Gaming consoles are very common connected devices which have evolved in functionality and applications (games and beyond) they support. This diversity of traffic generated from these consoles has diverse quality of service (QoS) requirements. However, in order to offer diverse QoS, ISPs and operators must be able to classify this traffic. To enable research in traffic classification (Machine Learning based or other), we have generated and collected this dataset. This is a labelled dataset collected from a gaming console, PlayStation 4.
Download Microsoft Network Monitor (at the following link: https://www.microsoft.com/en-us/download/details.aspx?id=4865) to be able to access the data. Open the capture file and then wait for all the collected frames to be loaded. The data set was collected using Microsoft Network Monitor 3.4. The traffic is Labelled by number, time and day, Source and Destination IP, Protocol, length and description. Using Microsoft Network Monitor, there is a way to Filter by Media type (check the following link: https://docs.microsoft.com/en-us/archive/blogs/netmon/intro-to-filtering...). To navigate the data easily, you can apply a filter on the media type by putting it Ethernet meaning that only the data exchanged between the Laptop and the PlayStation will show. The Excel sheet included with the dataset contains the date and the time of each capture and also when each activity was running and when it was stopped making it easy to identify the data. Refer to the time delay report attached for more information about the time synchronization aspects between the data capture and the PlayStation.
In this paper, we present a collaborative recommend system that recommends elective courses for students based on similarities of student’s grades obtained in the last semester. The proposed system employs data mining techniques to discover patterns between grades. Consequently, we have noticed that clustering students into similar groups by performing clustering. The data set is processed for clustering in such a way that it produces optimal number of clusters.
Case and contact definitions are based on the current available information and are regularly revised as new information accumulates. Countries may need to adapt case definitions depending on their local epidemiological situation and other factors. All countries are encouraged to publish definitions used online and in regular situation reports, and to document periodic updates to definitions which may affect the interpretation of surveillance data.
The First Few X cases and contacts (FFX) investigation protocol for Coronavirus Disease 2019 (COVID-19). This is about identification and tracing of cases and their close contacts in the general population or restricted to close settings (like households, health-care settings, schools). FFX is the primary investigation protocol to be initiated upon the identification of the initial laboratory-confirmed cases of COVID-19 in a country.
Several pathological phenomena are closely associated with mechanical properties of vessel and interactions of blood flow–wall dynamics. However, conventional techniques cannot easily measure these features. In this study, new deep learning-based simultaneous measurement of flow–wall dynamics (DL-SFW) is proposed by devising integrated neural network for super-resolved localization and vessel wall segmentation and combining with tissue motion measurement technique and flow velocimetry.
Academic spaces are an environment that promotes student performance not only because of the quality of its equipment, but also because of its ambient comfort conditions, which can be controlled by means of actuators that receive data from sensors. Something similar can be said about other environments, such as home, business, or industry environment. However, sensor devices can cause faults or inaccurate readings in a timely manner, affecting control mechanisms. The mutual relationship between ambient variables can be a source of knowledge to predict a variable in case a sensor fails.
Enrichment analysis performed by Enrichr toward genes associated with coronavirus infeciton
Genes are selected by TD based unsupervised FE and are uploaded to Enrichr
The dataset consists of two populations of fetuses: 160 healthy and 102 Late Intra Uterine Growth Restricted (IUGR). Late IUGR is an adverse pathological condition encompassing chronic hypoxia as a consequence of placental insufficiency, resulting in an abnormal rate of fetal growth. In standard clinical practice, Late IUGR diagnosis can only be suspected in the third trimester and ultimately confirmed at birth. This data collection comprises of a set of 31 Fetal Heart Rate (FHR) indices computed at different time scales and domains accompanied by the clinical diagnosis.
The data for healthy and Late IUGR populations are included in a single .xlsx file.
Participants are listed by rows and features by columns. In the following we report an exhaustive list of features contained in the dataset accompanied by their units, time interval employed for the computation, and scientific literature references:
Fetal and Maternal Domains
- Clinical Diagnosis [HEALTHY/LATE IUGR]: binary variable to report the clinical diagnosis of the participant
- Gestational Age [days]: gestational age at the time of CTG examination
- Maternal Age [years]: maternal age at the time of CTG examination
- Sex [Male (1)/Female (2)]: fetal sex
Morphological and Time Domains
- Mean FHR [bpm] – 1-min epoch: the mean of FHR excluding accelerations and decelerations
- Std FHR [bpm] – 1-min epoch: the standard deviation of FHR excluding accelerations and decelerations
- DELTA [ms] – 1-min epoch: defined in accordance with ,  excluding accelerations and decelerations
- II  – 1-min epoch: defined in accordance with ,  excluding accelerations and decelerations
- STV [ms] – 1-min epoch: defined in accordance with ,  excluding accelerations and decelerations
- LTI [ms] – 3-min epoch: defined in accordance with ,  excluding accelerations and decelerations
- ACC_L [#] – entire recording: the count of large accelerations defined in accordance with , 
- ACC_S [#] – entire recording: the count of small accelerations defined in accordance with , 
- CONTR [#]– entire recording: the count of contractions defined in accordance with , 
- LF [ms²/Hz] – 3-min epoch: defined in accordance with , LF band is defined in the range [0.03 - 0.15] Hz
- MF [ms²/Hz] – 3-min epoch: defined in accordance with , MF band is defined in the range [0.15 - 0.5] Hz
- HF [ms²/Hz] – 3-min epoch: defined in accordance with , HF band is defined in the range HF [0.5 - 1 Hz]
- ApEn [bits] – 3-min epoch: defined in accordance with , m = 1, r = 0.1*standard deviation of the considered epoch
- SampEn [bits] – 3-min epoch: defined in accordance with , m = 1, r = 0.1*standard deviation of the considered epoch
- LCZ_BIN_0 [bits] – 3-min epoch: defined in accordance with , binary coding and p = 0
- LCZ_TER_0 [bits] – 3-min epoch: defined in accordance with , tertiary coding and p = 0
- AC/DC/DR [bpm] – entire recording: defined in accordance with –, considering different combinations of parameters T and s, L is constant and equal 100 samples; e.g, AC_T1_s2 is defined as the acceleration capacity computed setting the parameters T = 1 and s = 2
 D. Arduini, G. Rizzo, A. Piana, P. Bonalumi, P. Brambilla, and C. Romanini, “Computerized analysis of fetal heart rate—Part I: description of the sys- tem (2CTG),” J Matern Fetal Invest, vol. 3, pp. 159–164, 1993.
 M. G. Signorini, G. Magenes, S. Cerutti, and D. Arduini, “Linear and nonlinear parameters for the analysis of fetal heart rate signal from cardiotocographic recordings,” IEEE Trans. Biomed. Eng., vol. 50, no. 3, pp. 365–374, 2003.
 FIGO, “Guidelines for the Use of Fetal Monitoring,” Int. J. Gynecol. Obstet., vol. 25, pp. 159–167, 1986.
 R. Rabinowitz, E. Persitz, and E. Sadovsky, “The relation between fetal heart rate accelerations and fetal movements.,” Obstet. Gynecol., vol. 61, no. 1, pp. 16–18, 1983.
 S. M. Pincus and R. R. Viscarello, “Approximate entropy: a regularity measure for fetal heart rate analysis.,” Obstet. Gynecol., vol. 79, no. 2, pp. 249–55, 1992.
 D. E. Lake, J. S. Richman, M. P. Griffin, and J. R. Moorman, “Sample entropy analysis of neonatal heart rate variability,” Am. J. Physiol. - Regul. Integr. Comp. Physiol., vol. 283, no. 3, pp. R789–R797, 2002.
 A. Lempel and J. Ziv, “On the complexity of finite sequences,” IEEE Trans. Inf. Theory, vol. 22, no. 1, pp. 75–81, 1976.
 A. Bauer et al., “Phase-rectified signal averaging detects quasi-periodicities in non-stationary data,” Phys. A Stat. Mech. its Appl., vol. 364, pp. 423–434, 2006.
 A. Fanelli, G. Magenes, M. Campanile, and M. G. Signorini, “Quantitative assessment of fetal well-being through ctg recordings: A new parameter based on phase-rectified signal average,” IEEE J. Biomed. Heal. Informatics, vol. 17, no. 5, pp. 959–966, 2013.
 M. W. Rivolta, T. Stampalija, M. G. Frasch, and R. Sassi, “Theoretical Value of Deceleration Capacity Points to Deceleration Reserve of Fetal Heart Rate,” IEEE Trans. Biomed. Eng., pp. 1–10, 2019.
This simulated combat reports dataset combines fictional headings, reporting units, and attack times with real data from 551 records of terrorist attacks in Afghanistan (2009–2010) . The dataset combines selected attributes from the DA Form 1594  and U.S. Army Spot Report . The dataset also includes additional attributes for tactical context.