Miracle-dz/UniFAR-FTUs huggingface dataset:
Interaction: a Key Component to Successful Online Learning: An Investigation of Interaction and a Course of Online Learning

In this global village we live in, education is not restricted to a special place like a school. All learners should be life-long learners, and learning should not be restricted to a time and place. Like any other kind of education, e-learning, a process to create and provide access to learning when the source of information and the learners are separated by time and distance, has special kinds of barriers which need to be known and considered, for instance the high drop-out rate of e-learning and the suitability of e-learning to cover different subjects. These problems appear to be related, directly or indirectly, to the issue of interactivity. Interactivity in e-learning is considered to be more than just clicking a mouse. Interactivity encourages both active learner reflection and manipulation of learning content; it reduces the feelings of isolation and of anxiety of not learning the curriculum. Interactivity facilitates the integration of learned content into existing schemata and in particular into real-life work situations. In the context of e-learning, interactivity is viewed as the principal method of engaging with learners and helping them to attain, retain and sustain the knowledge and skills they are learning. This thesis argues that an in-depth understanding of interactivity in e-learning will reinforce and enhance the capabilities of this learning mode, and consequently have a measurable positive impact on student drop-out rates, and widen the suitability and subject coverage of e-learning.
Predicting Personality Traits from Social Media using Text Semantics

Online social networks are becoming a very rich source of user generated content. This content motivates different types of applications that rely on personalization; such as recommender systems and online marketing. Detecting personalities through mining publicly available social data immerges as an important related issue that can assist web-based systems. Some approaches have been introduced to use publicly available social data to infer user’s personality. This paper presents an approach for personality traits inference based on text semantic analysis. Different representations of user text combined with several semantic based measures are proposed to predict users’ personality through their Facebook status updates. The proposed approach has been tested and validated on data released by the myPersonality project for the Workshop on Computational Personality Recognition. The results prove that the information content-based measure achieves the best average personality trait prediction with an accuracy of 64%.
Super-Resolution for Monocular Depth Estimation With Multi-Scale Sub-Pixel Convolutions and a Smoothness Constraint

Depth estimation from a monocular image is of paramount importance in various vision tasks, such as obstacle detection, robot navigation, and 3D reconstruction. However, how to get an accurate depth map with clear details and a fine resolution remains an unresolved issue. As an attempt to solve this problem, we exploit image super-resolution concepts and techniques for monocular depth estimation and propose a novel CNN-based approach, namely <inline-formula> <tex-math notation="LaTeX">$MSCN_{NS}$ </tex-math></inline-formula>, which involves multi-scale sub-pixel convolutions and a neighborhood smoothness constraint. Specifically, <inline-formula> <tex-math notation="LaTeX">$MSCN_{NS}$ </tex-math></inline-formula> makes use of sub-pixel convolutions with multi-scale fusions to retrieve a high-resolution depth map with fine details of the scene. Different from previous multi-scale fusion strategies, those multi-scale features come from supervised scale branches of the network. Furthermore, <inline-formula> <tex-math notation="LaTeX">$MSCN_{NS}$ </tex-math></inline-formula> incorporates a neighborhood smoothness regularization term to make sure that spatially closer pixels with similar features would have close depth values. The effectiveness and efficiency of <inline-formula> <tex-math notation="LaTeX">$MSCN_{NS}$ </tex-math></inline-formula> have been corroborated through extensive experiments conducted on benchmark datasets.
Development of a Software Tool for Calculating Transmission Line Parameters and Updating Related Databases

Abstract Transmission lines are essential components of electric power grids. Diverse power system applications and simulation based studies require transmission line parameters including series resistance, reactance, and shunt susceptance, and accurate parameters are pivotal in ensuring the accuracy of analyses and reliable system operation. Commercial software packages for performing power system studies usually have their own databases that store the power system model including line parameters. When there is a physical system model change, the corresponding component in the database of the software packages will need to be modified. Manually updating line parameters are tedious and error-prone. This paper proposes a solution for streamlining the calculation of line parameters and updating of their values in respective software databases. The algorithms used for calculating the values of line parameters are described. The software developed for implementing the solution is described, and typical results are presented. The proposed solution is developed for a utility and has a potential to be put into use by other utilities.
A Distributed Spatiotemporal Contingency Analysis for the Lebanese Power Grid

We address a topological vulnerability analysis of the Lebanese power grid subject to random and cascading failures. Using an Apache Spark implementation that maps the topology of the grid to a complex network, we begin by developing a local structural understanding of the Lebanese power grid that reveals a certain level of decentralization via numerous connected components. Our Apache Spark implementation simulates the random and cascading sequences of events by which energy centers in Lebanon can be exposed and are at risk. The implementation is based on the bulk-synchronous parallel model and maintains optimal work, linear communication time, and a constant number of synchronization barriers. We complement this paper with a spatial understanding of the exposed hotspots. Our results reveal that failures in the power grid are spatially long-range correlated and correlations decay with distance. In a couple of attack scenarios, our Spark implementation achieves significant speedup on 16 cores for a graph with about $9\times 10^{5}$ nodes. Scalability toward 32 nodes improves when experimenting with replicas of the power grid graph which are double and quadruple the original size. This renders this paper suitable to larger networks at many vital levels beyond the power grid.
Unambiguous Sparse Recovery of Migrating Targets With a Robustified Bayesian Model

The problem considered is that of estimating unambiguously migrating targets observed with a wideband radar. We extend a previously described sparse Bayesian algorithm to the presence of diffuse clutter and off-grid targets. A hybrid-Gibbs sampler is formulated to jointly estimate the sparse target amplitude vector, the grid mismatch, and the (assumed) autoregressive noise. Results on synthetic and fully experimental data show that targets can be actually unambiguously estimated even if located in blind speeds.
Mining Collaboration Patterns Between APIs for Mashup Creation in Web of Things

The Web of Things (WoT) extends the concept of “Internet of Things (IoT)” in that smart devices in the physical world can be interacted with or integrated via popular web technologies (e.g., HTML, HTTP, and Web API). With the WoT, smart devices can use Web APIs to make their data or functionalities accessible by software. With the popularization of Web 2.0 Mashup applications, creating Mashup applications for the IoT (or WoT) via combining different APIs, also has aroused increasing interests. This paper proposes an approach to mining collaboration patterns between APIs to aid mashup creation for the WoT. The goal of the approach is to disclose what kinds of Web APIs are frequently combined together in mashup creation and what kinds of API combination are popular. Based on a real-world mashup and Web API repository, http://PragrammableWeb.com, we exploit the text description and tags of Web APIs and employ an FP-growth-based association mining algorithm to discover popular collaboration patterns between APIs. To overcome the deficiency caused by tag sparsity, the approach also develops a method based on TF/IDF to expand the tags of Web APIs. The experimental results validated the performance of the proposed approach.
Matching and Modifying with Generics

Haskell has powerful features such as pattern matching and recursive data types for building tree structures easily. These features are not without problems, however: patterns are not composable and cannot be abbreviated; it can be hard to modify a specific part of a large tree structure without unique identifiers. This paper explains how a particular Haskell generics approach, Scrap Your Boilerplate, can be used to solve both of these problems in Haskell, without the need for meta-programming or new language features.
Multiobjective Reinforcement Learning-Based Intelligent Approach for Optimization of Activation Rules in Automatic Generation Control

This paper proposes a novel hybrid intelligent approach to solve the dynamic optimization problem of activation rules for automatic generation control (AGC) based on multiobjective reinforcement learning (MORL) and small population-based particle swarm optimization (SPPSO). The activation rule for AGC is to dynamically allocate the AGC regulating commands among various AGC units, and subsequently, the secondary control reserve of those units can be activated. Therefore, the activation rule for AGC is vital to ensure the overall control performance of AGC schemes. In this paper, MORL is applied to provide a customized platform for interactive self-learning to maximize the long-run discounted reward, i.e., minimize the generation cost, regulating error, and emission from a long-term viewpoint. SPPSO is utilized to effectively and efficiently obtain the optimality of activation rule with a fast convergence speed to fulfill the real-time requirement of AGC activation. Furthermore, a novel analytic hierarchy process-based coordination factor is introduced to identify the optimum multi-objective tradeoff in various power system operation scenarios. At last, the validation of the proposed hybrid method has been demonstrated via comprehensive tests using practical data from the dispatch center of China Southern Power Grids.
Joint Impact of Hardware Impairments and Imperfect CSI on Cooperative SWIPT NOMA Multi-Relaying Systems

The performance of a cooperative non-orthogonal multiple access (NOMA) multi-relay system, where the communication between the base state (BS) and users is carried out through either the direct transmission or the assistance of energy harvesting (EH) relays, is evaluated in the presence of hardware impairment and imperfect channel state information (ICSI). A relay is selected among K EH relays by using partial relay selection (PRS) protocol to forward the received signal to a far user (FU) and a near user (NU). More specifically, exact analytical expressions for the outage probability are derived in closed-form. To obtain deeper insights, we present asymptotic expressions for the outage probability. The numerical results manifest that distortion parameter, κ, will jeopardize the performance of considered systems, and increasing the value of estimation parameter, δ, conduces to improve the outage performance.
Tripod: Towards a Scalable, Efficient and Resilient Cloud Gateway

Cloud gateways are fundamental components of a cloud platform, where various network functions (e.g., L4/L7 load balancing, network address translation, stateful firewall, and SYN proxy) are deployed to process millions of connections and billions of packets. Providing <italic>high-performance</italic> and <italic>failure-resilient</italic> packet processing with a <italic>scalable traffic management</italic> mechanism is crucial to ensuring the quality of service of a cloud provider, and hence is of great importance. Many network functions nowadays are implemented in software with commodity servers for low cost and high flexibility. However, existing software-based network function frameworks oftentimes provide part of these features, while cannot satisfy all three requirements above simultaneously. To address these issues, in this paper, we introduce TRIPOD, a novel network function framework specialized for cloud gateways. Having identified the fundamental limitations of loosely coupling <italic>traffic</italic>, <italic>processing logic</italic> and <italic>state</italic>, TRIPOD jointly manages these three elements with the unique characteristics of cloud gateways, which is enabled by a <italic>simple, efficient traffic processing mechanism</italic>, and a <italic>high performance state management service</italic>. Adopting several effective techniques and optimizations, TRIPOD is able to achieve <italic>scalable traffic management</italic> (<100 flow rules for even ~Tbps traffic), <italic>high performance</italic> (reducing 40% of latency compared with state of the art) and <italic>failure resilience</italic> (similar packet/connection loss rate compared to state of the art), with reasonable overheads (less than 10% of the workload traffic) even under an extremely heavy traffic, making it a good fit for cloud gateways.
Long-Term Operational Data Analysis of an In-Service Wind Turbine DFIG

While wind turbine (WT) power capacities continue to increase and new offshore developments are being deployed, operation and maintenance (O&M) costs continue to rise, becoming the center of attention in the wind energy sector. The electric generator is among the top three contributors to failure rates and downtime of WTs, where the doubly fed induction generator (DFIG) is the dominant technology among variable speed WTs. Thus, the early detection of generator faults, which can be achieved through predictive maintenance, is vital in order to reduce O&M costs. The goal of this paper is to analyze a long-term monitoring campaign of an in-service WT equipped with a DFIG. A novel method named the harmonic order tracking analysis is used with two main objectives: first, to facilitate the data interpretation for non-trained maintenance personnel, and second, to reduce the amount of data that must be stored and transferred for the diagnosis of the DFIG. This method is applied and validated for the first time on an operating WT.
Successful User-Centred Design for Tablet PC: A Conceptual Framework

The use of tablet PCs in education enhances the learning process of students. However, the main components of the human–machine system, such as the user characteristics, task analysis, environments and tools (tablet PC specifications) of non-English tablet PC applications have received little attention. This article proposes a conceptual framework of user-centred design for tablet PCs. The user interface design will be improved through simplified user-centred design solutions. By applying this concept, novice designers and developers can better understand the essential components of the user-interface design process. This is illustrated with Arabic language as a case study.
Asynchronous Saddle Point Algorithm for Stochastic Optimization in Heterogeneous Networks

We consider expected risk minimization in multi-agent systems comprised of distinct subsets of agents operating without a common time scale. Each individual in the network is charged with minimizing the global objective function, which is an average of sum of the statistical average loss function of each agent in the network. Since agents are not assumed to observe data from identical distributions, the hypothesis that all agents seek a common action is violated, and thus the hypothesis upon that consensus constraints are formulated is violated. Thus, we consider nonlinear network proximity constraints, which incentivize nearby nodes to make decisions that are close to one another but do not necessarily coincide. Moreover, agents are not assumed to receive their sequentially arriving observations on a common time index, and thus seek to learn in an asynchronous manner. An asynchronous stochastic variant of the Arrow–Hurwicz saddle point method is proposed to solve this problem that operates by alternating primal stochastic descent steps and Lagrange multiplier updates that penalize the discrepancies between agents. This tool leads to an implementation that allows for each agent to operate asynchronously with local information only and message passing with neighbors. Our main result establishes that the proposed method yields convergence in expectation both in terms of the primal sub-optimality and constraint violation to radii of sizes ${\mathcal O}(\sqrt{T})$ and ${\mathcal O}(T^{3/4})$, respectively. Empirical evaluation on an asynchronously operating wireless network that manages user channel interference through an adaptive communications pricing mechanism demonstrates that our theoretical results translates well to practice.
An Equal Component Power-Based Generalized Hybrid Carrier System

In this letter, we derive the basic conditions that should be met for the weighted signal suitable for the communication system and extend the weighted fractional Fourier transform-based classical hybrid carrier system to the generalized hybrid carrier system. Furthermore, a generalized hybrid carrier signal design is presented with equal component power. Compared with the classical hybrid carrier signal, the power distribution of the proposed system in the time–frequency plane is more uniform, which makes it more suitable for doubly selective channels. The simulation results show that over doubly selective channels, the generalized hybrid carrier signal with equal component power achieves better bit error rate performance than the classical hybrid carrier signal does.
A Multi-dimension Measurement Study of a Large Scale Campus WiFi Network

The growing trend of wireless devices and WiFi networks poses significant management challenges to network administrators. Characterizing WiFi user behavior and understanding WiFi network usage pattern are helpful to identify the management challenges so that network administrators could manage WiFi networks more efficiently. In this work, we collect comprehensive datasets, i.e., DHCP dataset, AAA dataset, SNMP dataset of ACs in a large campus WiFi network. We provide a detailed measurement study from multiple dimensions, i.e., server plane, temporal plane, spatial plane and traffic plane. We observe that the WiFi network under study is far from optimal. First, the phenomenon of IP waste is severe due to the isolation between DHCP server and AAA server. Second, current deployment of network infrastructure resources is based on network administrators' experience and it results in that the WiFi performance varies a lot across different areas. Furthermore, we also study the user behavior with different types of devices and in different kinds of buildings. Our observations indicate that the WiFi network could be improved and managed more efficiently from multiple dimensions. We believe that this measurement study is helpful for network administrators and researchers to understand more about large scale WiFi networks.
A Nutrient Content Estimation System of Buffet Menu using RGB-D Sensor

To estimate ingested nutritional quantity from a buffet meal in one dish, we have developed an estimation system by using RGB-D sensor. This system uses a RGB-D sensor to get both color image and depth image. First, we extract dish region from captured image. Then, we get height information of food region from dish surface to estimate food volume. By combining meal region separation and meal menu recognition of each region, we can estimate weight of each meal region. By using estimated meal type and its weight, we can estimate the ingested nutritional quantity. As a preliminary experimental result, we can estimate weight of each meal elements within 10% error rate.
Multi-Objective Multi-Task Learning on RNNLM for Speech Recognition

The cross entropy (CE) loss function is commonly adopted for neural network language model (NNLM) training. Although this criterion is largely successful, as evidenced by the quick advance of NNLM, minimizing CE only maximizes likelihood of training data. When training data is insufficient, the generalization power of the resulting LM is limited on test data. In this paper, we propose to integrate a pairwise ranking (PR) loss with the CE loss for multi-objective training on recurrent neural network language model (RNNLM). The PR loss emphasizes discrimination between target and non-target words and also reserves probabilities for low-frequency correct words, which complements the distribution learning role of the CE loss. Combining the two losses may therefore help improve the performance of RNNLM. In addition, we incorporate multi-task learning (MTL) into the proposed multi-objective learning to regularize the primary task of RNNLM by an auxiliary task of part-of-speech (POS) tagging. The proposed approach to RNNLM learning has been evaluated on two speech recognition tasks of WSJ and AMI with encouraging results achieved on word error rate reductions.
Improved Anomaly Detection in Low-Resolution and Noisy Whole-Slide Images using Transfer Learning

Whole-slide imaging (WSI) is one of the most recent technologies introduced in medical pathology practices. WSI images are created using a computerized system that scans, stitches and stores pathology specimen glass slides into digital images, which provide a multi-resolution pyramid construction of a huge gigabyte size due to the need for containing a high amount of tissue details. Therefore, digital WSI brings major challenges in data storage, image analysis and transmission (e.g. telepathology and interoperability). In this paper, we propose a computer-aided diagnosis (CAD) system to detect cancer anomalies in breast lymph node WSI images under low-resolution (LR) and noise conditions. In particular, we investigate a transfer-learning approach to find the scale mappings between WSI levels using partial least-square (PLS) regression. The learned scale mappings can be used to detect anomalies in LR images and hence reduce the computational cost of anomaly detection. Then, we explore the effect of different levels of noise on detection performance. We simulated different scenarios where WSI images are contaminated with Gaussian noise and several de-noising algorithms were applied, namely de-noising with PLS, Block Matching $3D (BM3D)$ and the combination of PLS and BM3D. We show that these de-noising algorithms can help reduce the noise severity on anomaly detection. For example, for noisy images with 0.8 noise standard deviation, these three algorithms improved the LR detection accuracy from 63.50% to 93.81%, 92.73%, and 97.51%, respectively. Our results lead to useful conclusions on how to handle whole slide images under scaling and noise conditions.