Reviewerly/exHarmony huggingface dataset:
Mining of Probabilistic Controlling Behavior Model From Dynamic Software Execution Trace

Complex functional integration leads to intricate logical control flows which in turn presents a great challenge to construct software behavior models. In this paper, we propose a probabilistic software behavior model by mining the execution traces using control flow analysis. To describe the interactions between software components, a semantic characterization method is developed. A tracing mechanism is designed to collect execution logs, based on which algorithms are developed to recognize detailed control relations. Moreover, dynamic behavioral frequencies are statistically estimated which provide quantitative data for behavior prediction. Finally, a multi-label enhanced software complex network model, which holds single or composite call relations and calling probabilities, is constructed with the purpose of profiling systematically and structurally. An illustrative example shows that the modeling approach can discover interactive patterns correctly and characterize software behavior scientifically. The method has been used in seven real-world projects, and results show that the proposed model is effective on discovering the complexities of software behavior and hence help to detect defects in software design and to improve performance.
Non-linear multivariate models for the estimation of global solar radiation received across five cities in South Africa

South Africa continues to lag globally in the adoption of renewable energy systems despite a notable decrease in the cost of applicable renewable energy technologies over the past five years. Most applications of potential solar renewable energy systems are currently in various stages of investigation, leaving this readily accessible resource capacity idle. The present study proposes linear and non-linear analysis of multivariate models for estimating global solar radiation (GSR) received across five cities in South Africa. The significance of this study is to provide effective GSR estimation in the application of solar technologies, while increasing their implementation. The dependency of GSR on meteorological variables such as air temperature, relative humidity and relative sunshine duration was evaluated for January 2007 to June 2018 to realise estimation models for each of the study sites. The Hargreaves-Samani and Angstrom-Prescott empirical models served as the basis for single variable analysis of GSR reliance on each meteorological parameter and their relative variations. The results indicated that the proposed non-linear, multivariate equations perform better than the empirical models as well as linear, single variable regression equations. The suggested models are site-specific and demonstrate a strong correlation to historic GSR values with low, acceptable error indicators. It was also recognised that second- and third-order relationships between the clearness index and multiple meteorological variables provide a more accurate description of GSR for most of the cities under study. These methods are cost-effective, easily accessible and appropriate for the evaluation of the feasibility of solar photovoltaic technologies in South Africa.
Human-Computer Interaction in Intelligent Tutoring Systems

Due to the rapid evolution of society, citizens are constantly being pressured to obtain new skills through training. The need for qualified people has grown exponentially, which means that the resources for education/training are significantly more limited, so it’s necessary to create systems that can solved this problem. The implementation of Intelligent Tutoring Systems (ITS) can be one solution. Besides, ITS aims to enable users to acquire knowledge and develop skills in a specific field. To achieve this goal, the ITS should learn how to react to the actions and needs of the users, and this should be achieved in a non-intrusive and transparent way. In order to provide personalized and adapted system, it is necessary to know the preferences and habits of users. Thus, the ability to learn patterns of behaviour becomes an essential aspect for the successful implementation of an ITS. In this article, we present the student model of an ITS, in order to monitor the user’s biometric behaviour and their learning style during e-learning activities. In addition, a machine learning categorization model is presented that oversees student activity during the session. Additionally, this article highlights the main biometric behavioural variations for each activity, making these attributes enable the development of machine learning classifiers to predict users’ learning preferences. These results can be instrumental in improving ITS systems in e-learning environments and predict user behaviour based on their interaction with computers or other devices.
IMPACT OF SHARING INFORMATION WITH SUPPLIER AND BUYER ON THE ORGANIZATIONAL PERFORMANCE OF FOOD COMPANIES IN BOSNIA AND HERZEGOVINA

In the turbulent environment which is characterized by constant market changes and the development of informational technologies, supply chain is becoming the key instrument for competitive advantage. To survive on the market and be competitive, enterprises have to share and distribute knowledge and information. Information sharing affects the fundamental decisions of the supply chain management. Information sharing with partners is a precondition for the exchange of knowledge needed for business. Most important partners for every enterprise are their suppliers and buyers. Suppliers and buyers are key participants of the supply chain from which they get needed information. Relations with suppliers and buyers are an important precondition for the improvement of the operative performance of the enterprise. Sharing information with suppliers and buyers is done in the following way: information delivered to suppliers, information obtained from the supplier, information delivered to buyers and information obtained from buyers. To examine the impact of the supply chain used an empirical study about multi-dimensional relations. The research is focused on information sharing by food industry in Bosnia and Herzegovina (BiH). A random systematic sample was used to distribute the questionnaire to these companies. Collected data is analysed using the Confirmatory factor analysis (CFA), and the model was tested using the Structural equations model (SEM). The results showed that information sharing impacts the improvement of the organizational performance of companies, and partnerships with supplier and buyer impact on the information sharing. Results of this study showed that information sharing does not have a significant impact on the sub constructors of operative performances of the enterprise because there is no significant connection with flexibility, while for the other constructors there is a significant connection between information sharing and operative performances of the enterprise. Based on these facts, it examined the importance information sharing in the supply chain for the development of partnerships and improvement of performance this companies. The results will help managers on food industry in BiH how to improve quality of the information sharing through partnerships and how to developing operative performances companies. The model gives directions for developing business enterprises in food industry using information sharing within the supply chain. Apart from that, obtained results contributed to the better understanding of the significance of the information sharing in the food supply industry enterprises. Based on what is said, to improve the business of the enterprise, it is needed to share quality information which is possible to get through improving cooperation with key participants in the supply chain.
Indonesian English Learners’ Perception of The Implementation of Mobile Assisted Language Learning in English Class

Many Indonesian English Learners nowadays use the smartphone as their communication tools. It is used only for socializing through social media by most of them. This descriptive research aims to elaborate their perception on the implementation of MALL in English Class for University Level. The data were collected through a survey questionnaire which was adapted from the Technology Acceptance Model (TAM). The result proves that they have a positive perception on the usage of MALL.
Encouraging Paragraph Embeddings to Remember Sentence Identity Improves Classification

While paragraph embedding models are remarkably effective for downstream classification tasks, what they learn and encode into a single vector remains opaque.In this paper, we investigate a state-of-the-art paragraph embedding method proposed by Zhang et al. ( 2017) and discover that it cannot reliably tell whether a given sentence occurs in the input paragraph or not.We formulate a sentence content task to probe for this basic linguistic property and find that even a much simpler bag-of-words method has no trouble solving it.This result motivates us to replace the reconstructionbased objective of Zhang et al. ( 2017) with our sentence content probe objective in a semisupervised setting.Despite its simplicity, our objective improves over paragraph reconstruction in terms of (1) downstream classification accuracies on benchmark datasets, (2) faster training, and (3) better generalization ability. 1
A Spreading Activation Framework for Tracking Conceptual Complexity of Texts

We propose an unsupervised approach for assessing conceptual complexity of texts, based on spreading activation.Using DBpedia knowledge graph as a proxy to long-term memory, mentioned concepts become activated and trigger further activation as the text is sequentially traversed.Drawing inspiration from psycholinguistic theories of reading comprehension, we model memory processes such as semantic priming, sentence wrap-up, and forgetting.We show that our models capture various aspects of conceptual text complexity and significantly outperform current state of the art.
Deep Learning for Optical Character Recognition and Its Application to VAT Invoice Recognition

Optical character recognition (OCR) is considered as one of long-term and hot research topics due to the fact that OCR technique can change the documents from paper to computer-readable format by consistently growing. However, the recognition accuracy of current OCR technique is required to improve some special applications such as in reimbursement of value-added tax (VAT) invoices. This paper proposes two OCR techniques by using deep convolutional neural network (CNN) and residual network (ResNet), respectively. According to our test dataset, the formerly proposed techniques can reach up to 97.08%, while the latter can increase to 99.38%.
Target Identity Recognition Method based on Trusted Information Fusion

Safe and reliable target identity recognition is the important foundation of information security.In the complex environment of multisource target information, in view of the potential impact of many uncertain factors on target identity recognition and the performance requirements of information security in the process of recognition, a trusted target identity recognition method is proposed in this paper.The BP neural network based on momentum factor is used to study and build an ensemble classification model, and based on this model, the trusted target identity recognition model is constructed.According to the relevant information characterized by the model, it can improve the recognition reliability of the target to a certain extent, thus providing more security and credibility for the recognition of the identified target.Finally, the effectiveness and feasibility of the proposed algorithm is verified by simulation experiments under an uncertain set environment.
PARAMETER ADAPTATION FOR ANT COLONY SYSTEM IN WIRELESS SENSOR NETWORK

The Ant Colony System (ACS) algorithm has been applied in solving packet routing problems in Wireless Sensor Networks (WSNs).Solving these problems is complicated as packets need to be submitted through sensor nodes which are spatially distributed and heterogeneous by nature.Without an effective packet routing algorithm, energy consumption will be increased while network lifetime will be reduced.Most researches are focused on optimizing the routing process by using predefined parameters within a certain range.However, this approach will not guarantee optimal performance.This paper presents the parameter adaptation values for ACS experimental set-up in validating its performance.Possible values of each parameter within a defined range were employed.Experiments were conducted to obtain the best value of each parameter to be used for throughput, energy consumption, and latency.Results of this study can be adopted to achieve optimal performance for the packet routing process.
Multiparticle quantum walk with a gaslike interaction

We analyze the dynamics of multiparticle discrete-time quantum walk on the two-dimensional lattice, with an interaction inspired on a classical model for gas collision, called the HPP model. In this classical model, the direction of motion changes only when the particles collide head-on, preserving momentum and energy. In our quantum model, the dynamics is driven by the usual quantum-walk evolution operator if the particles are on different nodes, and is driven by the HPP rules if the particles are in the same node, linearly extended for superpositions. Using this form of evolution operator, we numerically analyze three physical quantities for the two-walker case: The probability distribution of the position of one walker, the standard deviation of the position of one walker, and the entanglement between the walkers as a function of the number of steps. The numerical analysis implies that the entanglement between the walkers as a function of the number of steps initially increases and quickly tends to a constant value, which depends on the initial condition. We compare the results obtained using the HPP interaction with the equivalent ones using the phase interaction, which is based on an evolution operator that inverts the sign of the coin operator if the walkers are in the same position.
A Robust Geometric Skin Colour Face Detection Method under Unconstrained Environment of Smartphone Database

Face detection is the primary task in building a vision-based human-computer interaction system and in special applications such as face recognition, face tracking, face identification, expression recognition and also content-based image retrieval. A potent face detection system must be able to detect faces irrespective of illuminations, shadows, cluttered backgrounds, orientation and facial expressions. In previous literature, many approaches for face detection had been proposed. However, face detection in outdoor images with uncontrolled illumination and images with complex background are still a serious problem. Hence, in this paper, we had proposed a Geometric Skin Colour (GSC) method for detecting faces accurately in real world image, under capturing conditions of both indoor and outdoor, and with a variety of illuminations and also in cluttered backgrounds. The selected method was evaluated on two different face video smartphone databases and the obtained results proved the outperformance of the proposed method under the unconstrained environment of these databases.
Deep Neural Model Inspection and Comparison via Functional Neuron Pathways

We introduce a general method for the interpretation and comparison of neural models.The method is used to factor a complex neural model into its functional components, which are comprised of sets of co-firing neurons that cut across layers of the network architecture, and which we call neural pathways.The function of these pathways can be understood by identifying correlated task level and linguistic heuristics in such a way that this knowledge acts as a lens for approximating what the network has learned to apply to its intended task.As a case study for investigating the utility of these pathways, we present an examination of pathways identified in models trained for two standard tasks, namely Named Entity Recognition and Recognizing Textual Entailment.
ReinforcementLearning: A Package to Perform Model-Free Reinforcement Learning in R

Reinforcement learning refers to a group of methods from artificial intelligence where an agent performs learning through trial and error (Sutton & Barto, 1998).It differs from supervised learning, since reinforcement learning requires no explicit labels; instead, the agent interacts continuously with its environment.That is, the agent starts in a specific state and then performs an action, based on which it transitions to a new state and, depending on the outcome, receives a reward.Different strategies (e.g.Q-learning) have been proposed to maximize the overall reward, resulting in a so-called policy, which defines the best possible action in each state.As a main advantage, reinforcement learning is applicable to situations in which the dynamics of the environment are unknown or too complex to evaluate (e.g.Mnih et al., 2015).However, there is currently no package available for performing reinforcement learning in R. As a remedy, we introduce the ReinforcementLearning R package, which allows an agent to learn the optimal behavior based on sampling experience consisting of states, actions and rewards (Pröllochs & Feuerriegel, 2017).Based on such training examples, the package allows a reinforcement learning agent to learn an optimal policy that defines the best possible action in each state.Main features of ReinforcementLearning include, but are not limited to:• Learning an optimal policy from a fixed set of a priori known transition samples • Predefined learning rules and action selection modes • A highly customizable framework for model-free reinforcement learning tasks
Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets

Imbalanced class data is a common issue faced in classification tasks. Deep Belief Networks (DBN) is a promising deep learning algorithm when learning from complex feature input. However, when handling imbalanced class data, DBN encounters low performance as other machine learning algorithms. In this paper, the genetic algorithm (GA) and bootstrap sampling are incorporated into DBN to lessen the drawbacks occurs when imbalanced class datasets are used. The performance of the proposed algorithm is compared with DBN and is evaluated using performance metrics. The results showed that there is an improvement in performance when Evolutionary DBN with bootstrap sampling is used to handle imbalanced class datasets.
Evaluating the Impact of GUI Similarity between Android Applications to Measure their Functional Similarity

Finding similar or related Android applications is a feature in popular search engines.An app's appearance is usually the first indicator of similarity.In this paper, the impact of GUI similarity for Android applications in measuring their functional similarity is evaluated.Accordingly, a number of Android applications will be analyzed to identify their resources and extract the most commonly used appearance features from each app's package kit (APK) and its xml layouts.An algorithm that automatically extracts these features is designed and developed.A sample of 50 Android apps from Google play store was chosen, and two separate experiments were performed: one using the presented method to measure appearance similarity, the second using one of the available methods to measure functional similarity, then the results were compared.Results show that there is a relationship between appearance and functional similarities, where a strong relationship exists between appearance similarity and most of the functional similarity anchors.
A Machine Learning Approach to Building a Tourism Recommendation System using Sentiment Analysis

Opinions have become extremely vital in today's "ratings" driven technological services.An android application, a toptier restaurant or any service for that matter thrives or wanes away on the reviews it gets.A good review can help attract potential users while a bad one may drive them away.Thus, it is essential to analyze these reviews to better understand the user's experience and work towards improving it.The general system that most services use today is based on star-ratings or a score out of 5 or 10.Although these serve the most basic purpose, text-based reviews allow one to understand the reason behind the ratings and are useful to both the user and the service provider to gain more insight.It is impractical for a human to go through thousands of reviews and comprehend the user's sentiment.Instead, training an algorithm to do this job is much more pragmatic and the advances in machine learning allows one to do so.This is where sentiment analysis comes in.In this paper, analysis of various machine learning algorithms like Multinomial Naïve Bayes, Random Forest Classifier and Bernoulli's Naïve Bayes has been done and their behavior has been studied.In addition, study of Convolutional Neural Networks and Recurrent Neural Networks is done to find out if deep learning algorithms perform better.Using these results, a recommendation system is built that maps an individual user's interests to the highest rated tourist places and generates a unique tour plan that is tailored to the user's needs.
Improved hybrid recommendation with user similarity for adult learners

Nowadays, adult learners like to study diverse and personal materials in e-learning of continuing education. Even though some materials are conducted by fewer learners, they can still be propagated through the whole learning community. Meanwhile, the huge online materials cannot meet learners’ requirements. It leads to most learners encountering the e-learning problems of ‘resource overload’ or ‘learning loses’, and learners giving up studying easily. So this study introduces an improved hybrid recommendation with user similarity (IHUS) for adult learners, which can generate the user's list of importance based on the greatest user similarity. In IHUS, when the online system runs from cold starting, the tags calculation is conducted. When this system achieves a stable learning community, an improved leaderRank method is conducted.
Touching! An Augmented Reality System for Unveiling Face Topography in Very Young Children

Developmental body topography, particularly of the face, is a fundamental research topic in the current decade. However, empirical investigation of this topic for very young children faces a number of difficulties related to the task requirements and technical procedures. In this study, we developed a new task to study the spatially-sensed position of facial parts in a self-face recognition task for 2.5- and 3.5-year-old children. Using the technique of augmented reality (AR) and 3D face tracking technology, we presented participants with their projected self-image on a screen, accompanied by a digital mark located on parts of their face. We prepared a cheerful visual and auditory reward on the screen when participants showed correct localization of the mark. We then tested whether they could indicate the position of the mark on their own faces and remain motivated for task repetition. To assess the efficacy of this task, 31 2.5- and 11 3.5-year-old children participated in this study. About half of the 2.5-year-olds and 80% of the 3.5-year-olds could perform more than 30 trials. Our new task, then, was to maintain young children's motivation for task repetition using the cheerful visual and auditory reward. The analysis of localization errors suggested the uniqueness of spatial knowledge of self-face in young children. The efficacy of this new task for studying the development of body image has been confirmed.