Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a Methodological Framework for Sequence Analysis in the Field of Self-Regulated Learning

In recent decades, conceptualizations and operationalizations of self-regulated learning (SRL) have shifted from SRL as an aptitude to SRL as an event. Alongside this shift, increased technological capability has introduced computer log files to the investigation of SRL, uncovering new research avenues. One such avenue investigates the time-related characteristics of SRL through learners’ behavioural sequences. Although sequence analysis is still relatively new in SRL research, other fields have fruitful traditions in its application and may serve as a basis for applications in the field of SRL. Ten years of investigating SRL through sequence analysis have produced a wide range of methodological approaches. While this variety of methods illustrates the diversity of opportunities, it also indicates the lack of consensus regarding the most appropriate approach. Since the introduction of sequences analysis in the field of SRL, researchers have emphasized the need for a methodological framework to guide its application. Yet, to date, no such framework has been proposed, hindering our progress through (1) transparent methods and (2) comparative studies to (3) empirical and ecological applications. To help overcome this issue, this manuscript aims to foster discussions of a methodological framework for the use of sequence analysis in SRL research. We first make a case for why such a framework is necessary; secondly, we propose a set of considerations which could serve as a starting point for the construction of a framework.
Types of market offers enabled by open source hardware

Data from 65 market offers of 27 companies was used to: i) classify market offers enabled by open source hardware into nine types; ii) identify the customer value propositions, profit formula elements, key resources, and key processes for each type of market offer; iii) compare market offers enabled by open source software and hardware; iv) compare market offers enabled by open source intellectual property cores (IP Cores) and printed circuit boards (PCBs); and v) provide an open source model that can be applied to both open source hardware and software. The results of this research suggest that: i) open source hardware market offers rely on highly-restrictive licenses, and are enabled by only two types of assets IP cores and PCBs; ii) two open source software market offer types were not found in the case of open source hardware subscription services and testing; iii) the reason for the differences between open source hardware and open source software market offers is the tangible (physical) nature of hardware products; iv) the reasons for the differences between open source IP core market offers, and open source PCB market offers are related with differences in their manufacturing processes. This research contributes towards the development of a more general open source business framework that can be applied across different domains (e.g., software, hardware, content).
Can Passenger Flow be Explained by Network Topology in Public Transport

It has been rarely investigated in the field of public transport whether passenger ow can be explained by network topology. Based on the rich data sets from The Hague, The Netherlands, we conduct this study try- ing to shed light upon this question. The relation between passenger ow and topological measures in different public transport network representations are investigated in detail. Our preliminary results show promising evidence that the passenger ow is indeed correlated to topological measures in the case study network. Several linear regression models are also constructed to quan- tify the explanatory power of these topological measures.
A Survey of Spatial Crowdsourcing

Widespread use of advanced mobile devices has led to the emergence of a new class of crowdsourcing called spatial crowdsourcing. Spatial crowdsourcing advances the potential of a crowd to perform tasks related to real-world scenarios involving physical locations, which were not feasible with conventional crowdsourcing methods. The main feature of spatial crowdsourcing is the presence of spatial tasks that require workers to be physically present at a particular location for task fulfillment. Research related to this new paradigm has gained momentum in recent years, necessitating a comprehensive survey to offer a bird’s-eye view of the current state of spatial crowdsourcing literature. In this article, we discuss the spatial crowdsourcing infrastructure and identify the fundamental differences between spatial and conventional crowdsourcing. Furthermore, we provide a comprehensive view of the existing literature by introducing a taxonomy, elucidate the issues/challenges faced by different components of spatial crowdsourcing, and suggest potential research directions for the future.
A Glossary for
Research on Human Crowd Dynamics

This article presents a glossary of terms that are frequently used in research on human crowds. This topic is inherently multidisciplinary as it includes work in and across computer science, engineering, mathematics, physics, psychology and social science, for example. We do not view the glossary presented here as a collection of finalised and formal definitions. Instead, we suggest it is a snapshot of current views and the starting point of an ongoing process that we hope will be useful in providing some guidance on the use of terminology to develop a mutual understanding across disciplines. The glossary was developed collaboratively during a multidisciplinary meeting. We deliberately allow several definitions of terms, to reflect the confluence of disciplines in the field. This also reflects the fact not all contributors necessarily agree with all definitions in this glossary.
In-Block Level Redundancy Management for Flash Storage System

Recently, the density of Flash memory has dramatically increased by moving to smaller geometries and storing more bits per cell with enhanced memory technologies. However, with the density increasing rapidly, the error rate of Flash memory is also increasing rapidly. To improve the reliability issue of upcoming flash memory, usually, redundancy techniques were adopted in both of architecture and operations for flash memory based systems. For outside the flash memory page, several reliability schemes were proposed which employ Redundant Array of Inexpensive Disks (RAID). In this paper, we propose in-block level redundancy scheme for providing reliability of flash memory while minimizing maintaining overhead of the redundancy. In the proposed scheme, the redundancy is generated in a flash memory block level. During the programming stage of a flash block, the redundancy is kept in Dram memory. If a block is exhausted with last-1 page to record incoming data, the in-keeping redundancy is flushed to last page of the block. By doing this, block level redundancy is maintained with minimal overhead.
Efficient Contour Computation of Group-Based Skyline

Skyline, aiming at finding a Pareto optimal subset of points in a multi-dimensional dataset, has gained great interest due to its extensive use for multi-criteria analysis and decision making. The skyline consists of all points that are not dominated by any other points. It is a candidate set of the optimal solution, which depends on a specific evaluation criterion for optimum. However, conventional skyline queries, which return individual points, are inadequate in group querying case since optimal combinations are required. To address this gap, we study the skyline computation in the group level and propose efficient methods to find the Group-based skyline (G-skyline). For computing the front <inline-formula><tex-math notation="LaTeX">$l$</tex-math><alternatives><mml:math><mml:mi>l</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq1-2905239.gif"/></alternatives></inline-formula> skyline layers, we lay out an efficient approach that does the search concurrently on each dimension and investigates each point in the subspace. After that, we present a novel structure to construct the G-skyline with a queue of combinations of the first-layer points. We further demonstrate that the G-skyline is a complete candidate set of top-<inline-formula><tex-math notation="LaTeX">$l$</tex-math><alternatives><mml:math><mml:mi>l</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq2-2905239.gif"/></alternatives></inline-formula> solutions, which is the main superiority over previous group-based skyline definitions. However, as G-skyline is complete, it contains a large number of groups which can make it impractical. To represent the “contour” of the G-skyline, we define the Representative G-skyline (RG-skyline). Then, we propose a Group-based clustering (G-clustering) algorithm to find out RG-skyline groups. Experimental results show that our algorithms are several orders of magnitude faster than the previous work.
Learning Distilled Graph for Large-Scale Social Network Data Clustering

Spectral analysis is critical in social network analysis. As a vital step of the spectral analysis, the graph construction in many existing works utilizes content data only. Unfortunately, the content data often consists of noisy, sparse, and redundant features, which makes the resulting graph unstable and unreliable. In practice, besides the content data, social network data also contain link information, which provides additional information for graph construction. Some of previous works utilize the link data. However, the link data is often incomplete, which makes the resulting graph incomplete. To address these issues, we propose a novel Distilled Graph Clustering (DGC) method. It pursuits a distilled graph based on both the content data and the link data. The proposed algorithm alternates between two steps: in the feature selection step, it finds the most representative feature subset w.r. t. an intermediate graph initialized with link data; in graph distillation step, the proposed method updates and refines the graph based on only the selected features. The final resulting graph, which is referred to as the distilled graph, is then utilized for spectral clustering on the large-scale social network data. Extensive experiments demonstrate the superiority of the proposed method.
Eliminating Noise in the Matrix Profile

As companies are increasingly measuring their products and services, the amount of time series data is rising and techniques to extract usable information are needed. One recently developed data mining technique for time series is the Matrix Profile. It consists of the smallest z-normalized Euclidean distance of each subsequence of a time series to all other subsequences of another series. It has been used for motif and discord discovery, for segmentation and as building block for other techniques. One side effect of the z-normalization used is that small fluctuations on flat signals are upscaled. This can lead to high and unintuitive distances for very similar subsequences from noisy data. We determined an analytic method to estimate and remove the effects of this noise, adding only a single, intuitive parameter to the calculation of the Matrix Profile. This paper explains our method and demonstrates it by performing discord discovery on the Numenta Anomaly Benchmark and by segmenting the PAMAP2 activity dataset. We find that our technique results in a more intuitive Matrix Profile and provides improved results in both usecases for series containing many flat, noisy subsequences. Since our technique is an extension of the Matrix Profile, it can be applied to any of the various tasks that could be solved by it, improving results where data contains flat and noisy sequences.
Resource Allocation Techniques forNon-Orthogonal Multiple AccessScheme for 5G and Beyond WirelessNetworks

The exponential growth of wireless networks and the number of connected devices as well as the emergence of new multimedia-based services have resulted in growing demands for high data-rate communications, and a spectrum crisis. Hence, new approaches are required for better utilization of spectrum and to address the high data- rate requirements in future wireless communication systems. Non-orthogonal multiple access (NOMA) has been envisioned as a promising multiple access technique for 5G and beyond wireless networks due to its potential to achieve high spectral efficiency (SE) and energy efficiency (EE) as well as to provide massive connectivity in supporting the proliferation of Internet of Things. In NOMA, multiple users can share the same wireless resources by applying superposition coding (SC) and power domain multi- plexing at the transmitter and employing successive interference cancellation (SIC) technique at the receiver for multi-user detection. NOMA outperforms conventional orthogonal multiple access (OMA) by simultaneously sharing the available communication resources between all users via the power domain multiplexing which offers a significant performance gain in terms of SE. In this thesis, several resource allocation problems have been addressed in NOMA based communication systems, in order to improve network performance in terms of power consumption, fairness and EE. In particular, the NOMA scheme has been studied in multiple-input-single-output transmissions where transmit beamformers are designed to satisfy quality of service using convex optimization techniques. To incorporate the channel uncertainties in beamforming design, robust schemes are proposed based on the worst-case design and the outage probabilistic-based design. Finally, the EE is investigated for non-clustering and clustering NOMA schemes with imperfect channel state information. To eliminate the interference between different clusters, zero-forcing beamformers are employed at the base station. Theoretical analysis and algorithmic solutions are derived and the performance of all these schemes has been verified using simulation results.
Developing Post-Occupancy Evaluation Using Value Engineering in the Higher Education Buildings

The research studies the evaluation of the higher education buildings after operating and using because the periodic maintenance works that are being conducted on these buildings, which are being carried out without reviewing the functional performance or following a scientific methodology the post-occupancy evaluation. Therefore, the research aims to conclude and formulate a methodology for developing and improving post-occupancy evaluation and trying to increase its efficiency after linking with value engineering to improve development plans. Consequently, during applying the post-occupancy evaluation framework that will depend on value engineering to benefit from the functional analysis stage as the most significant stages for analysing and improving functions. Then the field and practical study of the deduced methodology that will be investigated by the maintenance and development teams and specialists to measure and evaluate the significance of the methodology elements and the appropriateness of its mechanism in developing the higher education buildings. In addition to it will be applied in these projects as a practical study to show the new potentials of the deduced methodology. Therefore, the deduced methodology will improve the functional performance of the buildings and the educational spaces and be involved in development plans.
Fuzzy based PID Controller for Active Queue Management in TCP Networks

-A robust Active Queue Management (AQM) scheme based on fuzzy PID controller is introduced. In the TCP network ,AQM is important to regulate the queue length by passing or dropping the packets at the intermediate routers under dynamically changing network situations. The hybrid controller are used to reduce packet loss and improve network utilization in TCP/IP networks. Simulation and real time implementations will be carried out to demonstrate the effectiveness of the proposed method than the existing
Flock Patterns When Pigeons Fly over Terrain with Different Properties

The way in which flocks are organized affects the ability of birds to perceive the landscape over which they fly: if the pattern of the flock changes, then the generalized perception of the terrain over which birds fly will also change. In this paper the features of dynamic spatial organization of pigeons in a flock during flights over landscapes with different characteristics were studied based on the analysis of GPS tracks of birds. The ways of group flight were revealed for typical situations, such as survey of unfamiliar terrain or flight home from remote sites. The spatial distribution of distances between pairs of individual birds and directions of movement were calculated, and then related to the features of terrain over which flights were occurred. The data analysis was performed based on comparison of flock patterns during group flights over terrain of distinct types (sea coast, urban and countryside terrain, and natural landscape). The spatial data was processed using the geographic information system QGIS.
A Fully Object-space Approach for Full-reference Visual Quality Assessment of Static and Animated 3D Meshes

3D mesh models are exposed to several geometric operations such as simplification and compression. Several metrics for evaluating the perceived quality of 3D meshes have already been developed. However, most of these metrics do not handle animation and they measure the global quality. Therefore, a full-reference perceptual error metric is proposed to estimate the detectability of local artifacts on animated meshes. This is a bottom-up approach in which spatial and temporal sensitivity models of the human visual system are integrated. The proposed method directly operates in 3D model space and generates a 3D probability map that estimates the visibility of distortions on each vertex throughout the animation sequence. We have also tested the success of our metric on public datasets and compared the results to other metrics. These results reveal a promising correlation between our metric and human perception.
Greedy and IHT Algorithms for Non-convex Optimization with Monotone Costs of Non-zeros

Non-convex optimization methods, such as greedy-style algorithms and iterative hard thresholding (IHT), for $\ell_0$-constrained minimization have been extensively studied thanks to their high empirical performances and strong guarantees. However, few works have considered non-convex optimization with general non-zero patterns; this is unfortunate since various non-zero patterns are quite common in practice. In this paper, we consider the case where non-zero patterns are specified by monotone set functions. We first prove an approximation guarantee of a cost-benefit greedy (CBG) algorithm by using the {\it weak submodularity} of the problem. We then consider an IHT-style algorithm, whose projection step uses CBG, and prove its convergence guarantee. We also provide many applications and experimental results that confirm the advantages of the algorithms introduced.
Spectral Multi-Dimensional Scaling using Biharmonic Distance

The spectral property of the Laplace-Beltrami operator has become relevant in shape analysis. One of the numerous methods that employ the strength of Laplace-Beltrami operator eigen-properties in shape analysis is the spectral multidimensional scaling which maps the MDS problem into the eigenspace of its LaplaceBeltrami operator. Using the biharmonic distance we show a further reduction in the complexities of the canonical form of shapes making similarities and dissimilarities of isometric shapes more efficiently computed. With the theoretical sound biharmonic distance we embed the intrinsic property of a given shape into a Euclidean metric space. Utilizing the farthest-point sampling strategy to select a subset of sampled points, we combine the potency of the spectral multidimensional scaling with global awareness of the biharmonic distance operator to propose an approach which embeds canonical forms images that shows further “resemblance” between isometric shapes. Experimental result shows an efficient and effective approximation with both distinctive local features and yet a robust global property of both the model and probe shapes. In comparison to a recent state-of-the-art work, the proposed approach can achieve comparable or even better results and have practical computational efficiency as well.
Shape Modification from Endoscope Images by Regression Analysis

The VBW (Vogel-Breuß-Weickert) model is proposed as a method to recover 3-D shape under point light source illumination and perspective projection. However, the VBW model recovers relative, not absolute, shape. Here, shape modification is introduced to recover the exact shape. Modification is applied to the output of the VBW model. First, a local brightest point is used to estimate the reflectance parameter from two images obtained with movement of the endoscope camera in depth. A Lambertian sphere image is generated using the estimated reflectance parameter and VBW model is applied for a sphere. Regression analysis is introduced to improve the surface gradients, where linear coefficients can be obtained using true values of gradient parameters with a generated sphere. Depth can then be recovered using the modified gradient parameters. Performance of the proposed approach is confirmed via computer simulation and real experiment.
Defending N-Version Programming Service Components against Co-Resident Attacks in IoT Cloud Systems

The real innovation of Internet of Things (IoT) can be spurred only when being combined with cloud computing, a paradigm that allows numerous users to simultaneously access configurable resources and services. However, serious vulnerability concerns have arisen from the virtual machine co-resident architecture of the IoT cloud. Specifically, co-resident attacks can be launched, where an attacker can access and corrupt a user's sensitive data/software by co-locating their virtual machines on the same physical server. Various solutions have been suggested in literature to mitigate negative effects of the co-resident attacks in the cloud environment. However, to the best of our knowledge no work has been performed for studying co-resident attacks in cloud systems with N-version programming (NVP), a popular redundancy technique for enhancing survivability of critical cloud service components. This paper makes original contributions by modeling IoT cloud system services implementing the NVP component redundancy, and evaluating the corruption probability of the NVP service component. Further, users’ policies on choosing the optimal number of service component versions are investigated through formulating and solving a new set of optimization problems with the objective to minimize the expected cost of losses of a cloud service provider. As demonstrated through examples, these policies can effectively help defend the NVP service component against the co-resident attacks in the cloud system.