Miracle-dz/UniFAR-FTUs huggingface dataset:
Envy – an Unwanted, yet Unavoidable and Necessary Emotion

Envy is a word signifying an emotion that arises from social comparison and signals that one is outperformed by another person, or group of people, which makes one feel painful inferiority. It originates from the Latin "invidere" which means to "gaze maliciously". Yet, this emotion does not want to be communicated. It is hard to identify or admit it, partly because of a range of other negative emotions, such as hostility, resentment and rejection that accompany envious feelings. Envy may also trigger admiration that motivates one to "do better". However, subjective, historical and philosophical accounts overwhelmingly speak of its negative consequences. Not surprisingly, envy is regarded as a social taboo. Even the traditional scientific community considers envy as a "second class" emotion. It is said that envy does not meet the criteria for basic emotions. Only recently, with the paradigmatic shift in modern evolutionary psychology, a new framework was offered and started shedding light onto the emotion nobody likes to feel, think or talk about. This paper presents a review of theoretical considerations, predictions and empirical results, emerging from a relatively small amount of studies that incited a change in the conceptualization of this unwanted and seemingly inept emotion.
C3TO : a scalable architecture for mobile chat based tutoring

C3TO (Chatter Call Centre/Tutoring Online) is a scalable architecture to support mobile online tutoring using chat protocols over cell phones. It is the scalability of this architecture which is the primary focus of this dissertation. Much has been written lamenting the state of mathematics education in South Africa. It is not a pretty story. In order to help solve this mathematical crisis, the “Dr Math” research project was started in January, 2007. “Dr Math” strove to assist school pupils with their mathematics homework by providing access to tutors from a nearby university to help them. The school pupils used MXit on their cell phones and the tutors used normal computer workstations. The original “Dr Math” research project expected no more than twenty to thirty school pupils to participate. Unexpectedly thousands of school pupils started asking “Dr Math” to assist them with their mathematics homework. The original software could not scale. The original software could not cater for the thousands of pupils needing help. The scalability problems which existed in the original “Dr Math” project included: hardware scalability issues, software scalability problems, lack of physical office space for tutors, and tutor time being wasted by trivial questions. C3TO tackled these scalability concerns using an innovative three level approach by implementing a technological feature level, a tactical feature level, and a strategic feature level in the C3TO architecture. The technological level included specific components, utilities, and platforms which promoted scalability. The technological level provided the basic building blocks with which to construct a scalable architecture. The tactical level arranged the basic building blocks of the technological level into a scalable architecture. The tactical level provided short term solutions to scalability concerns by providing easy configurability and decision making. The strategic level attempted to answer the pupils questions before they actually arrived at the tutor thereby reducing the load on the human tutors. C3TO was extensively tested and evaluated. C3TO supported thousands of school pupils with their mathematics homework over a period of ten months. C3TO was used to support a small conference. C3TO was used to encourage people to volunteer their time in participation of Mandela Day. C3TO was used to support “Winter School” during the winter school holiday. In all these cases, C3TO proved itself to be scalable.
Bayesian Inference of Network Epidemics

Infectious diseases are studied to understand their spreading mechanisms, to evaluate control strategies and to predict the risk and course of future outbreaks. Because people only interact with a small number of individuals, and because the structure of these interactions matters for spreading processes, the pairwise relationships between individuals in a population can be usefully represented by a network. Although the underlying processes of transmission are different, the network approach can be used to study the spread of pathogens in a contact network or the spread of rumors in an online social network. We study simulated simple and complex epidemics on synthetic networks and on two empirical networks, a social / contact network in an Indian village and an online social network in the U.S. Our goal is to learn simultaneously about the spreading process parameters and the source node (first infected node) of the epidemic, given a fixed and known network structure, and observations about state of nodes at several points in time. Our inference scheme is based on approximate Bayesian computation (ABC), an inference technique for complex models with likelihood functions that are either expensive to evaluate or analytically intractable. ABC enables us to adopt a Bayesian approach to the problem despite the posterior distribution being very complex. The proposed methodology generally performs well and, somewhat counter-intuitively, the inference problem appears to be easier on more heterogeneous network topologies, which enhances its future applicability to real-world settings where few networks have homogeneous topologies.
Evaluation of Internet of Things Solutions which Includes Cloud Analytic Features

Internet of Things (IoT) is one of the most popular terms in the field of information technology in today’s world. This topic has drawn the concentration of many researchers and leading IT companies. Many companies and researchers are continually working on it to bring out sound solution. However, Many people heard about IoT and its potential benefit, but they may not have idea how to work on it because it is an emerging technological revolution. The main objective of this thesis is to provide overview of various IoT platforms and solid implementation guidelines for people so that they can choose right IoT platform and implement their own solution. To achieve this goal three different approaches will be used in this thesis. They are literature review, platform testing and simulation of real environment. The literature review approach will give general overview of 15 platforms and their features. The platform testing approach will provide details step by step description to implement your project from application creation to device registration, data collection, live visualization and realtime reaction (trigger event). It helps to create an idea to everyone to develop their own solution. Last but not the least, through the simulation of real environment approach, everyone can get the idea about how multiple sensors interact with each other. This thesis provide the valuable information on various IoT platforms and how to implementation on it. From the analysis of fifteen platforms, it is came to know that live visualization, real-time reaction and 24x7 hours of services are major features of all platforms. The real implementation on Bluemix and other platforms give wider view on how to create application, register device, support real-time visualization and reaction. In short, this thesis provides way to develop your own solution.
An Analysis of the English Pre-class Language

The English pre-class language, as one of the educational languages, has a direct impact on realization of the objectives of a class. This paper gives a detailed analysis of the value of the English pre-class language, its related theoretical proof as well as its main characteristics, which proves the significance of the pre-class language.
High Performance Multistring Converter Topology for Three-Phase Grid Tied 200 kW Photovoltaic Generating System

Under various external conditions, grid connected PV system performance is strongly affected by the topology that is used to connect a PV system with grid. This research aims to design a multistring based converter topology for three-phase grid connected 200 kW PV system that has a high performance in various operating conditions. Research was done by a simulation method using Matlab-Simulink with performance being evaluated including the generated power, efficiency, power quality in accordance with grid requirements, as well as the power flow. In the simulation, multistring converter topology was designed using two dc-dc boost multistring converters connected in parallel to a centralized of three-phase three-level NPC inverter with the size of the string being shorter and more parallel strings as well as the maximum voltage of the PV array of 273.5 V close to dc voltage reference of 500 V. Each dc-dc boost multistring converter have individual MPPT controllers. The simulation results showed that this multistring converter topology had a high performance in various operating conditions. This due to more power generated by the NPC inverter (> 190 kW) at the time of high power generation on the STC conditions (1000 W/m2, 25 oC), the lowest efficiency of the total system is 95.08 % and the highest efficiency of the total system is 99.4 %, the quality of the power generated in accordance with the requirements of grid, as well as the inverter put more active power to the grid and less reactive power to the grid. The response of the inverter slightly worse for loads with greater reactive power and unbalanced.
Secured Storage for Cloud Based Relational Database Management Systems

The Cloud database system is a new trend which anticipated to reshape all developments in computer technology. The data outsourced may be confidential which results in encryption of the documents. The proposed system applies encryption techniques for storage with security. This system considers structured data for storage as it would be easier for them extract information from the raw data. Moreover, data mining would be easier in structured data. The structured data are often sensitive and hence it is to be in a protected form. In order to ensure security, the proposed model provides a mechanism which increases the level of security based on are password protection and are encryption. It is achieved through blind storage which is a mechanism of concealing the access pattern and the storage pattern is also concealed by applying the concept of blind storage over relational databases in cloud.
Making 1:n explorable: a search interface for the ZAS database of clause-embedding predicates

We introduce a recently published corpus-based database of German clause-embedding predicates and present an innovative web application for exploring it. The application displays the predicates and the corpus examples for these predicates in two separate tables that can be browsed and searched in real time. While familiar web interface paradigms make it easy for users to get started, the data presentation and the interactive advanced search components for the two tables are designed to accomodate remarkably complex query needs without the need for resorting to a dedicated query language or a more specialized tool. The 1:n relationship between predicates and their examples is exploited in the two tables in that, e.g. the predicate table also shows, for each predicate and each example attribute, all values that occur in the examples for this predicate. An easy-to-use visual query builder for arbitrary boolean combinations of search criteria can optionally be displayed to pre-filter the underlying data presented in both tables. Several options for altering quantifier scope can be activated with simple checkboxes and considerably widen the space of searchable constellations
Vehicle Inter-dependent Control Transmission Architectures - VICTA

This paper proposes a novel hybridized vehicle inter-dependent control transmission architecture (VICTA) that offers key advantages over existing systems. It embodies a compact torque generation system formed by two or more electric machines axially linked through a conventional mechanical differential gear assembly by means of a simple yet innovative pair of co-axial shafts. The resultant electric-mechanical hybridized drivetrain system allows a unique and automatic way of transmitting, differentially and inter-dependently, the electromagnetic torques generated by the motors at either side of differential gear. A realistic virtual prototype of the VICTA based on MATLAB/Simulink modules is developed to validate the concept. Comprehensive performance prediction tests, including steering maneuvering and variations of the road surface, are undertaken. Excellent simulation results from the VICTA virtual prototype demonstrate its stability and motility performance. The results also confirm the VICTA concept has key advantages such as reliability, modularity, high efficiency and simplicity, and yet does not inherit the undesirable features of high unsprung mass or complex software control that continue to challenge and even flaw the existing systems.
Complexities and diversity in archaeologies of Island Melanesia

It has been over 20 years since Spriggs’ (1997) synthesis The Island Melanesians was published. In the ensuing two decades, a massive and accelerating amount of archaeological fieldwork, analysis, and publication has taken place in the region (see Kirch 2017:55–183 for a recently updated synthesis). There have since been a number of synthetic works highlighting the archaeology of specific archipelagos in Island Melanesia, including Vanuatu (Bedford 2006; Bedford and Spriggs 2014), the Solomon Islands (Walter and Sheppard 2017) and Fiji (Burley 2013; Clark and Anderson 2009). There have also been periodic volumes covering the archaeology of the Lapita Cultural Complex, which is a phenomenon primarily located in Island Melanesia, though also in the neighbouring Polynesian archipelagos of Tonga and to a lesser extent Samoa (e.g. Bedford et al. 2007; Sand and Bedford 2010; Sand et al. 2015). As more sites are discovered, the notion of what Lapita is, where it is located and how to interpret possibly related findings remains a matter of vibrant debate.
Modelo resiliente de redes de distribución considerando flujos de potencia basado en teoría de grafos

El concepto de resiliencia en redes de distribución es un tema emergente como una solución factible para afrontar condiciones climáticas extremas, debido a su naturaleza estocástica y su alto grado de afectación a la estructura topológica de la red, como factores claves dentro de la estrategia de desarrollo de energía sostenible. Por lo tanto, este documento ofrece un enrutamiento y despliegue resiliente de la red de distribución, basado en un modelo matemático meta-heurístico, para el óptimo emplazamiento de los trasformadores y, mínimum spanning tree (MST) basado en la variante del algoritmo PRIM para encontrar el óptimo enrutamiento del sistema de distribución antes, durante y después de un evento catastrófico extremo, minimizando la distancia asociada a cada vano como función objetivo. Cabe destacar que el modelo se probó en un escenario real georreferenciado con cargas estáticas usando el sistema algebraico MATLAB. Para solventar la factibilidad eléctrica el flujo de potencia se simuló con el software Cymdist, tomando en cuenta las distancias de las cargas.
Initial study of a pyramidal sinuous antenna as a feed for the ska reflector system in band-1

A sinuous antenna, projected onto a pyramidal structure, is presented as a possible wide band single pixel feed candidate for the Square Kilometre Array reflector system. Simulated results indicate a significant trade-off between reflection coefficient and receiving sensitivity performance, while optimal performance is similar to the other candidate antennas. This antenna might present an advantage in terms of size, but significant challenges remain in the design of a wide band and low loss feeding network.
A Capacitor-Splitting Switching Scheme with Low Total Power Consumption for SAR ADCs

A low-power capacitor-splitting switching algorithm for successive approximation register (SAR) and analog-to-digital converters (ADCs) is proposed. To reduce the total power consumption, it does not require reset energy, which accounts for a large proportion. Besides, energy-efficient one-side double-level switching technique is also utilized from the forth bit cycle. Thus, the proposed switching algorithm requires 26.54 CV[Formula: see text] total switching energy, 16.75% less over the tri-level one. Due to the capacitor-splitting structure, it also shows good linearity performance.
Performances of Machine Learning Algorithms for Binary Classification of Network Anomaly Detection System

The rapid growth of technologies might endanger them to various network attacks due to the nature of data which are frequently exchange their data through Internet and large-scale data that need to be handle. Moreover, network anomaly detection using machine learning faced difficulty when dealing the involvement of dataset where the number of labelled network dataset is very few in public and this caused many researchers keep used the most commonly network dataset (KDDCup99) which is not relevant to employ the machine learning (ML) algorithms for a classification. Several issues regarding these available labelled network datasets are discussed in this paper. The aim of this paper to build a network anomaly detection system using machine learning algorithms that are efficient, effective and fast processing. The finding showed that AODE algorithm is performed well in term of accuracy and processing time for binary classification towards UNSW-NB15 dataset.
Emotion recognition using facial expression and electroencephalography features with support vector machine classifier

Recognizing emotions from facial expression and electroencephalography (EEG) emotion signals are complicated tasks that require substantial issues to be solved in order to achieve higher performance of the classi ﬁ cations, i.e. facial expression has to deal with features, features dimensionality, and classi ﬁ cation processing time, while EEG emotion recognition has the concerned with features, number of channels and sub band frequency, and also non-stationary behaviour of EEG signals. This thesis addresses the aforementioned challenges. First, a feature for facial expression recognition using a combination of Viola-Jones al-gorithm and improved Histogram of Oriented Gradients (HOG) descriptor termed Edge-HOG or E–HOG is proposed which has the advantage of insensitivity to lighting conditions. The issue of dimensionality and classi ﬁ cation processing time was resolved using a combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which has successfully reduced both the dimension and the classi ﬁ cation processing time resulting in a new low dimension of feature called Reduced E–HOG (RED E–HOG). In
The use of gamification in xMOOCs about energy: Effects and predictive models for participants’ learning

Open access education has significantly grown in strength as a new way of fostering innovation in schools. Such is the case of massive open online courses (MOOCs), which have the added benefit of encouraging the democratisation of learning. In this sense, the BiNational Laboratory on Smart Sustainable Energy Management and Technology Training between Mexico and the United States of America was launched with the purpose of trying MOOC technology and measuring its impact on the academic, business, and social sectors. Under this scenario, this study aimed to show the relationship between using gamification and level of performance in a MOOC on energy topics. The methodology was quantitative, using the course analytical data for socio-demographic information and predictive models. A total of 6246 participants enrolled in the MOOC and 1060 finished it. The results showed that participants aged between 20 and 50 had the highest completion rates in the gamified challenge; the higher academic degree, the more inclined participants were to solve the gamified challenge; and no such distinction exists by gender.
Abstraction over Public Interfaces

The use of public interfaces as a means of encapsulating method implementations has become standard in software design, but still requires the development of appropriate verification techniques. In this paper we study the consequences on verification of dividing methods into public and private ones. We introduce a notion of interface behaviour of applets which abstracts from the internal, private behaviour, and we propose an abstraction technique, based on inlining of private methods, to verify properties of this interface behaviour. We define the inlining transformation and prove that it preserves the properties expressible in our simulation logic. Our abstraction technique is particularly suitable for compositional verification, since it allows global system properties to be inferred from the interface properties of the components that are not yet available. In addition, we show on a concrete case study how the reduction in the number of methods resulting from the inlining transformation drastically improves the performance of the computationally most expensive step in the compositional verification technique previously developed by the authors.
An Indoor Shop Localization and Navigation System for Malls Using Bluetooth Beacons

: Indoor navigation and location-awareness are important and growing research areas due to the interest of mobile users in location-based services [3]. The location sensing methods such as Global Positioning System or Inertial Measurement Unit may not be feasible to use Indoor [2]. We preferred Bluetooth device for indoor navigation which have advantage of being low cost and being widely available. We Implemented Navigation System using java, this implementation is adaptable to whatever indoor environment (Mall, Colleges, Commercial center, Offices, Museums etc.). In this System we also find the Shortest Path by using Dijkstra’s Algorithm.
Recommender Systems in ECommerce

E-commerce is growing rapidly offering a vast number of products and services to the users. Facing with a wide range of options, users cannot decide which one would be the most suitable option. Recommender systems help users to find the most suitable item easier and faster. To do this, recommender systems apply machine learning algorithms to user’s data to build sophisticated models to predict the user’s behavior in the future. There are many recommender systems employed by companies to increase their profitability. Some examples include Amazon, Movielens, Youtube, Facebook, and Linkedin. This presentation details the implementation of a cluster based recommender system that can accurately recommend items to users.
Research on Focus Content Tracking Method Based on Online Message Evolutionary

Aiming to the data flow continued to flow into the system in the process of training, it leads to the subject of each paragraph with constant change. This paper propose a topic tracking algorithm for online message delivery. And the paper analyzes the distribution feature of the topic focus in the text, and introduces the main steps and the algorithm model of the focus recognition method based on the analysis results. The text content comes from the real data of the real network, and the method is verified by experiments, the experimental results show that the proposed method can effectively obtain the focus of the topic development process, and can be expressed in the form of key words set and statement set.
Research on Super-resolution Reconstruction Optimization Method Based on Deep Learning

In order to reconstruct low-resolution images into high-resolution images and compensate for the loss of information between high-resolution images and low-resolution images, this paper presents an effective super-resolution reconstruction optimization method, which analyzes the advantages and disadvantages of existing super-resolution constructing methods, and throws out problems that need to be resolved, and designs reasonable solutions.