Reviewerly/exHarmony huggingface dataset:
High-Precision Luminosity Sensor for Solar Applications

In the present work, the design of a high-precision luminosity sensor (HPLS) is shown. On sunny and clear days, the design allows the sun position to be determined with 99.7% accuracy; in the presence of clouds (partly sunny or cloudy conditions), the most clear area, which represents the area with the greatest amount of direct solar radiation at the moment, is determined. In both cases (clear and cloudy sky conditions), the above is achieved through the location in real time of the maximum luminosity point (MLP) in the sky through the construction of luminosity gradients, which are obtained under the acquisition and processing of the output signals of a set of sensors constituting the HPLS. The distribution of the sensors is determined through optical and geometric analyses. The HPLS is validated through a set of experiments in which the search and signaling of the MLP were carried out on a sunny day and on days where partly sunny and cloudy conditions were present. Finally, a technical comparative analysis between the HPLS and other sensors is presented; the cost of the HPLS is $293.50 USD (obtained in a calculation performed in this work), and a cost comparison with some commercial sensors is presented as well.
Innovating in Control Engineering Teaching/Learning with Smartphones

Currently smartphones are used extensively by students. Thus, their proper use within teaching/learning activities is a reality which needs to be addressed. How teachers and students without advanced programming skills can develop their own mobile devices applications? An attempt to answer this question is presented in control engineering teaching and learning activities. It will be shown that the MIT App Inventor 2 provides an excellent tool to develop simple applications for Android based mobile devices. Examples of applications designed both by lecturers and students using the MIT App Inventor 2 are described, outlining different uses and features. A case study of using applications to support teaching/learning of PLC programming is presented.
Detection of terrorist threats on Twitter using SVM

Many people are exposed daily to different forms of terrorist threats on social networks, which make the control and detection of these contents paramount. We propose in this article, a system of detection of publications related to terrorism in the social network Twitter based on SVM (Simple Vector Machine). We established a 12-step process for analyzing, processing, and then detecting threatening tweets. We have developed 2 scenarios of use of this process, in the first scenario, we apply learning on 4000 tweets written in English, then 4000 tweets written in Arabic and finally 4000 tweets written in bilingual (in both languages). In the 2nd scenario, we apply learning on all the tweets at once (12000 tweets). These two scenarios allow us to compare the results obtained using SVM with results obtained using a syntactical approach.
Comparative Analysis of Statistical Classifiers for Predicting News Popularity on Social Web

In recent years, owing mainly to the ubiquity of the Internet, social media platforms are more popular than ever before. Facebook boasts of over 2 billion registered accounts and the number of individual users is said to be more than the population of most countries. It is clear that social media has our attention, and media houses are no strangers to this fact. A huge amount of time and resource is put into the research and development of strategies that will help news flash become more popular. One of the major driving factors of news popularity is the sentiment and emotion behind the news. Human emotions are the driving force of any microblog on social media today and in our research, we attempt to study some of these fields that affect the mood of people. These features include specific properties about the news, such as the sentiment and the topic of the news itself. They further include factors unrelated to the news articles that may affect the news reading behavior of readers, like the day of week or time of the day. Our research provides an approach to design a predictive model for the popularity of a news article on a particular social media platform, based on the input features.
Based on Artificial Intelligence and Deep Learning Hand Gesture Recognition for Interaction with Mobile Robots

The proliferation of mobile robots in a wide range of professional and home applications is a real stimulus of researches for more convenient natural ways and communication skills of mobile robots to interact with human. Voice communicating with mobile robots is always preferable but in many cases is inappropriate due to technical complexity, not enough accuracy and inability of real time of voice recognition. That is why voice communications with mobile robots are often replaced or combined with human interaction by hand gestures. Therefore, this is the goal of this article, with proposition of realization based on artificial intelligence and deep learning.
Multi-mode Control System of an Unmanned Vessel with Fuzzy Hybridization of Controllers

The paper considers the control system of the unmanned vessel in different modes of motion. The mathematical model of the vessel in the plane is described. Three modes are considered: terminal motion to a defined point, positioning at a defined point, and obstacle avoidance. A controller that realizes the set modes of motion was synthesized basing on the method of position-trajectory control. The transition from one mode of motion to another is realized by changing the algorithm of reference calculation. In the terminal motion mode, the desired speed is calculated by the set motion time and distance to the destination point. In the positioning mode, a transition to the calculation of the required speed is made based on information about the distance to the desired position of the vessel. To avoid an obstacle, a component generated by an additional differential equation, which solution is stable far from the obstacle, and unstable in the obstacle area, is added to the desired orientation angle of the vessel. For conjugation of various motion modes, fuzzy logic is used, which allows smoothing the transient modes and eliminating the singularity in the terminal control law. The asymptotic stability of the desired equilibrium state of a multimode control system with fuzzy logic is proved. The results of simulation and experiments using a mini-boat are considered.
Deterministic Channel Design for Minimum Leakage

This work explores the problem of designing a channel that leaks the least amount of information while respecting a set of operational constraints. This paper focuses on deterministic channels and deterministic solutions. This setting is relevant because most programs and many channel design problems are naturally modelled by deterministic channels. Moreover, the setting is also relevant when considering an attacker who can observe many outputs of an arbitrary channel while the secret input stays the same: when the number of observations is arbitrarily large, the channel of minimal leakage is deterministic. The deterministic channel design problem has different solutions depending on which leakage measure is chosen. The problem is shown to be NP-hard in general. However, for a particular class of constraints, called k-complete hypergraph constraints, a greedy algorithm is shown to provide the optimal solution for a wide class of leakage measures.
Ordering of Words by the Spoken Word Recognition Time

We measured the time needed to recognize spoken words in a group of 12 subjects. We see that recognition time varies for different words with the same sound duration and they can be ordered from the word perceived most quickly to the “slowest” one. Every subject “generates” his own ordered list of 24 words used. The individual lists are similar to some extent, so that the robust average list can be compiled. Presumably, it reflects distribution of the word representations in the cortex and the time required to retrieve any word depends on its position.
A novel obstacle avoidance method based on multi-information inflation map

Purpose This paper aims to provide a novel obstacle avoidance method based on multi-information inflation map. Design/methodology/approach In this paper, the multi-information inflation map is introduced, which considers different information, including a two-dimensional grid map and a variety of sensor information. The static layer of the map is pre-processed at first. Then sensor inputs are added in different semantic layers. The processed information in semantic layers is used to update the static layer. The obstacle avoidance algorithm based on the multi-information inflation map is able to generate different avoidance paths for different kinds of obstacles, and the motion planning based on multi-information inflation map can track the global path and drive the robot. Findings The proposed method was implemented on a self-made mobile robot. Four experiments are conducted to verify the advantages of the proposed method. The first experiment is to demonstrate the advantages of the multi-information inflation map over the layered cost map. The second and third experiments verify the effectiveness of the obstacle avoidance path generation and motion planning. The fourth experiment comprehensively verifies that the obstacle avoidance algorithm is able to deal with different kinds of obstacles. Originality/value The multi-information inflation map proposed in this paper has better performance than the layered cost maps. As the static layer is pre-processed, the computational efficiency is higher. Sensor information is added in semantic layers with different cost attenuation coefficients. All layers are reset before next update. Therefore, the previous state will not affect the current situation. The obstacle avoidance and motion planning algorithm based on the multi-information inflation map can generate different paths for different obstacles and drive a robot safely and control the velocity according to different conditions.
Explaining Completions Produced by Embeddings of Knowledge Graphs

Advanced question answering typically employs large-scale knowledge bases such as DBpedia or Freebase, and are often based on mappings from entities to real-valued vectors. These mappings, called embeddings, are accurate but very hard to explain to a human subject. Although interpretability has become a central concern in machine learning, the literature so far has focused on non-relational classifiers (such as deep neural networks); embeddings, however, require a whole range of different approaches. In this paper, we describe a combination of symbolic and quantitative processes that explain, using sequences of predicates, completions generated by embeddings.
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Identifying the correct meaning of words in context or discovering new word senses is particularly useful for several tasks such as question answering, information extraction, information retrieval, and text summarization. However, specially in the context of user-generated contents and on-line communication (e.g. Twitter), new meanings are continuously crafted by speakers as the result of existing words being used in novel contexts. Consequently, lexical semantics inventories and systems have difficulties to cope with semantic drifting problems. In this work, we propose an approach to induce and disambiguate word senses of some target words in collections of short texts, such as tweets, through the use of fuzzy lexico-semantic patterns that we define as sequences of Morpho-semantic Components (MSC). We learn these patterns, that we call MSC+ patterns, from text data automatically. Experimental results show that instances of some MSC+ patterns arise in a number of tweets, but sometimes using different words to convey the sense of the respective MSC in some tweets where pattern instances appear. The exploitation of MSC+ patterns when they induce semantics on target words enable effective word sense disambiguation mechanisms leading to improvements in the state of the art.
Integrated Power Management with Automated Attendance System

Optimal energy management in smart spaces requires intelligent and reliable energy-aware-based context sensing and technologies that are capable of recognizing and analyzing the power consumption. In this system, we propose a efficient and reliable power management system with inclusion of automated attendance. The main objective is to automatically control the fan and lights inside any space involving number of people. The next objective is automated attendance. ID cards containing barcode data is sufficient for their identity, show case their real time data, their usage additional fine system for being late. Priority of this work, is made to ensure that this system is affordable to all with assurance of data integrity and authority.
New Organisational Models That Break Silos in Organisations to Enable Software Delivery Flow

Obstacles exist that prevent organisations from continually evolving and achieving software delivery efficiency. One such obstacle is organisational silos and, because of their existence, corporates fail to fully integrate their software development activities to enable multiple teams to jointly develop, test, implement and maintain new applications more effectively and efficiently. This study aims to investigate how organisational silos can be broken down to enable software delivery flow. For organisations looking to eliminate silos through the adoption of agile-at-scale, this study recommends the adoption of practices and principles from existing bodies of knowledge, namely Lean, Agile and Systems Thinking that incorporate mechanisms such as cross-functional teams, gemba walks and communities of practise that are associated with end-to-end flow. Such mechanisms are normally not confined by formal structures and thus they are inclined to establish relationships across functional units and teams, while also eradicating “siloed” activity.
Automatic generation of tower defense levels using PCG

Tower defense is a popular subgenre of real-time strategy game requiring detailed level design and difficulty balancing to create an enjoyable player experience. Because level production and testing are both time-consuming and labor-intensive, we propose and implement a framework to automate the process. We first analyze the three main components, or "building blocks", of the popular tower defense game Kingdom Rush: Frontiers (KRF), i.e. road maps, tower locations and monster sequences. We then automatically create new building blocks in the style of the original game, utilizing techniques from Procedural Content Generation (PCG), and assemble them to create new levels. We also add a fourth block: automated testing via a Monte Carlo search algorithm, to ensure the generated content is playable. We focus on KRF because it is a popular video game in the tower defense genre, and highlights some of the challenges of designing appropriate PCG and playtesting algorithms for a commercial video game.
Endure: Augmented Reality Fitness Mobile Application

An alarming fact that surprisingly not many locals are aware of is that Malaysia has been labelled as Asia's fattest country in the year 2015 up until now. With abundance of food comes immense responsibility to take care of our health, however that is not the case for Malaysians. Based on the survey distributed, not many integrate exercising as a part of their weekly routine as they prioritize their work/study over a 20-minute workout session. The repercussions of this unhealthy habit are severe, and may cause a global pandemic of obese-stricken populations in the future. With that being said, `Endure' is an application proposed to aid in mitigating the issue mentioned. It is a fitness application that incorporates a gaming environment enabling the user to be the first-person player in a horror-themed game. Using immersive technology such as Augmented Reality (AR), users are able to play this chapter-by-chapter application whilst outdoors or indoors. Endure encourages users to challenge themselves by increasing the distance for each level while limiting the time required to complete each one of them. With the aid of an AR game object, users will be pushed to keep their virtual-selves alive by increasing their pace with regards to the countdown timer. In short, the application is developed to motivate users to keep their feet moving by creating a virtually fun environment for the players.
Application of Mamdani and Sugeno Fuzzy Toward Ready-Mix Concrete Quality Control

Abstract The value of concrete compressive strength based on several methods of acceptance requirements of concrete in Indonesia to obtain a more economical concrete quality is recommended using SNI 03-2847-2002 rules as a reference in the evaluation and quality control of concrete. Fuzzy logic is different from ordinary digital logic, where the usual digital logic only recognizes two condition that is “yes” or “no”. While Fuzzy logic imitate the way of human thinking by using the concept of the pseudonym of a value. the Mamdani type and Sugeno type is resulted in the same decision, although there was a difference in the calculation of the final result but still within the same range of manual calculations with the calculation with the help of MATLAB. Both fuzzy can be applied in determining the quality of concrete because it produces the same decision. Although both fuzzy method can be used in determining the quality but the more recommended is the Mamdani type because Mamdani type result of calculation is closer to the actual result, and while there is input change is entered then the end result will change but Sugeno type while input change value is not too big the change then the end result also there will be no change.
On generating random Gaussian graphical models

Structure learning methods for covariance and concentration graphs are often validated on synthetic models, usually obtained by randomly generating: (i) an undirected graph, and (ii) a compatible symmetric positive definite (SPD) matrix. In order to ensure positive definiteness in (ii), a dominant diagonal is usually imposed. In this work we investigate different methods to generate random symmetric positive definite matrices with undirected graphical constraints. We show that if the graph is chordal it is possible to sample uniformly from the set of correlation matrices compatible with the graph, while for general undirected graphs we rely on a partial orthogonalization method.
Exploring Textual Features for Multi-label Classification of Portuguese Film Synopses

The multi-label classification of film genres by using features extracted from their synopses has recently gained some attention from the scientific community, however, the number of studies is still limited. These studies are even scarcer for languages other than English. In this work we present the P-TMDb dataset, which contains 13, 394 Portuguese film synopses, and explore the film genre classification by experimenting with nine different groups of textual features and four multi-label algorithms. As our dataset is unbalanced, we also conducted experiments with an oversampled version of the dataset. The best result obtained for the original dataset was achieved by a TF-IDF based classifier, presenting an average F1 score of 0.478, while the best result for the oversampled dataset was achieved by a combination of several feature groups and presented an average F1 score of 0.611.
Context-Aware Indexing and Retrieval for Cognitive Systems Using SOEKS and DDNA

Visual content searching, browsing and retrieval tools have been a focus area of interest as they are required by systems from many different domains. Context-based, Content-Based, and Semantic-based are different approaches utilized for indexing/retrieving, but have their drawbacks when applied to systems that aim to mimic the human capabilities. Such systems, also known as Cognitive Systems, are still limited in terms of processing different sources of information (especially when structured in different ways) for decision making purposes. This issue becomes significantly greater when past information is retrieved and taken in account. We address this issue by proposing a Structuralized Context-Aware Indexing and Retrieval using Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA). SOEKS and DDNA allow the creation of a multi-modal space composed of information from different sources, such as contextual, visual, auditory etc., in a form of a structure and explicit experiential knowledge. SOKES is composed by fields that allow this experiences to participate in the processes of similarity, uncertainty, impreciseness, or incompleteness measures and facilitate the indexing and retrieval of knowledge in Cognitive Systems.
On utilizing weak estimators to achieve the online classification of data streams

Classification, typically, deals with unique and distinct training and testing phases. This paper pioneers the concept when these phases are not so clearly well-defined. More specifically, we consider the case where the testing patterns can subsequently be considered as training patterns. The paradigm is further complicated because we assume that the class-conditional distributions of the features/classes are non-stationary, as in the case of most real-world applications. Specifically, we consider the model where the training phase is non-stationary and that it is, further, interleaved with the testing, and where it can be done online and in a real-time manner. We propose a novel online classifier for complex data streams which are generated from non-stationary stochastic properties. Instead of using a single training model with “counters” that maintain important data statistics, our online classifier scheme provides a real-time self-adjusting learning model. The learning model utilizes the multiplication-based update algorithm of the Stochastic Learning Weak Estimator (SLWE) at each time instant as a new labeled instance arrives. In this way, the data statistics are updated every time a new element is seen, without requiring that we have to rebuild the model when changes occur in the data distributions. Finally, and most importantly, the model operates with the understanding that the correct classes of previously-classified patterns become available at a later juncture subsequent to some time instances. This forces us to update the training set, the training model and the class conditional distributions as the testing proceeds. The results from rigorous empirical analysis on two-dimensional/multi-dimensional and binomial/multinomial distributions are remarkable. We also report some results on two real-life datasets adapted to this model of computation, demonstrating the advantages of the novel scheme for both binomial and multinomial non-stationary distributions.