In this work, a multi-agent system for EV charging negotiation and management is designed. It adapts the trade with the EV owner depending on multiple parameters like seller's benefit, supplied energy and buyer's flexibility. The system can be used as a simulation process and also can be applied to a real situation. The results shows better management of the EV charging with a good satisfaction for both seller and buyers

The region-based segmentation approach has been a major research area for many medical image applications. A vision guided autonomous system has used region-based segmentation information to operate heavy machinery and locomotive machines intended for computer vision applications. The dataset contains raw images in .png format fro brain tumor in various portions of brain.The dataset can be used fro training and testing. Images are calssified into three main regions as frontal lobe(level -1, level-2), optus-lobe(level-1), medula_lobe(level-1,level-2,level-3).

The datasets are about two user studies for brushing points in the scatterplot.

This dataset includes FTM WiFi measurements made with several ESP32-S2 devices in different indoor and outdoor environments. The measurements include the actual distance between devices as well as the RTT (Round Trip Time) values generated by the module.

We present here an annotated thermal dataset which is linked to the dataset present in https://ieee-dataport.org/open-access/thermal-visual-paired-dataset

To our knowledge, this is the only public dataset at present, which has multi class annotation on thermal images, comprised of 5 different classes.

This database was hand annotated over a period of 130 work hours.

This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.

Clamp-on ultrasonic transit time difference is used extensively to calculate the volumetric flow rate of a fluid through a pipe. The operating principle is that waves travelling along a path that is generally against the flow direction take longer to travel the same path than waves travelling along the same path in the opposite direction. The transit time difference between the waves travelling in opposite directions can be used to calculate the flow rate through the pipe, by applying suitable mathematical correction factors.

Vehicle-to-barrier (V2B) communications is an emerging communication technology between vehicles and roadside barriers to mitigate run-off-road crashes, which result in more than half of the traffic-related fatalities in the United States. To ensure V2B connectivity, establishing a reliable V2B channel is necessary before a potential crash, such that real-time information from barriers can help (semi-)autonomous vehicles make informed decisions. However, the characteristics of the V2B channel are not yet well understood.

The data set deals with a typicla charge-discharge cycle of a supercapacitor (here a NICHICON UM series, 2.7V, 1F):

• Very fast charge of the EDLC
• Discharge at more or less constant power

Data have been sampled at 10 Hz with the biologic VMP3 potentiostat.

With the motivation of no good data sources available for all diseases (from generic to chronic) and their treatment courses, a new dataset is synthesized by exploring several medical websites and resources. It provides the precaution list corresponding to over 1000+ diaganosis. prec\_t.csv : (did, diagnose, pid) = (Disease identifier, Disease name, treatment course). This dataset can be utilized for many machine learning or deep learning based healthcare applications.

The data set contains 13 matlab code files.

Abstract—Chipless RFID tag decoding has some inherent degrees of uncertainty because there is no handshake protocol between chipless tags and readers. This paper initially compares the outcome of different pattern recognition methods to decode some frequency-based tags in the mm-wave spectrum. It will be shown that these pattern recognition methods suffer from almost 2 to 5% false decoding rate. To overcome this mis-decoding problem, two novel methods of making images of the chipless tags are presented.

Industrial Internet of Things (IIoTs) are high-value cyber targets due to the nature of the devices and connectivity protocols they deploy. They are easy to compromise and, as they are connected on a large scale with high-value data content, the compromise of any single device can extend to the whole system and disrupt critical functions. There are various security solutions that detect and mitigate intrusions.

Rot corn grain image

We present an Arabic Twitter dataset for online extremism detection consisting of 89K tweets with associated metadata. The dataset was manually annotated by three experts and achieved a Gwet’s AC1 score of 0.6, indicating substantial inter-annotator agreement. We performed further analysis of the tweet metadata to identify important features. For the extremism dataset, there were 89,816 tweets in total published by 52,929 unique users.

Samples for our experiment results

In this appendix, the tested implementation in Matlab of our 2D-TDOA localization algorithm is given for the easier repetition of the obtained results and the future hardware implementation, due to the complexity of the formulas (25)-(31).

We provide two folders: 

(1)The shallow depth of field image data set folder consists of 27 folders from 1 to 27. 

In folder 1-27, each folder contains two test images and two word files. Img1 is the shallow depth of field image with the best focusing state taken with a 300 mm long focal lens, and img2 is the overall blurred image. 

'XMD.mat' is the deterioration levels monitored before the failure of 10 components。

'XMDS.mat' is the deterioration levels monitored before the failure of 10 components subjected to random shocks.

In recent years, it has become more difficult to identify road traffic signage and panel guide material. Few studies have been made to solve these two issues at the same time, especially in the Arabic language. Additionally, the limited number of datasets for traffic signs and panel guide content makes the investigation more interesting. the Tunisian research groups in intelligent machines of the University of Sfax (REGIM laboratory of Sfax) will provide the NaSTSArLaT dataset free to researchers in traffic detection signs and traffic road scene text detection.

In early 2019, we developed a manually curated database named lncR2metasta to provide a comprehensive repository for the regulations of long non-coding RNAs (lncRNAs, an important ncRNA type) during various CMEs. We updated this database this year by supplementing other two important ncRNA types, microRNAs (miRNAs) and circular RNAs (circRNAs), for their involvement during various CMEs after a thorough manual curation from published studies.

# Student Test Results Prediction based on Learning Behavior: Learning Beyond Tests

Dataset Part A: The Goal is to predict Test Results, in the form of averaged correctness, averaged timespent in the test, based only on the learning history (learning  behavior records)

Dataset Part B: The objective is to predict the last test results, points and scores, based on the learning behavior records and the first test results.

# About the dataset

The raw data is provided by ALIN.ai where a large number of students participated in math learning and tests, online. 

More than 85% of traffic utilization via mobile phones are consumed in the urban area, and most of the traffic is used for downloading. Improving the throughput in LTE for 1 user equipment (UE) in cities is an urgent problem. The collected data is intended to study a dependence of the KPI mobile base station and neighboring from installation extra technology. This study will support the development of methods for comparing traffic utilization of urban area and carry out recommendations for the Channel Quality Indicator (CQI) increases.

Oral health problems are closely associated with the analysis of dental tissue changes and the stomatologic treatment that follows. The associated paper explores the use of diffuse reflectance spectroscopy in the detection of dental tissue disorders. The data set includes 78 out of 343 measurements of teeth spectra in the wavelength range from 400 to 1700 nm. The proposed methodology focuses on computational and statistical methods and the use of these methods for the classification of dental tissue into two classes (healthy and unhealthy) by estimating the probability of class membership.