dataesr/scientific-paragraphs-categorization huggingface dataset:
To assess goal achievement for the whole cohort, data was aggregated over the 3 days preceding each module (57 participants x 3 days, Fig. 4 ). Missing data from the activity tracker dataset was imputed using regression analysis. Differences between groups and gender with regards to anthropometry and physical tness were analysed using a two-way ANOVA (group x gender). Goals and achievements data were analysed using a Pearson Chi-square statistical test for goodness-of-t. A one-way ANOVA (gender) with repeated measures was used to test goals progression during the program. Pre-post-MVPA was analysed in 23 control and 41 iEngage© participants GENEActiv datasets. A two-way ANOVA with repeated measures (gender x group) was used to test the impact of iEngage on PA as assessed by GENEActiv sensor before and after the iEngage program.
Acknowledgements iEngage© (IP rights Agreement ( 2018-066 ) # CT24145 ) was co-created by  Professor Corinne Caillaud  and  Associate Professor Kalina Yacef  ( University of Sydney, Sydney, Australia ),  Associate Professor Olivier Galy  ( University of New Caledonia ) and  Gael Clerc  (bepatient). Study design and implementation: CC, OG, KY and SL. Data collection: SL, KY, CC, CD. Data management and analysis: CC, SL, CD. Manuscript: all authors. The authors thank post graduate students  Emilie Pau que (University of New Caledonia ) and honours student  Tricia Lam  ( University of Sydney ) who contributed to data collections and implementation of the program.
Social networks One of the first examples of social networks is the Zachary karate club, representing the friendships between the 74 members of a karate club (see Figure  1. 1). During the two-year study  (Zachary, 1977) , the club members split into two groups after a feud occurred between the main instructor and the club's president. This dispute makes the dataset extremely popular in the network science community. We would like to answer the intriguing question: can one predict the resulting two groups based only on the friendship graph? This lays the ground for the problem of community detection, which we will discuss in detail in Chapter 4. Data concerning social networks of real-life social relationships (acquaintances, interactions) are notoriously hard to gather. Indeed, questionnaires are physical and take time to analyse, making the collection from a large number of individuals difficult.
Moreover, they are prone to human error and personal interpretation. Fortunately, it is much easier to gather examples of datasets in online social networks. Thus, it is not surprising that most examples of datasets of large social networks come from online social networks or web-blogs. One such example is the LiveJournal dataset. LiveJournal is an online blogging community in which users can befriend each other. The users are also free to create groups which other users can join. These groups can be considered as ground-truth communities. Figure  1. 2 shows the LiveJournal friendship network restricted to the two largest communities. Adamic and Glance, 2005  studied the linking patterns of political bloggers during the U. S. Presidential Election of 2004. They considered 1494 blogs in total, 759 liberal and 735 conservative, and constructed the interactions by identifying whether one blog references another blog.
Face-to-face interaction networks The high-school datasets represent close proximity encounters between students in a French high school. Student-to-student interactions are recorded every 20 seconds through wearable sensors, and the experiments span several school days. The same experiment was performed in three consecutive years  (Fournet and Barrat, 2014; Mastrandrea et al. , 2015) , and the dimensions of each dataset are given in Table  1. 1. We also plot in Figure  1. 4 the weighted graph for the year 2013, where the weights correspond to the number of interactions recorded between two students. Finally, as each student belongs to one class, the question of recovering the classes based on the temporal interactions arises. We will study this dataset in more detail in Chapter 6. We note that time aggregation can result in a loss of important information, which could otherwise be inferred from the dataset's temporal nature.
Communication networks Communication networks constitute an important class, which includes various transportation networks (roads, airplane maps, etc. ) as well as phone and messaging communications between individuals. The Enron email dataset 1 contains approximately 500,000 emails from about 150 employees (mostly from the senior management team) of the Enron company (now bankrupt). Emails were recovered by the Federal Energy Regulatory Commission during the fraud investigation. This dataset was made public and has been used by many researchers for various information processing tasks, such as document classification or social network analysis  (Carley and Skillicorn, 2005). The Copenhagen networks study dataset  (Sapiezynski et al. , 2019)  records the interaction of 700 university students over 4 weeks, including close-proximity interactions, phone calls and Facebook friendships.
(Lusseau et al. , 2003). Colours show how the network split when a dolphin left the group. PubMed  (Namata et al. , 2012)  datasets. This procedure can be extended to other domains. For example, using IMDB data one can produce a network of movie actors, where two actors are connected if they starred in a movie together. Web-graph represents another example of information networks. It is constructed by linking webpage A to webpage B (usually with a directed link) if webpage A cites webpage B. Several Web-graph and Wikipedia networks are available from the NetSet database 2 and the Laboratory for Web Algorithmics (LAW). 3.
Geometrically defined network topologies In machine learning tasks, data often come as a matrix X = x 1 ,. , x n ∈ R m×n , where n is the number of data points and m is the dimension of each data point (e. g. , the number of features). To perform data analysis with the help of a network, 2. https://netset. telecom-paris. f r/index. html 3. https://law. di. unimi. it/datasets. php the topology and weights of the graph must be built from the data. A common way to define the weight of an edge connecting vertices i and j is by using a Gaussian kernel with thresholding w ij =    exp - x i -x j 2 τ 2 , if x i -x j 2 ≤ κ, 0, otherwise, where τ and κ are tunable parameters and • is a distance between data points. In particular, the cutoff parameter κ prevents having a too dense network with many small-weight edges.
Another common method is to connect each vertex to its K -nearest neighbours. We refer to  (Grady and Polimeni, 2010, Chapter 4 ) and  (Stankovic et al. , 2020)  for the description of other methods for data similarity network construction. The MNIST database  (LeCun et al. , 1998)  is a database of 70,000 handwritten digits commonly used as a benchmark in machine learning. Figure  1. 7 presents a network built from 300 pictures of digits 0, 1, 2 using the Gaussian kernel as a weight function. More precisely, we first compute a K -nearest neighbour graph (K = 8) with weights w ij =    exp - 4 x i -x j 2 τ i , if x j is among K nearest neighbours of x i , 0, otherwise, where τ i represents the distance between x i and its K th-nearest neighbour. The weight matrix is finally symmetrised by replacing W with 1 2 (W + W T ).
Semi-supervised Learning Labelling data is a laborious and expensive process. Therefore, in many datasets the amount of labelled data is small and standard supervised machine learning methods either lead to a significant amount of errors or are not applicable at all. Fortunately, a graph-based semi-supervised learning method can help in such situations  (Chapelle et al. , 2006). The main idea behind graph-based semi-supervised learning is first to construct a graph on the data points, where a link between two data points indicate a strong relationship between these points. And then, one can use a similarity measure on the graph to assign unlabelled data points to the classes defined by the labelled data points. One example of a similarity measure is given by the Personalized PageRank, see  (Avrachenkov et al.
umich. edu/~mejn/netdata/, including the popular Zachary Karate Club  (Zachary, 1977) , an interaction network between dolphins  (Lusseau et al. , 2003)  and the political blog data set  (Adamic and Glance, 2005). • The Linqs webpage https://linqs. soe. ucsc. edu/data hosts some data set, including Cora, Citeseer, Pubmed, and WebKB  (Lu and Getoor, 2003). • The netset webpage https://netset. telecom-paris. f r/ hosts several data sets, including graphs with links between wikipedia articles. • Finally, the Stanford Large Network Dataset Collection (https://snap. stanf ord. edu/data/) hosts a wide sample of larger networks. Using synthetic networks is also common to assess the validity of community detection algorithms. A widely used random graph model with community structure is the Stochastic Block Model (SBM) and its degree-corrected variant (see Section 2.
Spectral methods and dangling trees Let us analyse the failure of spectral clustering on the political blogs data set. Figure  4. 3 shows the values of eigenvector components of L associated to the second and third smallest eigenvalues. We see that the entries of the second eigenvector are localised over a few nodes. Moreover, those nodes are associated to a dangling tree, and do not correspond to a meaningful community structure (see Figure  4. 3c). On the contrary, the entries of the third eigenvector correspond to the correct community structure. In fact, using this eigenvector for clustering would lead to an accuracy of 95%. Figure  4. 3 shows that for this dataset, the good eigenvector for clustering is the third one, while the second eigenvector is concentrated around low degree nodes,   forming a dangling tree.
We select 1000 images from MNIST, representing digits 4 and 9, and construct a k-nearest neighbors (k = 8) similarity graph with Gausian weights. The digits 4 and 9 form the hardest digit pair to distinguish. We plot in Figure  4. 5 the accuracy obtained by spectral clustering as a function of the eigenvector order. We emphasis the fact that, unlinke the politicl blog data set, this is not an artifact due to dangling trees. We plot in Figure  4. 6 the predicted clusters using the eigenvectors associated to the second and smallest eigenvalues of the graph's normalized Laplacian, and compare them with the true clusters. We notice that the predicted clusters are of balanced sizes. We also note that the NCut of the true labels is 3. 8, while the NCut of the predicted labels  associated with the prediction using the second (resp.
Discussion Unlike spectral methods, modularity based methods do not require the a priori knowledge of the number of blocks. Moreover, the speed difference observed in practice makes the greedy method (Algorithm 6) out of the competition. Furthermore, it has also been empirically observed that Louvain returns partitions with high modularity. Table  4. 4 gives the performance of Louvain method on real data sets. In particular, we see that Louvain has the tendency to predict a high number of communities, but with a modularity higher than the ground truth partition. We plot in Figures 4. 8 and 4. 9 the predicted communities by Louvain on the karate club and the political blogs datasets, respectively. By comparing to the ground truth, we observe that Louvain split the ground truth communities into smaller communities.
Numerical Performance of the Laplacian-based Methods Choice of hyper-parameter α for Label Spreading We first investigate the effect of α on the classification performances. We choose two datasets for which we saw that unsupervised spectral clustering failed: DBLP and Cora. We let 2% of the nodes be labelled by the oracle, and we plot in Figure  5. 1 (blue curve) the accuracy as a function of α. We see that the accuracy increased when α increases, but suddenly dropped if α becomes too close to one. We also notice that the best choice of α can be made by looking at the modularity of the predicted partition (red curve in Figure  5. 1), as the modularity closely follows the accuracy. Noisy oracle We now study the effect of noise on classification performance. We keep the same two datasets, now with 5% labelled nodes.
Numerical Experiments To assess the performance of Poisson learning in a regime with extremely low amount of labelled nodes, we reproduce the results of  Calder et al. , 2020. They consider MNIST (LeCun et al. , 1998)  and Fashion-MNIST  (Xiao et al. , 2017)  datasets, on which they trained auto-encoders to extract important features from the data. More precisely, they used variational auto-encoders with 3 fully connected layers of sizes  (784,400,20) and (784,400,30) , respectively, followed by a symmetrically defined decoder  (Kingma and Welling, 2014). The auto-encoder was trained for 100 epochs on each data set. Then, a 10-nearest neighbours graph is build with Gaussian weights w ij = exp -4 x i -x j 2 /σ 2 i , where x i is the latent variables for image i and σ i is the distance between x i and its 10-nearest neighbour.
Community Detection in Temporal Networks Previous chapters focus on the study of static interactions, represented by a binary number or a positive weight. Nevertheless, in many application domains, interactions vary over time. The longitudinal nature of such datasets calls for replacing classical graph-based models with temporal network models represented by tensors  (Holme and Saramäki, 2012; Kivelä et al. , 2014). We note that taking into account the temporal aspects not carefully, e. g. , by aggregating or smoothing the temporal data along the time axis, can lead to a loss of valuable information. The problem of community detection in temporal networks has recently attracted a considerable amount of attention from the scientific community. While it led to many interesting results, it also led to an explosion of disparate terminologies and algorithms.
, 2015). We presented these data sets in the introduction. In particular, nodes correspond to students, interactions to close-proximity encounters, and communities to classes, with dimensions given in Table  1. 1. We make a hypothesis that the temporal characteristics of the interactions are similar each year. We then use the 2011 data set to estimate the transition probability matrices P and Q, and use these for clustering the 2012 and 2013 data sets. We assume that θ i = 1 (no degree correction). A standard estimator of Markov chain transition probability matrices  (Billingsley, 1961)  gives P = 0. 9992 0. 0008 0. 37 0. 63 and Q = 0. 999967 3. 3 × 10 -5 0. 48 0. 52. Using (6. 17), leads to α = 2. 9 and β = 0. 18. We observe in Figure  6. 5(b) that this choice of parameters gives a better accuracy on the 2013 data set than simply applying spectral clustering on the time-aggregated graph (α = β = 1).
The solution of this relaxation is determined by mimicking the reasoning of Section 5. 4. 2. In particular, by denoting the eigendecomposition of  (6. 22)  where γ * is the smallest solution of the explicit secular equation  (Gander et al. , 1989 )  (6. 23) This leads to Algorithm 19. In Figure  6. 6, we choose λ t to be constant and equal to 0. 5, while Figure  6. 7 explores other possible values. We observe that when λ t is equal to a constant in the interval [0. 1, 1], Algorithm 19 outputs similar performances. On the other hand, when λ becomes too large, Algorithm 19 gives too much importance to the oracle, and the accuracy becomes worse. In practice, the choice of the parameters λ t could be optimised from the data, e. g. , based on η or on the transition matrices P and Q. Moreover, it would be intuitive to increase λ t with t, as the confidence in the oracle is higher when more temporal data is available.
Real-world Network: DBLP We will now compare different sampling methods on the DBLP data set (n = 317, 080 nodes and m = 1, 049, 866 edges). In Figure  7. 4, we estimate the average degree, i. e. , f (v) = d (v). We also estimate the number of nodes with degree larger than 50 by considering f (v) = 1(d (v) ≥ 50) in Figure  7. 5. In both cases, we observe that sampling using Metropolis-Hastings produces larger variance than RDS or uniform sampling.
The Internet of things concept empowered by low-cost sensor technologies and headless computers has upscaled the applicability of vibration monitoring systems in recent years. Raspberry Shake devices are among those systems, constituting a crowdsourcing framework and forming a worldwide seismic network of over a thousand nodes. While Raspberry Shake devices have been proven to densify seismograph arrays efficiently, their potential for structural health monitoring (SHM) is still unknown and is open to discovery. This paper presents recent findings from existing buildings located in Bucharest (Romania) equipped with Raspberry Shake 4D (RS4D) devices, whose signal recorded under multiple seismic events has been analyzed using different modal identification algorithms. The obtained results show that RS4D modules can capture the building vibration behavior despite the short-duration and low-amplitude excitation sources.
Technologies such as micro-electro-mechanical systems (MEMS) enabled the production of cost-efficient accelerometers and other sensory devices contributing to the new age in the SHM discipline  [5]. These advancements combined with the modern data science frontiers promote spatially dense, scalable, and affordable SHM solutions with a compromise on the merits of high-fidelity instrumentation  [6]. MEMS technology has brought the sensor sizes to very small dimensions within millimeters. This has allowed the embedding of sensors within many industrial and consumer-grade devices (e. g. , smartphones) and deploying next-generation sensing modules empowered by low-cost programmable boards (e. g. , Arduino, Raspberry Pi). Smartphones, for example, provide vibration measurement alternatives that could be used for experimental modal analysis with support from heterogeneous sensory and crowdenabled environments  [7].
In line with the smart sensor advancements, Internet of things (IoT) protocols have assured everyday connectivity between cloud and operating sensor nodes  [8, 9]. IoT nodes have found deployable forms with the contribution of low-cost programmable boards and MEMS sensors for many engineering applications, for example, SHM as well as engineering seismology  [10]. One main initiative in seismology is the advent of cost-efficient and crowdsourcing-based seismometer networks, and Raspberry-Pi-based Raspberry Shake products have risen as one of the most prominent solutions integrating different sensors within the same module (e. g. , multiaxial accelerometers, geophones, and on-board processing, computation, and communication)  [11]. While Raspberry Shake devices spread around the world rapidly to form a new generation seismic network, their capabilities as SHM instrumentation for modal identification and damage detection are yet to be discovered  [12].
A focused review of the seismic instrumentation of structures in Romania, evolution, recent progress, and perspectives is presented in  [39]. Recent earthquakes occurring in the Vrancea intermediate-depth seismic region generated valuable datasets comprising real earthquake signals captured by RS4D devices. In this paper, the SHM methodologies presented in  [16]  are tested on RS4D data and comparisons are also made with the signals from high-fidelity broadband sensors installed in a benchmark structure. Even though no damage was identified due to the weak nature of the seismic signals, this paper provides evidence of acquiring modal characteristics of the buildings excited by the earthquakes. The rest of the paper is organized as follows: Section 2 introduces the RS4D modules and instrumentation deployed on multiple buildings situated in the testbed region; Section 3 investigates the dataset developed during the latest seismic activities in the region recorded from multiple buildings via RS4Ds; Section 4 briefs the identification techniques capturing the modal characteristics of building infrastructure; and finally, Section 5 presents the conclusive findings and further research directions.
Similar observations on instrumental self-noise are valid also for the sensors installed on the monitored buildings of the TURNkey project. The noise level was tested by using the McNamara and Buland methodology [47], based on data recorded during the quietest period since the deployment of the instruments. Five hours during a weekend night were selected, for the station located at the LAS 3rd basement. Being an office building, its activity during the weekend was reduced. In addition, the 1 May 2021 weekend was a National Holiday in Romania, thus many residents left Bucharest, contributing to further reductions in the city's ambient vibrations. With the reference to the standard low noise model (NLNM) and high noise model (NHNM) for seismic stations [48] plotted with black lines, Figure  3  shows the probabilistic power spectral density (PPSD) plots computed by using the Obspy routines  [49] , which are quite consistent with the ones present in the RS4D technical specification document  [50].
The five-hour interval was split into 19 data segments for which PPSD estimates were computed. Even though the vertical geophone is sensitive due to its low value of self-noise (Figure  3a ), during a strong EGM the geophone velocity sensor can be "saturated" or exceed the instrument's measurement limit. The integrated MEMS accelerometer should remain in scale during strong EGM, with the cost of a higher level of self-noise, as presented in Figure  3b. While for the velocity channel (Figure  3a ) all the analyzed segments fall below or slightly exceeds (for periods from 0. 4-0. 8 s) the NHNM limit, for the acceleration channel (Figure  3b ) the upper limit is exceeded on the entire period range of interest (0. 1-10 s), thus suggesting that the acceleration recordings are unreliable for ambient vibration analysis (as highlighted also in the next section) or small-amplitude Vrancea seismic events (M < 4.
5). However, the two types of sensors are complementary and proved to work well together. All stations were configured to forward data to the TURNkey platform server hosted at Gempa. The Common Acquisition Protocol Server (CAPS) was developed to fulfil the need to transfer multi-sensor data from the station to the data center (https://www. gempa. de/products/caps/) (accessed on 30 May 2022). In addition, the CAPS data compression module was implemented in the units and the data stream was rerouted to the Gempa CAPS system (Figure  4 ).
Broadband Sensors The Institute of Atomic Physics building (TURN) is located in Magurele, close to Bucharest. The building was completed in 1974, it was partially damaged by the 1977 earthquake, and it was retrofitted afterwards, in the 1990s. The monitoring system consists of three accelerometers installed in the basement, 6th floor and 10th floor. The three TSA-100S tri-axial accelerometers and a 24-bit Digitizer (iDAS) were installed in December 2013 and all the data are transmitted in real-time to the INFP's National Data Center, for the purposes of archiving and post-processing (Figure  5 ).