dataesr/scientific-paragraphs-categorization huggingface dataset:
These functional requirements are actions or tasks accessible to the players:  (1)  The serious game should allow to choose the accessibility need by the player;  (2)  The player should have a means to authenticate/associate the device; (3) The player can enter any queue for a particular game mode; (4) The player can learn Morse code before testing the knowledge against an opponent; (5) The player can check several times the possible answers until giving a final answer; (6) The player has unlimited response time;  (7)  The player has dynamic adjustment of the game difficulty;  (8)  The player should have access to his current score;  (9)  The player has access to a chat room; and  (10)  The player can leave the game. Regarding the database used in this serious game, its purpose is the preservation of data, namely those concerning the player.
The game is divided into four layers. At the top level there is the user interface. All game functionalities can be accessed through this layer by the user on his preferred device. Actions transmitted by the user go through the following layers. The second layer is where all clientside code is developed, with business layer component containing the DDA, game and accessibility scripts which linked with Unity offer an accessible front-end for its users. The next level which contains sockets and a RESTful API, help pass through information sent and received on users end between the game and server. The fourth layer represents the game back-end, where persistent data is stored with the help of an online hosted database hosted.
Data sources and search strategy PICO strategy was defined to structure the algorithm: defining the Population (adult people with treatment-resistant depression), Intervention (use of Ketamine to eliminate Depression), Comparison with the intervention (Electroconvulsive therapy) and Outcome (rapid and robust antidepressant effects). The PUBMED database was selected to obtain the articles for this review. Once inserted the keywords, synonyms (Mesh platform) and Boolean operators the search algorithm result was (("ketamine" OR "cetamine") AND ("adverse events" OR "side-effects" OR "pain" OR "chronic pain" OR "symptoms" OR "symptomatology" OR "adverse drug reactions" OR "medication adverse reactions") AND ("effects" OR "efficacy" OR "response" OR "success therapy" OR "success treatment" OR "treatment" OR "therapy" OR "medical" OR "clinical" OR "clinical practice") AND ("apathy" OR "depression" OR "major depressive disorder" OR "refractory depression" OR "resistant depressant" OR "major disorder"))).
Study eligibility criteria and selection Articles were included if they were clinical study or trial, including clinical trial phase I, II, III or IV, controlled clinical trial, comparative study, observational study, were published up to five years of the mentioned period of searching (from 2013 to 2018), approached patients with treatment-resistant depression, and described the therapeutic outcome and side-effects of Ketamine. Articles were excluded if they discussed other depressive disturbances besides treatment-resistance depression, used animal models or were review papers. For eligibility assessment, one author (D. F. ) reviewed titles and abstracts of database records and retrieved full texts. The full-text records were reviewed for eligibility by two authors independently (D. F. and M.
Severity Drug interactions could be grouped according to their severity, that is, they are divided according to the damage that they cause to the patient. So, drug interactions could be classified in the following grades: contraindicated, major, moderate, minor, and unknown. According to the literature and Micromedex database, a contraindicated interaction means that two or more drugs cannot be administered concurrently  [15]. Major interactions are those whose adverse effects can cause permanent damage to the patient or put the patient's life at risk, requiring intervention by health professionals  [11] ,  [15]. Moderate type interactions are less severe than major types, however, can change/modify the patient's clinical condition and medical treatment is necessary to reduce exacerbations and reframe the therapeutic regimen  [11] ,  [15].
Methodology In this project, a descriptive cross-sectional observational study was conducted related to drug interactions between oral antineoplastic and opioid analgesics. This investigation was conducted between April 2020 and June 2021. Potential drug interactions were collected, using the Micromedex database. For this data collection, the drugs used were selected in FHNM ("Formulário Hospitalar Nacional do Medicamento") and combined one by one to find specific drug-drug interactions, their severity, and the grade of evidence. This data collection was carried out on April 23, 2020. The results were organized in an Excel file. Six drug interactions were selected to be properly explained.
Results Following the analysis and collection of the drugs from FHNM, twenty-seven oral antineoplastic agents were selected (anastrozole, azathioprine, bicalutamide, busulfan, chlorambucil, cyclophosphamide, cyproterone, cyclosporine, etoposide, flutamide, hydroxycarbamide, idarubicin, imatinib, letrozole, lomustine, megestrol, melphalan, mercaptopurine, methotrexate, mitotane, mycophenolate mofetil, procarbazine, estramustine, tacrolimus, tamoxifen, thalidomide, and thioguanine) and seven opioid analgesics (buprenorphine, fentanyl, hydromorphone, morphine, oxycodone, tapentadol, and tramadol). After introducing one hundred and eighty-nine pharmaceutical combinations into Micromedex database, twenty-three interactions were detected (Table  1 ). Subsequently, analyzing the table, it was observed that the antineoplastic drug with the highest number of potential interactions was procarbazine (seven interactions, being five major and two contraindicated).
The imatinib is quickly absorbed after oral administration  [19]. This drug is metabolized mainly in the liver by the isoenzymes CYP3A4 and CYP3A5  [18]. The metabolites of this drug undergo bile excretion  [18] ,  [20]. Imatinib metabolism can be decreased and its plasma concentrations increased when administered concomitantly with drugs that inhibit CYP3A4 or CYP3A5  [19]. On the other hand, drugs that induce these enzymes can increase metabolism and decrease exposure to imatinib, as is the case with pro-carbamazepine  [19]. According to the Micromedex database, the simultaneous use of CYP3A4 inhibitors with imatinib should be carried out with caution  [15]. Knowing that imatinib is a potent inhibitor of CYP3A4 and that this isoenzyme is responsible for the metabolism of buprenorphine to norbuprenorphine, it is possible to understand that there may be an increase in the concentration of buprenorphine  [18].
The case studies reporting allergic or hypersensitivity reactions resulting from the application to the skin or scalp of a particular preservative present in a cosmetic or body care product published until 2018 were considered for the identification of the preservatives with the greatest potential to cause an irritant effect. Studies reporting adverse effects from a non-transdermal route of administration (i. e. oral, intravenous, etc. ) were excluded, as well as those reporting reactions resulting from the application of non-cosmetic products (such as paints, medical devices, etc. ). The statistical tool Statistical Package for the Social Sciences (SPSS) (version 25) was used for the purposes of registration and systematization of data. Student's t-test was applied and a p-value <0. 05 was considered statistically significant.
As shown in Fig. 1 , the target pulses, and then the X-ray pulses, have a duration of 4 µs. The nominal dose-per-pulse values are 277. 78 µGy and 555. 56 µGy for 6 MV and 18 MV photons, respectively  [5] [6]. The realized GI electronics exploits the sync signal available at the LINAC console to establish the integration of signals in a period around each pulse  [7]. A microcontroller unit synchronizes the signal integration, employees the analog-to-digital conversion and sends data to a computer. All the mentioned operation are performed within less than 500 µs, thus allowing the real-time acquisition of pulses for PRRs greater than 2 kHz. A specifically designed Labview® program was developed for remote control of the prototype and data recording. Figure  2  illustrates the set-up adopted during the measurements.
Experimental setup A lithium-ion cell (mod. 18650, Samsung) was characterized during this work. In order to simulate the optimal operating conditions, a constant-current source circuit was realized (Fig. 1 ). A fixed source/sink current of 2. 5 A was adjusted for the experiments. However, a 10 mΩ shunt was also inserted in series to the cell for the continuous monitoring of the current value. Three platinum resistance sensors (PT100 RS 158-985 PTFE) have been used for the temperature monitoring of the cell surface. The sensors were glued with silver paste at the centre and close the two poles of the cell. The voltage and the sink current of the cell, as well as the three temperatures, have been measured by means of a multi-channel acquisition system from National Instruments. A specifically designed Labview® program was developed for acquisition, processing and recording of data during the charge/discharge cycles.
Introduction My work in the last year was focused on two main topics  (1)  the semantic data mining for information discovery from Earth Observation (EO) images, and (2) complex-valued deep architectures for SAR data processing. In this regard, I have published three conference paper IGARSS 2021  [1] , EUSAR 2022  [2]  and IGARSS 2022  [3] , and one peer-reviewed journal paper IEEE J-STARS  [4]. The title of my research project is "Deep learning for SAR data in presence of adversarial samples" and the main objective is to develop deep learning solutions for spontaneous adversarial samples in SAR data classification. In order to achieve this goal, I started my research with generative models and datamining for latent semantic information discovery from EO images. Later, I started the development of complex-valued deep architectures for complex-valued SAR data processing and generating a complex-valued SAR annotated dataset.
Identification and selection of relevant studies We began to search for relevant literature by preparing a search string that is made up of key words for our research topic. To find the necessary material we looked in the following databases: Science Direct, Springer Link, ProQuest, Research Gate. We gained access to them through the data base of University of Maribor: UM-NIK. We limited our material search to the time from 25 th November 2021 to 20 th December 2021, when we performed the primary data collection. We performed another round of data collection due to a lack of relevant conclusions; this round went on from the 1 st to the 15 th of January 2022. Before we reviewed our collected literature, we decided on a set of criteria which we used to select the appropriate studies. Our criteria included: Material that has been written in the English language; scientific or research papers; research papers that are based on case studies; research papers that study B2B communication on social media and address at least one of our defined groups; the last criteria were the disciplines or areas in which we searched for our literature.
Data display and collection, summary and reporting our results Based on our research questions, we have chosen and displayed data from different studies. We used the database UM-NIK to find 1,805 research papers and other material. After removing the duplicates, we were left with 1,758 studies. In the first step of our review, we used the criteria for inclusion and exclusion, and marked the disciplines that are relevant for our research. Then we reviewed the titles and abstracts, after which we were left with 329 relevant works. After a further review we removed 306 irrelevant studies. The second step of the review contained a more detailed study of the remaining literature. We read 23 studies, from which we chose only the most relevant. We were left with 5 studies, which we analysed in greater detail and implemented in the results of our research.
As such, we hope to build on prior work by developing a machine learning algorithm trained on an image database of the Seretaide model and optimize the OCR performance for this specific inhaler. To accomplish the proposed objectives, annotated and pre-processed images of Seretaide inhaler devices were used to train and validate a machine learning model. Dataset. The database consists of 354 images of the Seretaide inhaler model in PNG format, with dimensions of 640 x 360 pixels, collected under ideal conditions using a LG-V700 (Android) camera app. These images show a wide representation of digits in the dose counter; additionally, the background varies between black, white, and multi-color, and the lighting source of the photo varies between natural light and artificial light. For the purpose of training a machine learning model on inhaler pictures, a manual annotation process was performed on all images present in the dataset.
This task was accomplished with the help of the VGG Image Annotator (VIA) tool  [11]. With the aid of this software, it was possible to collect annotations regarding the position and dimensions of the dose counter and the corresponding digits that appear on the display. Image Pre-Processing. All images suffered four pre-processing steps: rotating, cropping, convert to grayscale and resizing. The images in the dataset were rotated 90º right, since the photos were acquired horizontally, and cropped according to the dimensions of the dose counter indicated on the annotations file. Since the cropping measurements were not consistent across the dataset, the cropped-out images did not have the same size; hence, all images were resized to standardize the data. Model Architecture. With an OCR approach in mind, a neural network (NN) consisting of convolutional layers (CNN) to extract a sequence of features and recurrent layers (RNN) to propagate information through this sequence, was developed.
Additionally, it instantiates a new "endpoint layer" for implementing CTC loss. The former enables using unsegmented pairs of images and corresponding text transcriptions to train the model without any character/frame-level alignment  [12]. More specifically, the architecture of the NN consists of an input layer, two convolutional layers each followed by a pooling layer, two bidirectional layers, a CTC layer and finally an output layer (Figure  1 ). Model Training. In later stages, a training and validation dataset were generated. For this purpose, the dataset was shuffled randomly, so that each time the dataset is split a new training and validation dataset are created. Additionally, the dataset split into 90 % training set (318 images) and 10% validation set (36 images). This project runs using the Google Colab environment and the network was built using TensorFlow 2.
e. , it only considers the positive predicted results. In this case, the precision equals the exact match ratio (87,2 %). Furthermore, the F1 measure is the harmonic mean of Precision and Recall and gives a better measure of the incorrectly classified cases than the Accuracy Metric. The F1 measure reaches 87,1%, which is an indication of both good precision and good recall. The purpose of this effort was to enhance the Seretaide model's OCR performance. In comparison to prior study, the model's accuracy has increased significantly (from 38% to 87%). It is important to highlight, however, that the datasets utilized to evaluate these methodologies are not equal, and hence a direct comparison cannot be drawn. Nevertheless, the advancements in this research might be seen as an indication of significant improvement.
Conclusions and Future Work The purpose of this paper was to develop a text recognizer for an inhaler model (Seretaide). This was done by building a machine learning model, trained on a database of inhaler images, compatible with mobile applications. To the best of our knowledge, there are not many approaches in the literature that help to reduce the unreliability of patient compliance and self-reporting by making use of OCR to record effective dosage in inhaler dose counters. Furthermore, the proposed work explores the potential of a customized OCR approach to enhance the performance of already existing algorithms, thus making this work relevant to help mitigating the patient's unreliable, selfreported adherence. Nevertheless, further improvements are still needed to enhance the detection performance.
The authors used the WaveGAN structure for their work, which was originally designed to generate short audio files  (Donahue et al. , 2019). The structure of WaveGAN is one-dimensional deep convolutional generative adversarial networks (DCGANs) that first generate a spectrogram and then convert it into an audio signal. In this case, the GAN trained from a dataset of RIRs recorded in a real room, and could later change the acoustic parameters of the generated RIRs, such as reverberation time (RT60), direct to reverberant ratio (DRR) and others, to generate an unlimited number The authors of the IR-GAN algorithm had to solve the problem arising from the ability of GAN network to generate an unlimited variety of RIR impulses. There was a high probability that the generated RIRs would be noisy and have an unrealistically large reverberation time.
Evaluation methods Intelligence Advanced Research Projects Activity (IARPA) organized a competition called the ASpIRE challenge  (Harper, 2015). Those wishing to participate had to develop ASR systems without access to matched data for system training and development. This competition was for the evaluation of far-field recordings and differed from previous both methods achieve almost identical average error, but the algorithm proposed by Yu and Kleijn can offer significantly better computational efficiency -even 10 4 shorter working time due to the lower sampling rate used. Comparing the known room geometry parameters and those estimated by neural networks, the authors were able to achieve a minimum average error of 4 cm for the simulated RIR data and 6. 5 cm for the recorded RIR data. The smallest error in estimating absorption coefficients was 0.
Introduction Nowadays, open-source software is extensively used in many different areas and devices. From supercomputers and enterprise level network devices to smartphones and various Internet of Things (IoT) devices. Open-source software is decentralized in many cases, so multiple stakeholders can do adjustments or fixes. Development of open-source software is rapid, and it basically open to everyone without any cost. So, it is quite clear why it has achieved such popularity. In networking, open-source code is also playing a major role. TCP/IP stack in Linux and Unix systems is solid, mature and offers switching, routing, firewalling, and other functionality with possibility to tune it for various purposes. That is why it widely used by many companies in their proprietary software and hardware products using proprietary software.
Cisco Open NX-OS is built on Linux kernel (Cisco DevNet, 2021). Others Cisco's operating systems such as IOS-XE, NX-OS are also built on Linux. Juniper's Junos OS Evolved runs natively on Linux whereas classical Junos OS runs over an instance of the FreeBSD (Juniper Networks, 2021). Citrix Systems Netscaler software is also based on FreeBSD (Citrix, 2017). Additionally, in a lot of Linux based systems Netfilter framework is utilized for packet filtering, load-balancing and other manipulations with IP packets. The most famous Netfilter's utility iptables was introduced back in 1998. It became a standard for firewalling tools. However, various architectural limitations of iptables have pushed developers to introduce his successornftables. It overcome main iptables limitations  (Westphal, 2016).
For example, addition and removal of rules is now atomic, it is especially useful for applications such as Kubernetes or Red Hat's Openshift where ruleset updates are constant and very frequent. Most rule handling was moved to userspace. Support of new protocol will not require to implement kernel changes. Instead, only nft tool need to be updated. Since version 3. 13 nftables was merged into the Linux kernel. Despite all advantages, full migration from iptables to nftables have not happened yet. In 2018 iptables was considered legacy tools and iptables-nft tool was released to translate iptables rules into nftables and to enforce migration. Increasing network speeds and transferred data rate has led Linux community to think about alternative options of iptables replacer. In recent years, a lot of attention is focused on using eBPF functionality to make iptables alternative based on it.
Related works In our previous work "Performance Testing of Linux Firewalls"  (Melkov et al. , 2020)  we measured how TCP throughput depends on number of installed rules in iptables or nftables. Experiment was done using different Netfilter chains: PREROUTING, INPUT, FORWARD, OUTPUT. Different amount of virtual CPU (vCPU) was used on virtual machine with installed iptables or nftables. Results showed performance advantage of iptables over nftalbes in all scenarios. Best result was achieved using ipset extension. Scholz et al. (2018)  examined how number of processed packets per second depends on number of rules in iptables and nftables. Then they compared these results with results when XDP was used. XDP utilize eBPF virtual machine to process packets before they reach kernel. Witch such set-up they were able to reach four times better results than using iptabes and nftables.
Bertrone et al. (2018a)  in paper "Toward an eBPF-based clone of iptables" proposed architecture of a possible replacement of iptables with an equivalent software based on eBPF technology. They proposed how to implement matching algorithm and connection tracking using eBPF preserving iptables semantic and syntax. In their another paper "Accelerating Linux Security with eBPF iptables" (Bertrone et al. , 2018b) they made performance tests of iptables and their designed iptables alternative -bpf-iptables. Two different tests were done. In first test they measured UDP throughput in FORWARD chain and in second test TCP throughput was measured in INPUT chain. In both cases custom bpf-iptbales tool outperformed standard iptables. Greater advantage seen with increased number of installed rules. In further work from same authors "Securing Linux with a Faster and Scalable Iptables" (2019) they did nftables performance test in same scenarios.
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sk lodowska-Curie  grant agreement No  860370. The work was carried out at  CEOSpaceTech, Bucharest, Romania  and  Pmdtec, Siegen, Germany. Thank you to the staff there, and especially to  Prof. Dr. Daniela Coltuc ,  Prof. Dr. Mihai Datcu , and  Dr. Oliver Lottner  who have provided valuable feedback.
Acknowledgments This research was part of the project " mINSPIRE-mHealth to measure and improve adherence to medication in chronic obstructive respiratory diseases -generalization and evaluation of gamification, peer support and advanced image processing technologies ",  POCI-01-0145-FEDER-029130 , supported by the  ERDF (European Regional Development Fund) ,  COMPETE2020  ( Programa Operacional Competitividade e Internacionalização) , Portugal 2020 and by  Portuguese Funds through FCT (Fundação para a Ciência e a Tecnologia). We thank  PaCeIT  for providing the Seretaide image dataset, without which this work would not have been feasible.