dataesr/scientific-paragraphs-categorization huggingface dataset:
Anomaly Detection: experimental results The classification algorithm has been tested over two of the considered datasets, namely the MIT1 and the EDH. The absence of the MIT2 is mainly due to the fact that this step requires a preliminary labeling of the trajectories into normal and abnormal, which makes sense only if abnormal trajectories can be identified in the dataset. In both cases, given the entire dataset D, the normal trajectory dataset D * has been randomly partitioned into three folds and one of these has been used for the learning phase. The remaining two folds have been mixed with the remaining trajectories (D \D * ) and are used to test the system. Finally, a cross-validation strategy has been adopted in order to obtain the results presented in Figure ??. These results present the area under curve (AUC) of the Receiver Operating Characteristic (ROC) curves computed on the MIT1 and the EDH using both our traditional Dirac Kernel (k ZSS ) and Weighted Dirac kernel (k W ZSS ) with an increasing number of zones (from 10 to 60).
Starting from the obtained results, the following consideration can be done: the number of zones |Z| does not strongly influence the performance of the proposed system. As a matter of fact, we can note that by using a Dirac Kernel in the MIT dataset (Table  5. 1e, line 1 and Figure  5. 1a) we achieve comparable performance with |Z| ranging from 10 to 40; the performance decreases with a higher number of zones (|Z| ranging from 50 to 70). This is mainly due to the fact that the system in this case pays in terms of generalization and it is not able to correctly classify those normal trajectories only slightly different from the ones included in the dataset. This consideration is also confirmed by the result obtained on the EDH (Table  5. 1e, line 2 and Figure  5. 1b), where the optimum number of zones ranges from 30 to 50.
Furthermore, we can note that in this case a low number of zones (|Z| = 10) does not guarantee good performance. This is mainly due to the different trajectories' distribution of the two considered datasets. In fact we observe in Figure  5. 15 that the trajectories in the MIT1 are located in a limited area of the image and are much more compact than in the EDH, where people can freely move in the entire image. For this reason, although the proposed scene partitioning algorithm is able to optimize the partition of the space, the limited number of zones seems to not give enough informative content because of the homogeneous distribution of the dataset. Finally, we can note that the introduction of the Weighted Dirac Kernel only slightly improves the performance in both the considered datasets.
However, its main advantage lies in the fact that this similarity evaluation is less sensitive to the different number of zones thanks to its nature: as a matter of fact, it also guarantees good performance with a low, as well as an high number of zones, since it also considers the proximity of zones. Furthermore, it is able to provide a better generalization than the Dirac Kernel, without paying in terms of false positive errors. This consideration is confirmed by the analysis of the AUCs, reported in Table  5. 1e: the improvement between the best and the worst case by using the Dirac Kernel is higher than 20%; for instance, for the MIT1, where we obtain 0. 88-0. 67 0. 88 = 0. 23. On the other hand, it is lower than 7% by using the Weighted Dirac Kernel, where, for instance, we obtain on the same dataset 0.
Query by sketch: experimental results Two different evaluations will be provided in this section, a quantitative and a qualitative one, in order to confirm the effectiveness of the method proposed for solving queries by skecth. Both these evaluations have been performed over the three datasets described in Section 5. 2. 1. The quantitative evaluation has been carried out by evaluating the improvement of the performance if compared to a brute-force approach. Among the entire datasets, 3000 trajectories have been randomly selected from each dataset for testing and the remaining have been used for training. In particular, the number of the trajectories used to build the k-d tree has been progressively increased in order to verify the performance of the proposed method with different size, as shown in Figure  5.
23. A k nearest neighbor search is performed and the number of trajectories analyzed by the algorithm, expressed in percentage with respect to the size of the considered dataset, has been evaluated. The results are shown in Figure  5. 23: as evident, the improvement is much more significant with big datasets: for instance, if we are interested in discovering the most similar trajectory (k = 1) in a dataset composed of 3000 trajectories, we only have to analyze 24% of it (approximatively 700 trajectories) in the MIT1 (Figure  5. 23a) and 11% of it (approximatively 300 trajectories) in the EDH and the MIT2 (Figure  5. 23b), resulting in a significant improvement of the overall performance. Furthermore, a simple Graphical User Interface has been designed in order to allow the user to solve a query by sketch.
Indexing and Retrieval Engine The storing and retrieving engine has been tested over both synthetic and real datasets. The former has been used to stress the proposed method with a very complex scenario, while the latter has been chosen in order to confirm the feasibility of our approach. The database has been implemented by storing the trajectories' data in Postgres using PostGIS; data are indexed using the standard bi-dimensional R-tree over Generalized Search Trees (GiST) indexes since, as highlighted in the specialized literature, this choice guarantees higher performance in case of spatial queries if compared with the PostGIS implementation of R-trees. In the next sections the obtained results will be presented: in particular, in Section 5. 3. 1 the graphical user interface designed for making easier the use of the human operators is presented; Section 5.
Experiments over Real Dataset The real dataset that we chose for our experimentation is the MIT Trajectory dataset (MIT1), already introduced in Section 5. 2. 1. The whole dataset, composed of approximately 4 * 10 4 trajectories with 109 points in each trajectory (on average), has been used. At loading time, each trajectory has been segmented using AreaM in = 1, so obtaining approximately 1. 92 * 10 6 segments with 24 points in each segment (on average). We conducted our experiments on a PC equipped with an Intel quad core CPU running at 2. 66 GHz, using the 64 bit version of the PostrgreSQL 9. 2 server and the 2. 1 release of PostGIS. Table  5. 27 shows QT DRSQ (in seconds) as D c varies. It is worth pointing out that QT DRSQ results from the sum of four terms: T 1 is the time needed to select the segments whose bounding box is completely inside the query box on each bi-dimensional plane (min selection), T 2 is the time needed to select the segments whose bounding box intersects the query box on each bi-dimensional plane (max selection), T 3 is the time to clip the segments (clipping) while T 4 is the time needed to extract the whole trajectory, so obtaining: T Q DRSQ = T 1 + T 2 + T 3 + T 4.
The dataset The experimental validation of the system has been carried out considering a typical audio surveillance application that requires the recognition of the following three classes of abnormal audio events: scream, broken glass and gunshot. From the user perspective, the system must raise an alarm in presence of an abnormal audio event, while the sounds produced by any other source should be recognized as belonging to the background noise class. To the best of our knowledge, there are no publicly available datasets for the benchmarking of audio surveillance applications. Thus, we constructed our own dataset of PCM audio clips sampled at 32 KHz frequency and with a resolution of 16 bits per sample. We collected a total of 650 audio clips, some of them recorded by us in different environment conditions and others selected from compilations of sounds usually used for movie effects.
In particular, we collected 271 different sounds that belong to the three classes of abnormal sounds defined above. Furthermore, in order to test the robustness of the proposed approach with respect to the presence of different kinds of background sounds both from indoor and outdoor environments, we collected and included into the dataset 379 audio clips representative of the following types of sound: silence and white gaussian noise, rain, whistles, crowded ambiance, vehicles, household appliances, bells, applauses and claps. All the audio clips have been recorded with an Axis P8221 Audio Module and an Axis T83 omnidirectional microphone for audio surveillance applications. A key requisite for an audio surveillance system is the ability to detect that an abnormal event (hereinafter foreground sound) occurs even when it is mixed with one or many kinds of background sounds and when its energy is only slightly higher or even comparable to the one of the background noise.
That means that a surveillance system must be able to detect abnormal events also for low values of the signal to noise ratio (SNR). Thus, in order to account for these necessities, we defined and adopted a procedure to create a new dataset of audio clips suitable for abnormal events detection and classification purposes. The audio clips from the original dataset have been normalized so that they have all the same overall energy, as follows: x(n) = x(n) 1 N N i=1 x(i) 2 , ( 5. 11) Before to start generating the dataset, the normalized audio clips were split in two groups comprising 70% and 30% of the total amount of sounds from the original dataset, respectively. We used the clips from the first group to build the training set and the ones from the second group to build the test set. Thus, we generated two sets of audio clips of about 3 minutes duration, by mixing foreground sounds with different combinations of a background sounds.
The procedure adopted to create a new audio clip is the same for both the training set and the test set and is explained in detail in the following. First, an environmental sound is created by mixing a number d ∈ {1, 2, 3} of sounds from the background class. The number d is randomly defined before the creation of every clip. Since the original background sound files have different durations, we replicated them in order to fit the duration of 3 minutes of the new audio clip to be created. Let us define as B j (n) = Once the environmental sound is created, a number N e of foreground events is randomly chosen from the original dataset and mixed with the environmental sound, in order to simulate the occurrence of an abnormal event in a real and complex environment. The foreground events are distanced each other by ∆t n seconds, whose value is uniformly distributed in the interval  [3.
5, 5]. A certain event from the original dataset occurs a number of times in the final dataset, but with different background noise in order to simulate the presence of a specific sound in different environment configurations. It is worth noting that in a real environment the source of a target event can be at different distances from the acquisition equipment resulting in signals with various values of the SNR. Thus, when a foreground sound is mixed with the environmental sound, the energy of the foreground sound is amplified or attenuated in order to guarantee a specific value SN R p , p = 1,. , 6 of the SNR from the set ζ = {5dB, 10dB, 15dB, 20dB, 25dB, 30dB} for the target sound. The rule for the construction of the audio clip considering foreground events at a certain SNR value is defined as follow: y p j (n) = Ne i=1 B j (n) ⊕ [si,ei] A p x i (n) , (5.
12) where A p = 10 SN R p /20 rms(B j (n)) rms(x i (n)). (5. 13) The amplification (or attenuation) coefficient A p depends on the specific value SN R p and on the root mean square values (rms) of the environmental sound and of the foreground sound. With ⊕ [si,ei] we define an operator that allows to mix the signal A p x i (n) with the signal B j (n) in the interval delimited by [s i , e i ] (s i and e i are the starting and ending points of the target sounds within the clip y p j (n), respectively). The final dataset consists of a training set and a test set that contain, respectively, 396 and 184 audio clips of about 3 minutes. The total duration of the sounds in the dataset is about 20 hours for the training set and about 9 hours for the test set. Given a specific sequence S = e 1 , e 2 ,.
, e m of foreground events, six versions of the audio clip are created. Such versions differ each other for the value SN R p of the desired SNR at which the foreground events occur. In the following we will refer to the different classes with the abbreviations BG for broken glass, GS for gunshot, S for scream and BN for background noise. The training set of the experimental dataset is composed by 700 events for each target class. Each event is present at six different SNR values, for a total of 4200 events for each class. In the same way, in the training set 300 events per class are provided in six versions, for a total of 1800 events for each foreground class. In Table  5. 30 we report a summary of the composition of the dataset. Figure 5. 31 Results achieved using the proposed system on the whole test set.
errors. We also observed an improvement in the detection of false positives, which decrease for higher values of the SNR due to the reduced influence of the background noise on the target sound. It is worth noting that the recognition rate for events at 5dB SNR is only 5% lower than the average recognition rate on the whole dataset and about 9% lower than the best value achieved for the events at 30dB SNR. The correct classification rate and the false positive rate that we achieved in different SNR conditions prove that the proposed system is robust to background noise variation and can be used for surveillance applications also in highly noisy environments. As said in Section 4. 2, the value K = 1024 has been chosen by evaluating the performance results achieved for different numbers of cluster centroids.
For events with higher SNR, instead, the recognition rate achieved by  [96]  on our dataset is slightly better than the one of the proposed system. The classification error between the three foreground sounds classes achieved by the bag of aural words system is remarkably lower than the one achieved by  [96]. The proposed system, thus, provides a generally better recognition of the events of the classes of interest, reaching good performance results also in environments in which the target sounds occur with a low value of the SNR. In Table  5. 39 we report the false positive rates achieved by the proposed  system and by  [96]  on the whole test set. It is worth pointing out that, using the LVQ classifier proposed in  [96] , the number of false positives hugely increases with respect to the number of false positives detected by the proposed bag of aural words classifier.
Achievements In this section the results obtained by the proposed system will be briefly summarized. • The tracking algorithm has been tested over two standard datasets, namely PETS and ISSIA Soccer. The obtained results (MOTA = 0. 66, MOTP = 0. 62, ATA = 0. 09, Number of id-switches = 2. 8, being the average trajectory length = 184 for the PETS dataset; MOTA = 0. 75, MOTP = 0. 74, ATA = 0. 15, Number of id-switches = 2. 8, being the average trajectory length = 532, for the ISSIA Soccer dataset) strongly confirm the effectiveness of the proposed approach. Furthermore, the algorithm participated to a competition (PETS 2013 contest), where it has been selected by the organizers as the most robust one in terms of accuracy (MOTA). As for the computational cost, the algorithm is able to work in real time, with a frame rate of 35 fps by considering 4 CIF images, so confirming the real applicability of the proposed approach.
• The module for visual behavior analysis has been tested over three standard datasets, namely MIT1, EDH and MIT2, and the results have been compared with state of the art approaches: the proposed clustering algorithm reveals to be very promising both in terms of c-index (0. 11 vs 0. 23, on average) and time required for the computation (0. 48 vs 57 hours, on average). Furthermore, the high accuracy of the proposed approach is confirmed by the module for anomaly detection, which obtains an average AUC of 0. 88 by considering 30 zones. Finally, a significant improvement has been also obtained by the k-NN search based on KD-Tree for solving queries by sketch: less than 20% (in the worst case) over the whole dataset needs to be analyzed for k = 1, while less than 50% (in the worst case) for k = 5.
• The storing and retrieving engine has been tested over both synthetic and real dataset (MIT1 dataset). The obtained results have been compared with the standard PostGis solutions, showing a significant improvement, up to 70 times by considering a small query box size and a small area min value. • In order to test the algorithm for recognizing audio events of interest, a new dataset, composed approximatively by 6000 events of interest, has been introduced and made available for benchmarking purposes. The performance results that we achieved (Rec. Rate = 84. 8%, False Positive Rate = 2. 1%) have been compared with the results of another method from the literature (Rec. Rate = 83. 9%, False Positive Rate = 13. 8%), and show the robustness of the proposed approach with respect to noise and its applicability to real environments.
As soon as an abnormal behavior occurs, an alert is sent to the human operator in order to allow a prompt intervention for managing the particular situations. This step is achieved by defining a proper representation of the trajectories, based on strings, which takes into account position, speed and shape. The similarity between trajectories is evaluated by a novel string kernel, while the extraction of prototypes is made possible by a novel kernelbased clustering algorithm. Furthermore, in order to allow the human operator to easily and efficiently retrieve events of interest if necessary, all the obtained data are properly stored. In particular, the most complex information to be managed pertains the tra-jectories, for their spatio-temporal nature. For this reason, a novel indexing schema has been defined in this thesis, by taking advantage on bi -dimensional off-the-shelf solutions.
All the above mentioned modules, respectively devoted to tracking, behavior analysis by visual inspection, audio event analysis and storing, have been tested over standard datasets and a proper comparison with state of the art approaches has been carried out. In particular, the tracking algorithm has been tested over two extensively used datasets: the obtained results, both in terms of accuracy (MOTA = 0. 70, MOTP = 0. 68, ATA = 0. 12 in average) and computational cost (frame rate of 35 fps by considering 4 CIF images) confirm the robustness of the proposed approach. Furthermore, the proposed method participated to an international contest, namely the PETS competition, ranking at the the first places in all the considered metrics. As for the module for visual behavior analysis, it has been tested over three standard datasets and the obtained results (c-index = 0.
11 and execution time = 0. 48 hours in average) have been compared with several state of the art approaches (c-index = 0. 23 and execution time = 57 hours in average for the best one), confirming its effectiveness. Promising performance have been also obtained by the module for audio event recognition: a new dataset has been made available for benchmarking purposes and the obtained results (Rec. Rate = 84. 8%, False Positive Rate = 2. 1%), compared with a state of the art approach (Rec. Rate = 83. 9%, False Positive Rate = 13. 8%), confirm its strong robustness. Finally, the module for storing and retrieving trajectories has been evaluated over both standard and synthetic datasets and the obtained results have been compared with the standard solution provided by PostGis. Even in this case, very encouraging results have been obtained, strongly outperforming (up to 70 times) PostGis solution.
On the other hand, audio events of interest, such as gun shots, screams and broken glasses are recognized by analyzing the audio stream acquired by a microphone (Audio Event Analysis). Finally, this large amount of data needs to be properly stored (Storing) in a way that the human operator can efficiently interact with the system by submitting different typologies of queries (Querying), whose parameters are defined only at query time (that is exactly in the moment the query is thought). For instance, the objects crossing a given area in a given time interval (dynamic spatio temporal query) or the objects following a particular trajectory, similar to the one manually drawn by the user (query by sketch). A brief description of the above mentioned modules is provided in the following:.
[131]rison between the proposed method and[131]in terms of split and occlusion patterns resolution over the PETS dataset. Split Occlusion View Resolution Percentage Resolution Percentage [131] Proposed [131] Proposed 1 45% 73% 75% 95% 3 36% 51% 61% 76% 4 35% 74% 45% 74% 5 15% 51% 56% 67% 6 12% 56% 51% 62% 7 16% 61% 52% 63% 8 20% 51% 42% 59% Figure 5. 7.
True Posi1ve Rate (TPR) False Posi1ve Rate (FPR) ROC Curve Edinburgh Dataset 10 zones 20 zones 30 zones 40 zones 50 zones 60 zones (d) 10 zones 20 zones 30 zones 40 zones 50 zones 60 zones MIT1 Dirac 0. 87 0. 88 0. 85 0. 87 0. 74 0. 67 EDH Dirac 0. 72 0. 73 0. 84 0. 87 0. 86 0. 77 MIT1 Weighted 0. 87 0. 85 0. 92 0. 86 0. 89 0. 86 Dirac EDH Weighted 0. 79 0. 85 0. 85 0. 82 0. 79 0. 80 Dirac (e).
The numerical algorithm was written in MATLAB ® , and the function used to detect the enclosed regions was 'regionprops'. To validate the developed algorithm, several surfaces have been created numerically with peaks simulated by spherical and sinusoidal caps with known radius. In the example below, illustrated in Figure  2 , the surface has 3 3 peaks, their radius is = 80pt and the surface size is 512pt 512pt. After application of the algorithm described above, the detected peaks radii can be plotted as a function of their equivalent diameter and the distance between the maximum height and the intercepting plane ( , Figure  1 ). It can be noted that the algorithm is more precise for large values of (or ), which is due to the discretization of the surface: if the value of (or ) decreases, it becomes closer to the sampling length, affecting the peak radius calculation.
RESULTS AND DISCUSSION The surface topography was acquired using a Zygo NewView 7300 profiler, which is a Vertical Scanning Interferometer (VSI). The measurement principle is based on unfiltered white light beam that is split in two: half of the beam is directed through a microscope lens and reflected from the surface, and the other half is reflected from the reference mirror. When the reflected beams recombine they produce interference fringes, where the best-contrast fringe occurs at best focus. In a VSI, the objective moves vertically to scan the surface at various heights. A 3D surface is then reconstructed by analysis of fringes at every pixel. A VSI uses an algorithm to process fringe modulation data from the intensity signal to calculate the surface heights. The obtained resolution will therefore depend on the precision of the zaxis positioning.