic format - information. Informational Linguistics is a discipline, dedicated to the interdisciplinary investigation of the specifics of communication contents.
Look‐ahead content balancing method in variable‐length computerized classification testing

Content balancing is one of the most important issues in computerized classification testing. To adapt to variable‐length forms, special treatments are needed to successfully control content constraints without knowledge of test length during the test. To this end, we propose the notions of ‘look‐ahead’ and ‘step size’ to adaptively control content constraints in each item selection step. The step size gives a prediction of the number of items to be selected at the current stage, that is, how far we will look ahead. Two look‐ahead content balancing (LA‐CB) methods, one with a constant step size and another with an adaptive step size, are proposed as feasible solutions to balancing content areas in variable‐length computerized classification testing. The proposed LA‐CB methods are compared with conventional item selection methods in variable‐length tests and are examined with different classification methods. Simulation results show that, integrated with heuristic item selection methods, the proposed LA‐CB methods result in fewer constraint violations and can maintain higher classification accuracy. In addition, the LA‐CB method with an adaptive step size outperforms that with a constant step size in content management. Furthermore, the LA‐CB methods generate higher test efficiency while using the sequential probability ratio test classification method.
Using Randomness to Improve Robustness of Tree-based Models Against Evasion Attacks

Machine learning models have been widely used in security applications. However, it is well-known that adversaries can adapt their attacks to evade detection. There has been some work on making machine learning models more robust to such attacks. However, one simple but promising approach called randomization is under-explored. In addition, most existing works focus on models with differentiable error functions while tree-based models do not have such error functions but are quite popular because they are easy to interpret. This paper proposes a novel randomization-based approach to improve robustness of tree-based models against evasion attacks. The proposed approach incorporates randomization into both model training time and model application time (meaning when the model is used to detect attacks). We also apply this approach to random forest, an existing ML method which already has incorporated randomness at training time but still often fails to generate robust models. We proposed a novel weighted-random-forest method to generate more robust models and a clustering method to add randomness at model application time. Experiments on intrusion detection and spam filtering data show that our approach further improves robustness of random-forest method.
Compact structures for continuous time Bayesian networks

The continuous time Bayesian network (CTBN) is a model capable of describing the probability distribution over a set of variables as it changes in time. The model relies on a directed graph structure to describe direct dependencies between variables, thereby simplifying the underlying parameters that describe the initial distribution and transition behavior. Although this approach can be effective in managing complexity, the size of the model can still be intractable if one variable depends directly on many other variables. To address this issue, we present two methods for imposing additional structure on the model that are capable of capturing regularities in the data that cannot be represented using the CTBN graph structure alone. We demonstrate how these methods can reduce model complexity and compare the representational capabilities of both approaches.
Multi-views Action Recognition on Deep Learning and K-SVD

In order to solve the problem of low action accuracy due to changes of view angles, this paper investigates this issue based on deep learning and K-SVD sparse algorithm. Firstly, the paper extracts the feature maps from the different views by convolutional neural networks (CNN) and long short term memory (LSTM), and the extracted feature maps are the multi-views high-level features with semantic information. Secondly, the paper uses the K-SVD sparse algorithm to get the dictionaries corresponding to each views, the dictionaries have very good sparse representations for the features of the action. Then the softmax classifier is used for classification and recognition. The results show that the accuracy are 89.22% and 91.4% on IXMAS datasets and WVU datasets respectively.
Method of lemmatizer selections in multiplexing lemmatization

The article describes the method of selecting a particular lemmatizer while multiplexing lemmatization using word's part of speech and suffix. The article also describes the experiment to determine the best suffix (or prefix) length for multiplexing lemmatization. Lemmatizers, that are used to build the multiplexing lemmatizer, show worse results, than the resulting lemmatizer. The developed lemmatizer is compared with the best theoretically possible multiplexing lemmatizer The practical problems of creating multiplexing lemmatizer are discussed.
An in-depth analysis of single-image subjective quality assessment of light field contents

Quality assessment of light field images poses new questions and challenges, due to the enriched nature of the content and the possibilities it offers at the rendering step.Image-based rendering is conventionally used to showcase the increased capabilities of light field contents on traditional 2D screens.However, the range of possibilities for rendering parameters is virtually endless, which poses the problem of what rendered images should be used when performing visual quality assessments, as well as how to properly present them to subjects during quality evaluations.Single-image assessment has been used in the past to conduct subjective quality evaluations.Since this type of assessment generates a large number of stimuli to be evaluated, which increases the complexity, length, and cost of the test, it is fundamental to analyze whether the added strain on the evaluation procedure is compensated by statistically relevant results.In this paper, we analyze the results of a subjective evaluation campaign that used single-image assessment by means of statistical tools, to understand whether the advantages of evaluating light field contents through separately rendered images counterbalance the increase in complexity.In particular, we test whether different types of rendering lead to statistically different ratings, and if testing a variety of rendering parameters through single-image assessment is advisable.Results provide useful guidelines to designs more efficient subjective quality assessment for light field contents.
A Class-Cluster k-Nearest Neighbors Method for Temporal In-Trouble Student Identification

Temporal in-trouble student identification is a classification task at the program level that predicts a final study status of a current student at the end of his/her study time using the data gathered from the students in the past. Moreover, this task focuses on correct predictions for the in-trouble students whose predicted labels are at the lowest performance level. Educational datasets in this task have many challenging characteristics such as multiple classes, overlapping, and imbalance. Simultaneously handling these characteristics has not yet been investigated in educational data mining. For the existing general-purpose works, their methods are not straightforwardly applicable to the educational datasets. Therefore, in this paper, a novel method is proposed as an effective solution to the previously defined task. Combining the traditional k-nearest neighbors and clustering ensemble methods, our method is designed with three new features: relax the number k of the nearest neighbors, use a set of the cluster-based neighbors newly generated by partitioning the subspace of each class, and set four new criteria to decide a final class label rather than the majority voting scheme. As a result, it is a new lazy learning method able to provide correct predictions of more instances belonging to a positive minority class. In an empirical evaluation, higher Accuracy, Recall, and F-measure confirmed the effectiveness of our method as compared to some popular methods on our two real educational datasets and the benchmarking "Iris" dataset.
A Comparative Study of Techniques for Avoiding Premature Convergence in Harmony Search Algorithm

The present article summarizes two techniques allowing to avoid premature convergence in Harmony Search algorithm, which was adapted for solving the instances of the Asymmetric Traveling Salesman Problem (ATSP). The efficiency of both approaches was demonstrated on the basis of the results of statistical test and 'test bed' consisting of nineteen instances of ATSP. The conclusion was that the best results were obtained in case of applying mechanisms which enable to reset the components of harmony memory at the moment of reaching stagnation. This process is controlled by parameters which are depended on the problem size.
A Data Preprocessing Method to Classify and Summarize Aspect-Based Opinions Using Deep Learning

Opinion summarization is based on aspect analyses of products, events or topics, which is a very interesting topic in natural language processing. Opinions are often expressed in various different ways in regards to objects. Therefore, it is important to express the characteristics of a product, event or topic in a final summary compiled by an automatic summarizing system. This paper proposes a method for conducting data preprocessing on the sentence level of a text using Convolutional Neural Networks. The corpus includes Vietnamese opinions on cars collected from social networking sites, forums, online newspapers and the websites of automobile dealers. The data processing phase will standardize terms for aspects that occur in opinion expressing aspects of the product. These aspects are used by manufacturers. Similarly, the standardization will be performed for both positive and negative terms used in opinions. The sentiment terms in the opinions will be replaced by standardized sentiment terms expressing the same sentiment polarities as those being replaced. This standardization is performed with the support of a semantic and sentiment ontology which has a tree hierarchy in the case of cars. This ontology ensures that the semantics and sentiment of the original opinion are not changed. The experimental results of the paper show that the proposed method gives better results than using no data preprocessing method for deep learning.
On Linear Logic, Functional Programming, and Attack Trees

This paper has two main contributions. The first is a new linear logical semantics of causal attack trees in four-valued truth tables. Our semantics is very simple and expressive, supporting specializations, and supports the ideal semantics of causal attack trees, and partially supporting the filter semantics of causal attack trees. Our second contribution is Lina, a new embedded, in Haskell, domain specific functional programming language for conducting threat analysis using attack trees. Lina has many benefits over existing tools; for example, Lina allows one to specify attack trees very abstractly, which provides the ability to develop libraries of attack trees, furthermore, Lina is compositional, allowing one to break down complex attack trees into smaller ones that can be reasoned about and analyzed incrementally. Furthermore, Lina supports automatically proving properties of attack trees, such as equivalences and specializations, using Maude and the semantics introduced in this paper.
Can Image Captioning Help Passage Retrieval in Multimodal Question Answering?

Passage retrieval for multimodal question answering, spanning natural language processing and computer vision, is a challenging task, particularly when the documentation to search from contains poor punctuation or obsolete word forms and with little labeled training data. Here, we introduce a novel approach to conducting passage retrieval for multimodal question answering of ancient artworks where the query image caption of the multimodal query is provided as additional evidence to state-of-the-art retrieval models in the cultural heritage domain trained on a small dataset. The query image caption is generated with an advanced image captioning model trained on an external dataset. Consequently, the retrieval model obtains transferred knowledge from the external dataset. Extensive experiments prove the efficiency of this approach on a benchmark dataset compared to state-of-the-art approaches.
Revised approach for the prediction of functional size of mobile application

Mobile applications affect our everyday activities and have become more and more information centric. Effort estimation for mobile application is an essential factor to consider in the development cycle. Due to feature complexities and size, effort estimation of mobile applications poses a continued challenge for developers. This paper attempts to adapt COSMIC Function Point and Unified Modeling Language (UML) techniques to estimate the size of a given mobile application. The COSMIC concepts capture data movements of the functional processes whereas the UML class analyzes them. We utilize the Use Case Diagrams, sequence diagrams and class diagrams for mapping the Function user requirements for sizing mobile applications. We further present a new size measurement technique; Unadjusted Mobile COSMIC Function points (UMCFP) to get the functional size of mobile application using Mobile Complex Factors as an input. In this study eight mobile applications were analyzed using UMCFP, Function Point Analysis and COSMIC Function Point. The results were compared with the actual size of previous Mobile application projects.
Eiffel: Evolutionary Flow Map for Influence Graph Visualization

The visualization of evolutionary influence graphs is important for performing many real-life tasks such as citation analysis and social influence analysis. The main challenges include how to summarize large-scale, complex, and time-evolving influence graphs, and how to design effective visual metaphors and dynamic representation methods to illustrate influence patterns over time. In this work, we present Eiffel, an integrated visual analytics system that applies triple summarizations on evolutionary influence graphs in the nodal, relational, and temporal dimensions. In numerical experiments, Eiffel summarization results outperformed those of traditional clustering algorithms with respect to the influence-flow-based objective. Moreover, a flow map representation is proposed and adapted to the case of influence graph summarization, which supports two modes of evolutionary visualization (i.e., flip-book and movie) to expedite the analysis of influence graph dynamics. We conducted two controlled user experiments to evaluate our technique on influence graph summarization and visualization respectively. We also showcased the system in the evolutionary influence analysis of two typical scenarios, the citation influence of scientific papers and the social influence of emerging online events. The evaluation results demonstrate the value of Eiffel in the visual analysis of evolutionary influence graphs.
Are Trackers Social Actors? The Role of Self-tracking on Self-evaluation

Despite the increased usage and potential benefits of self-tracking technologies for pursuing healthy lifestyles, the relationship that users have with these personal devices has remained under-studied. The current paper presents a field study to explore the perceived role of self-tracking devices as social actors. Participants received a pedometer which they carried on their person for one day. Users’ access to numerical feedback and the feeling of being tracked were manipulated, and users were interviewed afterwards regarding their experiences and their perceived social relationship to the tracker. Results of a thematic analysis indicated that in general, the feeling of being tracked led to higher self-awareness regarding participants’ walking activity. In particular, having access to agent feedback gave rise to more frequent self-evaluative reports towards one’s performance as well as a closer relationship between the device and its user. The results extend the CASA (Computers As Social Actors) paradigm by demonstrating that the capturing (and feeding back) of data can make a device be perceived as a social actor and be described in relational terms, even in the absence of clear social cues.
Insolvency modeling with generalized entropy cost function in neural networks

In this study, we analyzed empirically the customer insolvency problem in the telecommunication industry. The goal was to provide objective, data-driven, and exhaustive recommendations that can support the decision-making process of the telecom operator. In this context, we comprehensively examined a q-generalized function based on the Tsallis statistics as an alternative error measure in neural networks. Many error functions have been proposed in the literature to achieve a better predictive power of neural networks. However, there is no direct implementation of the Tsallis statistics as the error function, although it was successfully applied to other fields. Our results indicate that the proposed entropy as a cost function can be applied successfully to neural networks yielding satisfactory results. The proposed neural network models, which were derived numerically, performed well and depending on the q-parameter, could detect significantly, in top deciles, a large number of insolvent customers along with the amount due. We believe that the applicability of the proposed approach can be extended to any business, where customers can purchase goods or services on credit (without paying cash) and paying for them later.