Miracle-dz/UniFAR-FTUs huggingface dataset:
Lean Gamification Canvas : A New Tool for Innovative Gamification Design Process

In the contemporary circumstances where possessing a good and unique idea is not enough to start a successful business, it seems that how to perform and apprehend the idea itself is of great importance; The business model should support the designers/stakeholders to produce accurate information/knowledge about their users to be applied into their business model. The process of designing gamified systems/environment/services is no exception to the mentioned rule that paying attention to the needs of the users can improve the effectiveness of the gamified experience. Emphasizing the need of a user-oriented approach in the gamification process, the present article tries to selectively review the literature on the subject by conducting the interdisciplinary studies [business model and gamification], and in the next step, with the help of an analytical-descriptive approach in the process of the commercialization of gamification, we have introduced a model inspired by “Gamification 3.0”, “Lean Canvas” and focussing on the “Gamification Structure MDE”, entitled “Lean Gamification Canvas” to offer an effective way to accommodate user-related “deep knowledge” and eventually provide the “gamifiers” [gamification designers] with the sufficient knowledge to streamline their design process to increase gamified synergistic engagement and the successful commercialization of gamify-able ideas.
On Perfect Privacy

The problem of private data disclosure is studied from an information theoretic perspective. Considering a pair of dependent random variables <inline-formula> <tex-math notation="LaTeX">$(X,Y)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula> denote the private and useful data, respectively, the following problem is addressed: What is the maximum information that can be revealed about <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula>, measured by mutual information <inline-formula> <tex-math notation="LaTeX">$I(Y;U)$ </tex-math></inline-formula>, in which <inline-formula> <tex-math notation="LaTeX">$U$ </tex-math></inline-formula> denotes the revealed data, while disclosing no information about <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>, captured by the condition of statistical independence, i.e., <inline-formula> <tex-math notation="LaTeX">$X \Vert\! \! \! \! {_{\_{}\_{}}} U$ </tex-math></inline-formula>, and henceforth called <italic>perfect privacy</italic>)? We analyze the supremization of <italic>utility</italic>, i.e., <inline-formula> <tex-math notation="LaTeX">$I(Y;U)$ </tex-math></inline-formula> under the condition of perfect privacy for two scenarios: <italic>output perturbation</italic> and <italic>full data observation</italic> models, which correspond to the cases where a Markov kernel, called <italic>privacy-preserving mapping</italic>, applies to <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula> and the pair <inline-formula> <tex-math notation="LaTeX">$(X,Y)$ </tex-math></inline-formula>, respectively. When both <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula> have a finite alphabet, the linear algebraic analysis involved in the solution provides some interesting results, such as upper/lower bounds on the size of the released alphabet and the maximum utility. Afterwards, it is shown that for the jointly Gaussian <inline-formula> <tex-math notation="LaTeX">$(X,Y)$ </tex-math></inline-formula>, perfect privacy is not possible in the output perturbation model in contrast to the full data observation model. Finally, an asymptotic analysis is provided to obtain the rate of released information when a sufficiently small leakage is allowed. In particular, in the context of output perturbation model, it is shown that this rate is always finite when perfect privacy is not feasible, and two lower bounds are provided for it; When perfect privacy is feasible, it is shown that under mild conditions, this rate becomes unbounded.
Big Data Analytics for Prediction Modelling in Healthcare Databases

Bigdata in healthcare has manifested as well as benefited healthcare practioners and scientists around the globe to detect hidden patterns for future clinical decision making. The major complexity faced in real world application domain is the volume of Electronic Health Records (EHR) which has gathered due to high end IT based technology which has boomed in past century for early detection of disease. The traditional technology tools adopted were incapable to discover hidden patterns due to its computational requirements. So, Big data has its generosity need in healthcare intervene technology due to diverse nature of data and accelerated speed of data that needs to processed for better diagnostic interventions. This study has been conducted using predictive data analytics on big data for discovery of knowledge for future decision making. The study consists of information about 3,56,507 patients from 1982–2010. Data curation has been done by organizing under various categories including Age, Year (1982–2010), Incidence Counts (1982–2010, all age groups and both genders), and Mortality Counts (1982–2010, all age groups). The results represents invariable patterns which can be utilized for future predictive modelling.
An HARQ Assisted Cognitive NOMA Scheme for Secure Transmission With Imperfect SIC

This paper employs hybrid automatic repeat request (HARQ) to assist the cognitive non-orthogonal multiple access (NOMA) scheme for secure transmission in Internet of Things networks where the security-required (SR) users with high-security requirements and quality of services (QoS)-sensitive (QS) users with real-time process requirements are paired to perform NOMA for increasing the network connectivity. In addition, the imperfect successive interference cancellation (SIC) is considered at both legitimate users and eavesdropper for providing a realistic analysis. The closed-form expressions for the connection outage probability (COP), the secrecy transmission probability (RSP), and effective secrecy throughput (EST) of the SR user are derived in the proposed lightweight but efficient randomized retransmission scheme (RRS). As benchmarks, the secrecy performances of the fixed retransmission scheme (FRS) and orthogonal multiple access (OMA) scheme are also investigated. Besides, the asymptotic secrecy analysis is given to gain a better insight into secrecy performance. The analytical results developed in the paper demonstrate that HARQ is beneficial to secrecy transmission and the RRS outperforms or equals to the FRS and OMA scheme when the transmit power and secrecy coding redundancy is sufficiently high.
Design and Comparison of Three Surface-Mounted PM Motors for a Light Electric Vehicle

Electric vehicles (EV) are increasingly used in both urban and industrial environments. Electric motors are the essential part of the EV power train systems. Different types of electric motor technologies have been developed for the purpose. Amongst others, permanent magnet synchronous motors (PMSM) are widely used for EV applications because of their unique advantages such as high efficiency and high power density. This paper presents the optimal design of three different surface-mounted PMSMs with fractional-slot toothconcentrated and distributed winding topologies, respectively. The performance comparison of these motors will outline their respective advantages and disadvantages.
Scattering by Perfectly Conducting Cylindrical Targets Hidden Below a Multilayered Medium

An approach to solve the scattered field by perfectly conducting cylinders with circular cross-section, placed below a multilayered medium, under illumination by a line source, is presented. In principle, the interaction of the field scattered by the targets with the multilayer above leads to an infinite number of scattered fields, excited as multiple reflections in each layer by the interfaces. Nevertheless, the developed theoretical formulation is a very compact one, as only two scattered-field contributions are used in each layer, a down-propagating term, described as scattered-reflected field, and an up-propagating one relevant to the scattered-transmitted field. Suitable basis functions, i.e., cylindrical waves expressed through plane-wave spectra, are employed, which are defined through reflection and transmission coefficients of a multilayered medium. As shown in the numerical implementation, the method can be applied to the modeling of several radar problems of targets in complex environments. For example, the technique can be employed to model buried cylinders in geophysical scenarios, in Ground Penetrating Radar applications. The modeling of scattered field by hidden targets in Through-the-Wall radar surveys is another possible field of use. The multilayer may, indeed, model a stratified masonry, but also a room hiding a target in a buildings’ interior.
An Active Load-Pull Technique to Emulate Outphasing Power Amplifiers

A general measurement-based design methodology for outphasing transmitter architectures is presented. The emulation technique allows to predict the full real world performance of the outphasing scheme without requiring to fabricate the complete power amplifier (PA). Simply, iterative active load-pull measurements of a single branch PA together with the Sparameters of the output combiner are needed. The method determines the authentic dynamic loading condition of both branch PAs by revealing the internal interactions between the nonlinear active devices. Thereby, the insight into the dynamics of load modulation during the outphasing operation provided by the technique, allows PA designers to optimize circuit performance. The design methodology is demonstrated by emulating an outphasing scheme at 2.14 GHz operated in pure-and mixed-mode.
Epidemic Threshold in Temporal Multiplex Networks With Individual Layer Preference

Many efforts have been devoted to understanding how human behaviors induced by the awareness of disease influence the epidemic dynamics in multiplex networks. The awareness and virus spreading is mostly studied with the unaware-aware-unaware-susceptible-infected-susceptible (UAU-SIS) model over a given and fixed (static) network. However, the role of the spatial-temporal properties of multiplex networks to the epidemic spreading is not fully understood. In this work, a temporal multiplex network consists of a static information spreading network and a temporal physical contact network with a layer-preference walk. Defining the tendency of aware nodes moving to a specified layer as the layer preference, we focus on two scenarios: (i) the layer preference is constant, (ii) the layer preference is a function of the degree of nodes in the information layer. We deduce the epidemic threshold of such a temporal multiplex network, and validate it through numerical simulations. Moreover, we find that the epidemic threshold decreases with the decrease of the effective information spreading rate and the increase of the layer preference. Furthermore, we find that when the node with a higher degree displays a higher layer preference, the spreading process on the network is remarkably promoted. The findings shed a new light on the control of the epidemic spreading process.
Trajectory Learning for Stable Bipedal Walking Robots using Sequential Networks

Bipedal walking in an unknown environment is an extremely challenging problem due to its inherently unstable structure. For bipedal robots, so far, this problem has been tried using analytical methods with predefined sets of parameters, having limited success. Such robots could walk only in a structured environment with a flat floor and other restrictions. This paper proposes such models having learning ability to be imparted to the biped robots based on walking sequences. Two sequential network models have been configured, Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM), to learn the joint trajectories of the hip, knee, and ankle joints, separately and their performances have been studied. Using these two models, learning has been imparted to a robot both in the sagittal plane and in the frontal plane. We use the Pybullet physics engine of a biped robot for simulation purposes. Data have been collected by running several simulations and trained our sequential network models with these collected data. We observe that the present model's walking patterns could be learned both by GRU and LSTM within ten episodes with L2 regularizer. For our present study, the GRU model has several thousand fewer network parameters to learn than the LSTM model. We recommend using GRU as a first choice for controlling the biped robots, walking with simple modes. However, for more complex modes, such as walking with long steps, brisk walking, walking, having push recovery capability, etc., we may need to collect larger data sets for training and testing to suggest which model would be more suitable. Our recommendations are backed by rigorous experimentation.
CKDAC: Cluster-Key Distribution and Access Control for Secure Communication in IoT

The Internet of Things (IoT) is a system of interconnected Smart Devices, Digital Machines, and Things that exchange data through Machine-to-Machine communication with minimal human mediation. The scalability of the IoT devices increases the degree of connectivity to share the Data and causes unidentified trap doors for any intruder to exploit. In the proposed system, Cluster-Key Distribution and Access Control (CKDAC) for secure communication in IoT uses Publication-Subscription architecture with the dynamic Cluster Head election to enhance the security and availability of the communicating IoT Devices. The proposed CKDAC reduces Time of Computation by decreasing the processing time of the re-keying operations when the IoT node enters or exits the Cluster using the Cryptographic algorithm in the Publication-Subscription model. CKDAC extends the availability by electing the Cluster Head with the highest resources from the set of IoT Devices. The proposed scheme provides improved performance compared to Distributed Group Key Management,
DDES: A Distribution-Based Dynamic Ensemble Selection Framework

Dynamic Ensemble Selection (DES) is a special type of ensemble modeling that selects different subsets of base classifiers for different test sample cases. In this process, multiple base classifiers are compared in terms of their competence as an ensemble to the best possible prediction for a given test sample case. Traditional DES methods rely on the Euclidean distance-based k-Nearest Neighbor (kNN) algorithm to identify the most relevant reference data points in such a way that the base classifiers correctly classifying them can be considered for an optimal ensemble. However, this approach is sensitive to the local structure of the data and the presence of noisy or irrelevant attributes. This study proposes a novel distribution-based DES (DDES) framework that takes the data structure into consideration when selecting reference data points. The experimental results for 30 classification problems indicate that the proposed approach yielded the best accuracy among the competing DES methods. Additionally, we discuss the correlation between the data complexity and the improvement in classification performance.
Optimizing RGB-D Fusion for Accurate 6DoF Pose Estimation

Today's standard object localization systems often do not meet the industry's demands regarding 2D and 3D accuracy for digital manufacturing applications. Two targets are considered: digital-based assistance and robotic inspection. 2D precision is necessary to provide accurate assistance whilst 3D precision is crucial to get an inspection as much close to the object's true state. In this letter, we propose a new pose estimation system which ensures highest both 2D and 3D precision. While most RGB-based solutions focus on obtaining best 2D accuracy, RGBD-based systems mainly use depth information to maximize 3D accuracy. Very few solutions propose a way to jointly optimize both constraints. Nonetheless, pose estimation should produce high accuracy as a slight 2D error can result in a large 3D error (and inversely). To address this problem, we present a new system which uses RGB-D to fully take advantage of the depth information. A new 3D primitive is proposed in order to minimize the effect of RGB-D noise on 3D coordinates accuracy. CNN Keypoint Detector (KPD) method is used to localize this new primitive in order to achieve pose estimation task. Finally, we propose a novel refinement method which ensures optimal precision as both RGB and depth information are fused. We show the results of our experimentation on widely-used and challenging Linemod and Occlusion datasets. We demonstrate that our solution outperforms state-of-the-art methods when taking into account both 3D and 2D accuracy.
Optimizing Resource Allocation for Data-Parallel Jobs Via GCN-Based Prediction

Under-allocating or over-allocating computation resources (e.g., CPU cores) can prolong the completion time of data-parallel jobs in a distributed system. We present a predictor, ReLocag, to find the near-optimal number of CPU cores to minimize job completion time (JCT). ReLocag includes a graph convolutional network (GCN) and a fully-connected network (FCNN). The GCN learns the dependency between operations from the workflow of a job, and then the FCNN takes the workflow dependency together with other features (e.g., the input size, the number of CPU cores, the amount of memory, and the number of computation tasks) as input for JCT prediction. The prediction result can guide the user to determine the near-optimal number of CPU cores. Besides, we propose two effective strategies to overcome the time-consuming issue of training sample collection in big data applications. First, we develop an adaptive sampling method to collect essential samples judiciously. Second, we further design a cross-application transfer learning model to exploit the training samples collected from other applications. We conduct extensive experiments in a Spark cluster for 7 types of exemplary Spark applications. Results show that ReLocag improves the JCT prediction accuracy by 4—14 percent. Moreover, the CPU core consumption decreases by 58.2 percent.
Towards Embedding Data Provenance in Files

Data provenance (keeping track of who did what, where, when and how) boasts of various attractive use cases for distributed systems, such as intrusion detection, forensic analysis and secure information dependability. This potential, however, can only be realized if provenance is accessible by its primary stakeholders: the end-users. Existing provenance systems are designed in a ‘all-or-nothing’ fashion, making provenance inaccessible, difficult to extract and crucially, not controlled by its key stakeholders. To mitigate this, we propose that provenance be separated into system, data-specific and file-metadata provenance. Furthermore, we expand data-specific provenance as changes at a fine-grain level, or provenance-per-change, that is recorded alongside its source. We show that with the use of delta-encoding, provenance-per-change is viable, asserting our proposed architecture to be effectively realizable.
Towards Constructing HMM Structure for Speech Recognition With Deep Neural Fenonic Baseform Growing

For decades, acoustic models in speech recognition systems pivot on Hidden Markov Models (HMMs), e.g., Gaussian Mixture Model-HMM system, Deep Neural Network-HMM system, etc., and achieve remarkable results. However, the popular HMM model is the three-state left-to-right structure, without the superiority certainty. There are multiple studies on the HMM structure’s optimization, but none of them addresses this problem leveraging deep learning algorithms. For the first time, this paper proposes a new training method based on Deep Neural Fenonic Baseform Growing to optimize the HMM structure, which is concisely designed and computationally cheap. Moreover, this data-driven method customizes the HMM structure for each phone precisely without external assumptions concerning the number of states or transition patterns. Experimental results on both TIMIT and TEDliumv2 corpora indicate that the proposed HMM structure improves both the monophone system and the triphone system substantially. Besides, its adoption further improves state-of-the-art speech recognition systems with remarkably reduced parameters.
Perfect Tabular Hashing in Pseudolinear Time

We present an algorithm for generating perfect tabulation hashing functions by reduction to Boolean satisfaction (SAT). Tabulation hashing is a high-performance family of hash functions for hash tables that involves computing the XOR of random lookup tables. Given $n$ keys of word size $W$, we show how to compute a perfect hash function in $O(nW)$ worst-case time. This is competitive with other perfect hashing methods, and the resultant hash functions are simple and performant.
Sparse Dual Graph-Regularized Deep Nonnegative Matrix Factorization for Image Clustering

Deep nonnegative matrix factorization (Deep NMF) as an emerging technique for image clustering has attracted more and more attention. This is because it can effectively reduce high-dimensional data and reveal the latent hierarchical information of the complex data. However, two limitations may still deteriorate their performances: (1) the local invariance of the input data is insufficiently explored, that is, the intrinsic geometrical structures of the original data in the data and feature spaces are not considered simultaneously; (2) the sparseness that can greatly improve the ability of learning parts is also ignored. In this paper, we propose a novel approach to address the above two problems, referred to as Sparse Dual Graph-regularized Deep Nonnegative Matrix Factorization (SDG Deep NMF), which can learn sparse and informative deep features while sufficiently exploring the local invariance of the data to discover valuable information underlying the input data. Specifically, SDG Deep NMF learns the informative deep features by performing the dual graph regularization in the deep NMF framework, which can respect the intrinsic geometrical structures of the input data in the data and feature spaces while mining the data information in hidden layers. Meanwhile, SDG Deep NMF also imposes sparse constraints on the basis matrix during the feature learning to improve the part-based learning capabilities. Moreover, we construct the objective function of SDG Deep NMF in the form of the Euclidean distance for convenience, the iterative updating scheme is chosen to optimize it. Comprehensive experiments on four benchmark datasets can demonstrate the effectiveness of the proposed approach in image clustering.