Miracle-dz/UniFAR-FTUs huggingface dataset:
A Flexible N-Triples Loader for Hadoop

The wide adoption of the RDF data model demands efficient and scalable query processing strategies. For this purpose distributed programming paradigms such as Apache Spark on top of Hadoop are increasingly being used. Unfortunately, the Hadoop ecosystem lacks support for Semantic Web standards, e.g. reading an RDF serialization format, and thus, bringing in RDF data still requires a large amount of effort. We therefore present PRoST-loader, an application which, given a set of N-Triples documents, creates logical partitions according to three widely adopted strategies: Triple Table (TT), Wide Property Table (WPT) with a single row for each subject, and Vertical Partitioning (VP). Each strategy has its own advantages and limitations depending on the data characteristics and the task to be carried out. The loader thus leaves the strategy choice to the data engineer. The tool combines the flexibility of Spark, the deserialization capabilities of Hive, as well as the compression power of Apache Parquet at the storage layer. We managed to process Dbpedia (approx. 257M triples) in 3.5 min for TT, in approx 3.1 days for VP, and in 16.8 min for WPT with up to 1,114 columns in a cluster with moderate resources. In this paper we aim to present the strategies followed, but also to expose the community to this open-source tool, which facilitates the usage of Semantic Web data within the Hadoop ecosystem and which makes it possible to carry out tasks such as the evaluation of SPARQL queries in a scalable manner.
Every Day’s a New Day: A Captain’s Reflection on a Losing Season

Being captain of any team is a significant and prestigious position. In elite sport, the captain plays a vital role in both team and organisational leadership. To date there has been minimal research investigating captaincy, and none assessing the impact of regularly losing performances. A captain of a women’s national basketball league team participated in an in-depth, semi-structured interview reflecting on her experience during a losing season. Following Schutz’s (1967) framework, a social phenomenological analysis approach was taken, with seven higher-order themes emerging: being captain; relationship with coaching staff; relationship with teammates; team development; stressors; stress management; and self. Results suggest that while poor results affect all team members, there are increased levels of stress for the captain. The captain is expected to lead by example and avoid external expression of negativity. Further strains are placed on the captain, as they are the conduit between coaching and playing groups. As a result, the captain needs to have good support networks, from a variety of sources, to cope and minimise the impact on personal performance.
Multiple Reflection Analysis of TDR Signal for Complex Dielectric Spectroscopy

Most dielectric spectroscopy techniques require careful system calibration, tedious measurement, specially designed probes, precise input source, and some even involved complicated inversion models. This paper proposed a rapid, robust, and model-free multiple reflection analysis (MRA) of time-domain reflectometry (TDR) signals to measure the complex dielectric permittivity (CDP) spectrum. The key to MRA approach is to decompose the first top reflection and the subsequent multiple reflections from TDR signal and to compare their spectral ratio (MRA ratio). This ratio was theoretically derived from the transmission line theory and found to be independent of source function and impedance mismatches in the leading sections. Based on this theoretical formulation, the CDP spectrum can be uniquely inverted from the measured MRA ratio through optimization and an iterated initial guess method. Numerical evaluations and experimental verifications had proven that MRA is a reliable algorithm for measuring CDP spectrum covering 10 MHz–1 GHz. Factors influencing the reliable frequency region were discussed and recommendations on enhancing CDP measurement was proposed for highly dispersive materials. The MRA approach enables dielectric spectroscopy to be conveniently conducted in both laboratory and field, without complicated system setup and calibration.
Divide and Count: Generic Object Counting by Image Divisions

We propose a general object counting method that does not use any prior category information. We learn from local image divisions to predict global image-level counts without using any form of local annotations. Our method separates the input image into a set of image divisions—each fully covering the image. Each image division is composed of a set of region proposals or uniform grid cells. Our approach learns in an end-to-end deep learning architecture to predict global image-level counts from local image divisions. The method incorporates a counting layer which predicts object counts in the complete image, by enforcing consistency in counts when dealing with overlapping image regions. Our counting layer is based on the inclusion–exclusion principle from set theory. We analyze the individual building blocks of our proposed approach on Pascal-VOC2007 and evaluate our method on the MS-COCO large scale generic object data set as well as on three class-specific counting data sets: UCSD pedestrian data set, and CARPK, and PUCPR+ car data sets.
Adaptive Layerwise Quantization for Deep Neural Network Compression

Building efficient deep neural network models has become a hot-spot in recent years for deep learning research. Many works on network compression try to quantize a neural network with low bitwidth weights and activations. However, most of the existing network quantization methods set a fixed bitwidth for the whole network, which leads to large performance drop under high compression rate. In this paper we introduce an adaptive layerwise quantization method which quantizes the network with different bitwidth assigned to different layers. By using entropy of weights and activations as an importance indicator for each layer, we keep most of the layers under a high compression rate while a few most important layers receive more bit assignment. Experiments on CI-FAR10 and ImageNet2012 datasets demonstrate that our layerwise quantization could achieve smaller model size and less computation cost than the comparison fixed bitwidth methods with comparable accuracy, or higher accuracy with similar model size and computational complexity.
CMOS Compatible Pyroelectric Applications Enabled by Doped HfO2 Films on Deep-Trench Structures

Fully functional pyroelectric films are fabricated on a silicon substrate with deep-trench structures, for the first time. Future integrated pyroelectric applications such as infrared sensors, energy harvesters or temperature manipulation devices require large current responses at low device footprints. In this work, pyroelectric Si-doped HfO2 is deposited in trench structures to considerably increase the pyroelectric response, yielding a coefficient of f $\pmb{p=-1560\mu}\mathbf{C}/\mathbf{m}^{2}\mathbf{K}$, which is four times larger than that of PZT projected to the device area. A large harvestable energy density of $\pmb{F_{\mathbf{E}}=542}\ \ \mathbf{J}/\mathbf{m}^{3}\mathbf{K}^{2}$ is measured. Simultaneously, CMOS compatible and RoHS compliant manufacturing is demonstrated. Metalorganic atomic layer deposition (ALD) is used to coat the $\pmb{1:16}$ aspect ratio structures conformally with uniform silicon dopant levels.
Clustering Variational Mode Decomposition for Identification of Focal EEG Signals

The electroencephalogram (EEG) provides a noninvasive way to assess the partial epileptic brain pathology. The diagnosis of epilepsy is made possible by identification of focal EEG signals. In this article, a novel method clustering variational mode decomposition (VMD) is introduced for focal EEG signal identification. VMD is an adaptive signal decomposition technique, which shows the limitations for processing of nonhomogeneous signals, such as EEG. The clustering-VMD (CVMD) is proposed to overcome the limitations of VMD. In CVMD, first, the nonhomogeneous EEG signal is analyzed by optimum allocation sampling into homogeneous EEG-clusters and then decomposed into band-limited modes. The spectral moment based features are extracted from the modes of CVMD and applied as the input to the extreme learning machine classifier for identification of focal EEG signals. The proposed method provides the identification of focal EEG signals, and the performance measures of accuracy, sensitivity, and specificity are 96, 94.69, and 97.39, respectively.
Extending Embedding Representation by Incorporating Latent Relations

The semantic representation of words is a fundamental task in natural language processing and text mining. Learning word embedding has shown its power on various tasks. Most studies are aimed at generating embedding representation of a word based on encoding its context information. However, many latent relations, such as co-occurring associative patterns and semantic conceptual relations, are not well considered. In this paper, we propose an extensible model to incorporate these kinds of valuable latent relations to increase the semantic relatedness of word pairs by learning word embeddings. To assess the effectiveness of our model, we conduct experiments on both information retrieval and text classification tasks. The results indicate the effectiveness of our model as well as its flexibility on different tasks.
MPCA SGD—A Method for Distributed Training of Deep Learning Models on Spark

Many distributed deep learning systems have been published over the past few years, often accompanied by impressive performance claims. In practice these figures are often achieved in high performance computing (HPC) environments with fast InfiniBand network connections. For average deep learning practitioners this is usually an unrealistic scenario, since they cannot afford access to these facilities. Simple re-implementations of algorithms such as EASGD <xref ref-type="bibr" rid="ref1">[1]</xref> for standard Ethernet environments often fail to replicate the scalability and performance of the original works <xref ref-type="bibr" rid="ref2">[2]</xref> . In this paper, we explore this particular problem domain and present MPCA SGD, a method for distributed training of deep neural networks that is specifically designed to run in low-budget environments. MPCA SGD tries to make the best possible use of available resources, and can operate well if network bandwidth is constrained. Furthermore, MPCA SGD runs on top of the popular Apache Spark <xref ref-type="bibr" rid="ref3">[3]</xref> framework. Thus, it can easily be deployed in existing data centers and office environments where Spark is already used. When training large deep learning models in a gigabit Ethernet cluster, MPCA SGD achieves significantly faster convergence rates than many popular alternatives. For example, MPCA SGD can train ResNet-152 <xref ref-type="bibr" rid="ref4">[4]</xref> up to 5.3x faster than state-of-the-art systems like MXNet <xref ref-type="bibr" rid="ref5">[5]</xref> , up to 5.3x faster than bulk-synchronous systems like SparkNet <xref ref-type="bibr" rid="ref6">[6]</xref> and up to 5.3x faster than decentral asynchronous systems like EASGD <xref ref-type="bibr" rid="ref1">[1]</xref> .
Language independent recommender agent

Abstract This paper presents a new ‘Language Independent Recommender Agent’ (LIRA), using information distributed over any text-source pair on the Web about candidate items. While existing review-based recommendation systems learn the features of candidate items and users’ preferences, they do not handle varying perspectives of users on those features. LIRA constructs agents for each user, which run regression algorithms on texts from different sources and builds trust relations. The key advantages of LIRA can be listed as: LIRA does not require reviews from target users, LIRA calculates trust values based on prediction accuracy instead of social connections or rating similarity, LIRA does not require the reviews to come from the same community or peer user group. Since ratings of the reviewers are not necessary for LIRA, we can collect and use reviews from different sources (web pages, professional critiques), as long as we know the corresponding item and source of that text. Since LIRA does not combine text from different sources, texts from different sources are not required to be in the same language. LIRA can utilize text from multiple languages, as long as sources are consistent with their language usage.
A Fully-on-Chip Low-Voltage Low-Dropout Regulator with Negative Charge Pump

A negative charge pump enhanced fully-on-chip low-dropout (LDO) regulator is proposed for low-voltage operation. The negative charge pump provides a negative voltage and works as the ground of the LDO controller, which enhances the performance significantly. The proposed LDO is implemented in a standard 65-nm CMOS process. It achieves 0.5V output with 0.6V input voltage. The current regulation range is 1uA-45mA. The quiescent current of the LDO is $21 \mu \mathrm {A}$.
Cyber Security Challenges of Deploying IoT in Smart Cities for Healthcare Applications

As technology improves at an astounding rate, cities have to improve their services in terms of safety, healthcare, transportation, and the overall welfare of their residents. Internet of Things (IoT) play a major role in the infrastructure of a smart city by gathering and processing data from distributed sensors to implement smart services, such as traffic control, environment monitoring, and waste management. However, the most challenging part is securing data from various attacks while maintaining low-complexity techniques and algorithms. Considering security concerns to the deployment of IoT as the main source of data collection and transmission, attacks are classified to identify a suitable scheme of defending against them. This paper provides a two-fold contribution to analyze security and privacy in the context of smart cities for healthcare applications. Thus, on one hand an overview of different IoT applications and their cyber vulnerabilities are presented. On the other hand, a comprehensive evaluation of possible approaches to mitigate the problem of cyber attacks is described.
Energy-Efficient Hardware Caching Decision Using Fuzzy Logic in Mobile Edge Computing

To bring data contents and services in close proximity to the mobile user, mobile edge networks are good candidate because they provide cloud computing and caching capabilities at the edge of cellular networks, hence offering proximity that ensures low latency and high bandwidth through the concept of Mobile Edge Computing (MEC) [22]. To supply effective caching services in the highly resourceconstrained and dynamically mobile environment, we propose an energy-efficient fuzzy caching technique for edge devices. This technique uses various parameters (such as mobility and requests frequency) to ensure the effectiveness of our proposed mechanism that highlights the challenge of the computational complexity in mobile edge computing. In this paper, our contribution is a novel solution based on a hardware implementation that uses Field-Programmable Gate Array (FPGA) as an alternative computational architecture that cuts overall energy requirements. Preliminary implementation and evaluation results demonstrate that the proposed solution reduces the energy consumption and the server load of the edge devices by using an FPGA implementation for fuzzy logic caching decision.
PrivSet: Set-Valued Data Analyses with Locale Differential Privacy

Set-valued data is useful for representing a rich family of information in numerous areas, such as market basket data of online shopping, apps on mobile phones and web browsing history. By analyzing set-valued data that are collected from users, service providers could learn the demographics of the users, the patterns of their usages, and finally, improve the quality of services for them. However, privacy has been an increasing concern in collecting and analyzing users' set-valued data, since these data may reveal sensitive information (e.g., identities, preferences and diseases) about individuals. In this work, we propose a privacy preserving aggregation mechanism for set-valued data: PrivSet. It provides rigorous data privacy protection locally (e.g., on mobile phones or wearable devices) and efficiently (its computational overhead is linear to the item domain size) for each user, and meanwhile allowing effective statistical analyses (e.g., distribution estimation of items, distribution estimation of set cardinality) on set-valued data for service providers. More specifically, in PrivSet, within the constraints of local e-differential privacy, each user independently responses with a subset of the set-valued data domain with calibrated probabilities, hence the true positive/false positive rate of each item is balanced and the performance of distribution estimation is optimized. Besides presenting theoretical error bounds of PrivSet and proving its optimality over existing approaches, we experimentally validate the mechanism, the experimental results illustrate that the estimation error in PrivSet has been reduced by half when compared to state-of-the-art approaches.
A Modified Fuzzy Hysteresis Controller for Shunt Active Power Filter

This paper presents the design and simulation of a fuzzy logic based hysteresis controller for shunt active power filter capable of reducing the total harmonic distortion. Active Power Filter uses power electronic switching of Voltage source inverter to generate harmonic currents that cancel the harmonic currents generated by a non-linear load. The estimated harmonic current is considered as the reference current. Hysteresis band control technique is adopted for the current control of the voltage source inverter. This paper proposes a complete fuzzy controller for the shunt active power filter. A fuzzy hysteresis controller is designed to adjust the bandwidth of the hysteresis controller according to the supply voltage and slope of the reference compensator current. The fuzzy PI controller provides a better dynamic control of DC link capacitor voltage of voltage source inverter. The fuzzy controller and active filter are designed and simulated using MATLAB/SIMULINK.
Modeling supply network configuration problems with varying demand profiles

In this paper, we develop a novel multi-objective modeling approach to support supply network configuration decisions, while considering varying demand profiles. In so doing, we illustrate how such an approach could contribute to building supply network robustness and resilience. The proposed model entails two key objectives; minimizing lead time and cost across the supply network. The solution approach first employs a bidding mechanism to select a set of supply network entities that match with a given demand profile from a candidate pool of entities. It then applies the popular technique known as N on-dominated Sorting Genetic Algorithm-II to generate a set of Pareto-optimal solutions representing alternative supply network configurations. The proposed model is tested on a case study of a refrigerator supply network to draw delivery time and cost comparisons under static and dynamic demand profiles.
Bayesian Network Classifiers Under the Ensemble Perspective

Augmented naive Bayesian classifiers relax the original independence assumption by allowing additional dependencies in the model. This strategy leads to parametrized learners that can produce a wide spectrum of models of increasing complexity. Expressiveness and efficiency can be controlled to adjust a trade-off specific to the problem at hand. Recent studies have transposed this finding to the domain of bias and variance, demonstrating that inducing complex multivariate probability distributions produces low-bias/high-variance classifiers that are especially suitable for large data domains. Frameworks like AkDE avoid structural learning and reduce variance by averaging a full family of constrained models, at the expense of increasing its spatial and computational complexity. Model selection is then required and performed using Information Theory techniques. We present a new approach to reduce model space from the point of view of ensemble classifiers, where we study the individual contribution to error for each model and how model selection affects this via the aggregation process. We perform a thorough experimentation to analyse bias stability and variance reduction and compare the results within the context of other popular ensemble models such as Random Forest, leading to a discussion on the effectiveness of the previous approaches. The conclusions support new strategies to design more consistent ensemble Bayesian network classifiers which we explore at the end of the paper.
Knowledge Management Systems Usage From The User's Perspective: The Influence Of Organizational Factors In Jordanian Banking Sector

Nowadays, most of organizations are shifting or shifted from the industrial economy to the knowledge economy. In this new economy, a new set of advanced information technology systems has emerged. Knowledge management systems (KMS) are innovative advanced information systems that have demonstrated a significant value towards individuals and organizational performance. This research aimed for an empirical evaluation on the influence of organizational factors on KMS usage among decision makers in Jordanian banks. In this respect, a survey (self-administered questionnaire) was distributed at the decision makers who are using the KMS's within Jordanian banks, which resulted with 341 valid responses. Structural equation modeling (SEM), in particular AMOS statistical software was employed for further data analysis. Findings revealed that, decision makers' usage of KMS's within Jordanian banks is significantly affected by the extent of top management support, formalization, and centralization.
Robust adaptive power control for cognitive radio networks

In this study, the problem of robust adaptive power control (PC) in an underlay cognitive radio network with multiple secondary users (SUs) and primary users (PUs) is considered. Due to the effects of uncertainties (i.e. estimation errors, delays), the optimal PC (resource allocation) cannot guarantee the quality of service of SUs and PUs under imperfect channel state information and interference power of PUs. A robust resource allocation problem is formulated to maximise sum throughput of SUs under individual power constraints and signal-to-interference-and-noise ratio constraints of SUs as well as interference temperature constraints of PUs, whereas channel uncertainties and interference uncertainties induced into the secondary system are modelled by multiplicative uncertainties. Under the worst-case approach, the problem is transformed into a geometric programming problem solved by Lagrange dual methods. The performance of the different algorithms and the impact of uncertainties are discussed according to several simulation results.
A Survey of Textual Emotion Detection

Emotion detection in text is a research field that gained an extensive interest recently. The advancement in communication networks and the spread usage of social web is the reason of massive amount of emotions that can be extracted frequently. The extraction and analysis of such emotions provides a powerful tool in detecting and recognizing the feelings, and this provides many advantages in diverse areas. In this survey, we focused on the textual emotion detection as a task of sentiment analysis. This survey explored the latest state of art approaches for emotion detection in text, and discussed their classification according to the used techniques, the used emotional model and the different used datasets. The outcome of this paper tends to highlight the limitation and gaps of these recent works and direct the possible future research to fulfill these gaps in this increasingly developed field.