Miracle-dz/UniFAR-FTUs huggingface dataset:
In depth analysis of Long-Short-Term-Memory Neural Networks with the purpose of detecting cyberbullying

Social media usage has skyrocketed the last decades. Along with all the upsides of social media, there are also a few downsides. One of the most problematic downsides has been the rise of cyberbullying. New statistics shows that this problem has kept on escalating in the recent years. The problem with detecting and preventing cyberbullying is that social media generates so much data that it is virtually impossible to detect cyberbullying by performing manual inspections. This has motivated researchers to come up with new automated methods of detecting cyberbullying. In the most recent years, it has become popular among researchers to utilize a type of artificial Neural Network called Long-Short-Term-Memory Neural Network. In this thesis we present the empirical evaluation of different types of artificial Neural Network to see how the network can be designed to maximize its performance with regards to detecting cyberbullying. To find the best design of a Long-Short-Term-Memory Neural Network we conduct two experiments, testing 56 different designs. These experiments have been designed as factorial experiments, meaning that a set of variables has been modified in combination with each other. This lets us track any cause and effect relationships between the different variables in a much more accurate way, than what is possible by studying the existing research within the field. In order to make the results of this study as relevant as possible to the state-of-the-art cyberbullying research, we base the whole Natural Language Processing pipeline that leads up to the artificial Neural Network on the existing state-of-the-art research. We also explain this Natural Language Processing pipeline in depth, such that it can be recreated, and used for continuous study of other variables that are not tested by us, in future research projects. Four main variables are tested in this thesis; composition of different layer types, stacking of equal layer types, training epochs and activation mechanisms for the classes. Our findings show that the best composition of layer types are found by combining a traditional Convolutional Neural Network with a Long-Short-Term-Memory Neural Network. We also find that all of the Neural Networks, no matter what layer composition, benefit from going from no stacking to stacking its layers twice. As expected, we also find that Neural Networks perform better when properly trained, but there is no correlation between how much training a model needs and how well it performs. Lastly, we prove that there is no reason to blindly choose Softmax as the activation for the classes in the Neural Network models, as most of the researchers tend to do. Our findings show that using an activation mechanism that mimics the mechanisms of a Support-Vector-Machines classifier outperforms the Softmax activation, with all four different layer compositions of the Neural Network that we tested.
Joint Multi-User Computation Offloading and Data Caching for Hybrid Mobile Cloud/Edge Computing

In this paper, we investigate a hybrid mobile cloud/edge computing system with coexistence of centralized cloud and mobile edge computing, which enables computation offloading and data caching to improve the performance of users. Computation offloading and data caching decisions are jointly optimized to minimize the total execution delay at the mobile user side, while satisfying the constrains in terms of the maximum tolerable energy consumption of each user, the computation capability of each MEC server, and the cache capacity of each access point (AP). The formulated problem is non-convex and challenging because of the highly coupled decision variables. To address such an untractable problem, we first transform the original problem into an equivalent convex one by McCormick envelopes and introducing auxiliary variables. To the end, we propose a distributed algorithm based on the alternating direction method of multipliers (ADMM), which can achieve near optimal computation offloading and data caching decisions. The proposed algorithm has lower computational complexity compared to the centralized algorithm. Simulation results are presented to verify that the proposed algorithm can effectively reduce computing delay for end users while ensuring the performance of each user.
Robust Resource Allocation and Power Splitting in SWIPT Enabled Heterogeneous Networks: A Robust Minimax Approach

Heterogeneous network (HetNet) with energy harvesting is a promising technique to provide perpetual power supplies and ubiquitous coverage as well as high data rate for next-generation wireless communications. In this article, we consider a robust power allocation and power splitting (PS) problem for downlink simultaneous wireless information and power transfer (SWIPT)-enabled HetNets. The robust energy-efficiency (EE) maximization problem of femtocell users (FUs) is formulated under the outage-probability interference power constraint of macrocell user (MU), the maximum allowable transmission power of FU, and the EE-based outage constraint of each FU. The originally fractional optimization problem with the probabilistic constraint is NP-hard and difficult to solve. Without knowing the distribution of uncertain parameters, a min–max probability machine approach is first introduced to convert the semi-infinite optimization problem into a deterministic one which is transformed into a deterministic convex one by using the Dinkelbach method and the quadratic transformation approach. An iterative power allocation and PS scheme is obtained based on convex optimization methods. Finally, the effectiveness of the proposed algorithm is demonstrated by simulation results from the perspective of EE and robustness.
Construction Project Scheduling Taking into Account the Risk of Temperatures Below –10°C in Poland

The article attempts to determine the value of the duration of the building process depending on the risk of the occurrence of temperatures below –10°C in Poland. It summarizes the results of measurements of the temperature study over 62 years (from 1950 to 2011) in Poland. The number of days with temperatures below –10°C in future years was forecasted. The method of ARIMA (Autoregressive Integrated Process Average) STATISTICA STAT SOFT was used. Stationarity series was tested using an IDW Durbin Watson integrative statistics and homoscedasticity was tested by the Levene test. The forecast error was verified using autocorrelation and partial autocorrelation residues functions. On the basis of calculations, the risk values for outdoor works in each month in Poland was established. It is a part of BIM 6-D calculation. The calculated risk value can be used for the calculation in Risky Project Professional and in other programs. The proposed risk values and the method of calculation are the upcoming modification product, which has not been reported yet.
Concept Meaning Acquisition based on HowNet and Its Application in the Construction of Taxonomy

In studies related to the construction of domain ontology, the acquisition of concept meaning has not received enough attention. According to the definition of the concept, the acquisition of concept meaning is a requisite task in the process of ontology construction. This paper studied the automatic acquisition of concept meaning based on HowNet and researched the problem of meaning acquisition for complex terms and synonym removal. Grounded on concept meaning, this paper put forward a sememe suffix tree algorithm and applied it to the construction of ontology taxonomy. Compared to traditional algorithms, this method is more efficient and comprehensible. This paper implemented the methods to the domain of ethnic minorities, and the experimental results showed that this paper is referable.
Dominating Sets Construction in RF-based Battery-free Sensor Networks with Full Coverage Guarantee

A new network architecture, named as RF-based battery-free sensor network, was proposed in recent years to overcome the lifetime limitation of traditional wireless sensor networks. In an RF-based battery-free sensor network, the battery-free nodes equip no battery and can be recharged by RF-signals. The Dominating Set (DS) is a key method to maintain the coverage of traditional WSNs, and it can be also adopted in the RF-based battery-free sensor networks. However, considering the specific features of RF-based battery-free sensor networks, the DS construction is totally different from that in traditional WSNs. Thus, the problem of constructing DS in a battery-free sensor network is deeply investigated in this article. The NP-Hardness of such problem is proved. Four approximation algorithms are proposed to deal with the snapshot and continuous DS construction requirements, respectively. The approximation ratios of these four algorithms have been analyzed, and the theoretical results show that all these four algorithms are effective. Furthermore, the electromagnetic interference problem in the RF-based battery-free sensor network is considered and defined. An approximated algorithm is proposed to solve such problem. Finally, extensive simulations are carried out. The experimental results verify that the proposed algorithms have high performance in terms of accuracy and efficiency.
Novel Schemes for Dynamic Monte Carlo Weighting

Ion Cyclotron Resonance Heating (ICRH) is a technique used for heating magnetically confined plasmas [1]. The goal of these plasma experiments is to achieve controlled thermonuclear fusion. Under the influence of ICRH the ion distribution function is no longer Maxwellian, it develops a high energy tail [2]. Most ions still have energies comparable to the thermal energy of the ion distribution. The energetic particles are however important for understanding the heating process [3], for the loss of particles [4, 5] and for the excitation of instabilities [6]. The distribution function evolves according to diffusion-advection equations [2, 7], which can generally be solved with Monte Carlo methods. For this purpose, one uses a discrete set of markers that evolve according to the Langevin equation. However only inaccurate information can be obtained about regions where the distribution function is small, because they are populated only sparsely by markers. An example for such a region is the high energy tail of the ion distribution function in a plasma. This thesis proposes and investigates reweighting algorithms that permit us to have differently weighted markers for different regions of phase space. This allows better resolution of regions with small densities. When markers travel between regions we split them or remove some of them to avoid mixing of markers of different weight. This leads to correlated markers. These correlations can reduce the accuracy of the results. Simple diffusion-advection equations serve as test cases for characterizing our new methods. Without reweighting, the square of the relative error of density calculations is inversely proportional to the density. We establish that with reweighting the squared relative error can be kept in the same order of magnitude even when the density varies over several orders of magnitude. After suggesting a general procedure for determining the reweighting parameters, we implement our algorithms in the code ASCOT-RFOF [8, 9], which calculates the ion distribution function under the influence of ICRH. As one result of our thesis, the computational time required for resolving the high energy tail at E > 1.3 MeV can be reduced by a factor of 100. Simultaneously the number of markers required by the simulation for developing the high energy tail is reduced by a factor of approximately 50. Our methods can be included in any Monte Carlo code for solving diffusion-advection equations, provided it can handle a fluctuating number of markers with nonuniform weight.
Deep Convolutional Neural Network for Ulcer Recognition in Wireless Capsule Endoscopy: Experimental Feasibility and Optimization

Wireless capsule endoscopy (WCE) has developed rapidly over the last several years and now enables physicians to examine the gastrointestinal tract without surgical operation. However, a large number of images must be analyzed to obtain a diagnosis. Deep convolutional neural networks (CNNs) have demonstrated impressive performance in different computer vision tasks. Thus, in this work, we aim to explore the feasibility of deep learning for ulcer recognition and optimize a CNN-based ulcer recognition architecture for WCE images. By analyzing the ulcer recognition task and characteristics of classic deep learning networks, we propose a HAnet architecture that uses ResNet-34 as the base network and fuses hyper features from the shallow layer with deep features in deeper layers to provide final diagnostic decisions. 1,416 independent WCE videos are collected for this study. The overall test accuracy of our HAnet is 92.05%, and its sensitivity and specificity are 91.64% and 92.42%, respectively. According to our comparisons of F1, F2, and ROC-AUC, the proposed method performs better than several off-the-shelf CNN models, including VGG, DenseNet, and Inception-ResNet-v2, and classical machine learning methods with handcrafted features for WCE image classification. Overall, this study demonstrates that recognizing ulcers in WCE images via the deep CNN method is feasible and could help reduce the tedious image reading work of physicians. Moreover, our HAnet architecture tailored for this problem gives a fine choice for the design of network structure.
SDN/NFV‐based handover management approach for ultradense 5G mobile networks

With the great increase of connected devices and new types of applications, mobile networks are witnessing exponential growth of traffic volume. To meet emerging requirements, it is widely agreed that the fifth‐generation mobile network will be ultradense and heterogeneous. However, the deployment of a high number of small cells in such networks poses challenges for the mobility management, including frequent, undesired, and ping‐pong handovers, not to mention issues related to increased delay and failure of the handover process. The adoption of software‐defined networking (SDN) and network function virtualization (NFV) technologies into 5G networks offers a new way to address the above‐mentioned challenges. These technologies offer tools and mechanisms to make networks flexible, programmable, and more manageable. The SDN has global network control ability so that various functions such as the handover control can be implemented in the SDN architecture to manage the handover efficiently. In this article, we propose a Software‐Defined Handover (SDHO) solution to optimize the handover in future 5G networks. In particular, we design a Software‐Defined Handover Management Engine (SDHME) to handle the handover control mechanism in 5G ultradense networks. The SDHME is defined in the application plane of the SDN architecture, executed by the control plane to orchestrate the data plane. Simulation results demonstrate that, compared with the conventional LTE handover strategy, the proposed approach significantly reduces the handover failure ratio and handover delay.
Continuous Athlete Monitoring in Challenging Cycling Environments Using IoT Technologies

Internet of Things (IoT)-based solutions for sport analytics aim to improve performance, coaching, and strategic insights. These factors are especially relevant in cycling, where real-time data should be available anytime, anywhere, even in remote areas where there are no infrastructure-based communication technologies (e.g., LTE and Wi-Fi). In this article, we present an experience report on the use of state-of-the-art IoT technologies in cycling, where a group of cyclists can form a reliable and energy efficient mesh network to collect and process sensor data in real-time, such as heart rate, speed, and location. This data is analyzed in real-time to estimate the performance of each rider and derive instantaneous feedback. Our solution is the first to combine a local body area network to gather the sensor data from the cyclist and a 6TiSCH network to form a multihop long-range wireless sensor network in order to provide each bicycle with connectivity to the sink (e.g., a moving car following the cyclists). In this article, we present a detailed technical description of this solution, describing its requirements, options, and technical challenges. In order to assess such a deployment, we present a large publicly available data-set from different real-world cycling scenarios (mountain road cycle racing and cyclo-cross) which characterizes the performance of the approach, demonstrating its feasibility and evidencing its relevance and promising possibilities in a cycling context for providing low-power communication with reliable performance.
Exploiting parsed corpora in grammar teaching

The principal barrier to the uptake of technologies in schools is not technological, but social and political. Teachers must be convinced of the pedagogical benefits of a particular curriculum before they will agree to learn the means to teach it. The teaching of formal grammar to first language students in schools is no exception to this rule. Over the last three decades, most schools in England have been legally required to teach grammatical subject knowledge, i.e. linguistic knowledge of grammar terms and structure, to children age five and upwards as part of the national curriculum in English. A mandatory set of curriculum specifications for England and Wales was published in 2014, and elsewhere similar requirements were imposed. However, few current English school teachers were taught grammar themselves, and the dominant view has long been in favour of ‘real books’ rather than the teaching of a formal grammar. English grammar teaching thus faces multiple challenges: to convince teachers of the value of grammar in their own teaching, to teach the teachers the knowledge they need, and to develop relevant resources to use in the classroom. Alongside subject knowledge, teachers need pedagogical knowledge – how to teach grammar effectively and how to integrate this teaching into other kinds of language learning. The paper introduces the Englicious1 web platform for schools, and summarises its development and impact since publication. Englicious draws data from the fully-parsed British Component of the International Corpus of English, ICE-GB. The corpus offers plentiful examples of genuine natural language, speech and writing, with context and potentially audio playback. However, corpus examples may be ageinappropriate or over-complex, and without grammar training, teachers are insufficiently equipped to use them. In the absence of grammatical knowledge among teachers, it is insufficient simply to give teachers and children access to a corpus. Whereas so-called ‘classroom concordancing’ approaches offer access to tools and encourage bottom-up learning, Englicious approaches the question of grammar teaching in a concept-driven, top-down way. It contains a modular series of professional development resources, lessons and exercises focused on each concept in turn, in which corpus examples are used extensively. Teachers must be able to discuss with a class why, for instance, work is a noun in a particular sentence, rather than merely report that it is. The paper describes the development of Englicious from secondary to primary, and outlines some of the practical challenges facing the design of this type of teaching resource. A key question, the ‘selection problem’, concerns how tools parameterise the selection of relevant examples for teaching purposes. Finally we discuss curricula for teaching teachers and the evaluation of the effectiveness of the intervention.
Clickbait detection using multiple categorisation techniques

Clickbaits are online articles with deliberately designed misleading titles for luring more and more readers to open the intended web page. Clickbaits are used to tempt visitors to click on a particular link either to monetise the landing page or to spread the false news for sensationalisation. The presence of clickbaits on any news aggregator portal may lead to unpleasant experience to readers. Automatic detection of clickbait headlines from news headlines has been a challenging issue for the machine learning community. A lot of methods have been proposed for preventing clickbait articles in recent past. However, the recent techniques available in detecting clickbaits are not much robust. This article proposes a hybrid categorisation technique for separating clickbait and non-clickbait articles by integrating different features, sentence structure and clustering. During preliminary categorisation, the headlines are separated using 11 features. After that, the headlines are recategorised using sentence formality and syntactic similarity measures. In the last phase, the headlines are again recategorised by applying clustering using word vector similarity based on t-stochastic neighbourhood embedding (t-SNE) approach. After categorisation of these headlines, machine learning models are applied to the dataset to evaluate machine learning algorithms. The obtained experimental results indicate that the proposed hybrid model is more robust, reliable and efficient than any individual categorisation techniques for the dataset we have used.
A Microstrip MIMO Antenna with Enhanced Isolation for WiMAX Applications

In this paper, A Multiple Input Multiple Output (MIMO) antenna is designed by using Defected Ground Structure (DGS) to obtain high isolation. The proposed antenna constructed by two sets of four elements (2 x 2), which is designed at the central frequency of 3.5 GHz for Worldwide Interoperability for Microwave Access (WiMAX) applications. The structure of the antenna has the overall size of 144 x 99 x 1.6 mm3. Thanks to DGS, the MIMO antenna gets the high isolation of 30 dB and a while the antenna witnesses a high efficiency of over 90%. Besides, the gain of MIMO antenna achieves 7.5 dBi. The antenna is fabricated on FR4 substrate with height of 1.6 mm and there is a good agreement between simulated S-parameter and measurement results of the antenna.
Hiring is Broken: What Do Developers Say About Technical Interviews?

Technical interviews—a problem-solving form of interview in which candidates write code—are commonplace in the software industry, and are used by several well-known companies including Facebook, Google, and Microsoft. These interviews are intended to objectively assess candidates and determine fit within the company. But what do developers say about them? To understand developer perceptions about technical interviews, we conducted a qualitative study using the online social news website, Hacker News—a venue for software practitioners. Hacker News posters report several concerns and negative perceptions about interviews, including their lack of real-world relevance, bias towards younger developers, and demanding time commitment. Posters report that these interviews cause unnecessary anxiety and frustration, requiring them to learn arbitrary, implicit, and obscure norms. The findings from our study inform inclusive hiring guidelines for technical interviews, such as collaborative problem-solving sessions.
Des algorithmes presque optimaux pour les problèmes de décision séquentielle à des fins de collecte d'information.Near-optimal algorithms for sequential information-gathering decision problems

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Near-Optimal Algorithms for Sequential Information-Gathering Decision Problems Mauricio Araya-López