Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of ConvLSTM Network in Numerical Temperature Prediction Interpretation

The application research of machine learning methods has attracted attention in the meteorological field. In this paper, we establish a spatiotemporal temperature deviation prediction model (PredTemp) based on convolution and long short-term memory network (ConvLSTM). The model is trained with numerical weatherprediction (NWP), and the prediction results are used to correct the temperature prediction in NWP. Exploring the influence of the weather elements added to the model on the prediction results is also the focus of this paper. Two datasets are constructed for this purpose: the temperature forecast deviationdataset (dataset1) is constructed by using the temperature forecastsand the analysis field in NWP, a precipitation forecast dataset (dataset2) was constructed using the precipitation forecasts in NWP. The experimental results show that the model is effective. Using dataset1 as dataset for training, the accuracy rate of temperaturecorrected by PredTempwas increased by 3%compared to NWP; using dataset1 and dataset2 as dataset for training, the accuracy rate of temperature corrected by PredTemp was increased by 4%. The addition of precipitation elements has played a positive role in improving the accuracy of the model prediction.
Mitogen-activated Protein Kinases Play an Essential Role in Oxidative Burst-independent Expression of Pathogenesis-related Genes in Parsley*

Plants are continuously exposed to attack by potential phytopathogens. Disease prevention requires pathogen recognition and the induction of a multifaceted defense response. We are studying the non-host disease resistance response of parsley to the oomycete, Phytophthora sojae using a cell culture-based system. Receptor-mediated recognition of P. sojae may be achieved through a thirteen amino acid peptide sequence (Pep-13) present within an abundant cell wall transglutaminase. Following recognition of this elicitor molecule, parsley cells mount a defense response, which includes the generation of reactive oxygen species (ROS) and transcriptional activation of genes encoding pathogenesis-related (PR) proteins or enzymes involved in the synthesis of antimicrobial phytoalexins. Treatment of parsley cells with the NADPH oxidase inhibitor, diphenylene iodonium (DPI), blocked both Pep-13-induced phytoalexin production and the accumulation of transcripts encoding enzymes involved in their synthesis. In contrast, DPI treatment had no effect upon Pep-13-induced PRgene expression, suggesting the existence of an oxidative burst-independent mechanism for the transcriptional activation ofPR genes. The use of specific antibodies enabled the identification of three parsley mitogen-activated protein kinases (MAPKs) that are activated within the signal transduction pathway(s) triggered following recognition of Pep-13. Other environmental challenges failed to activate these kinases in parsley cells, suggesting that their activation plays a key role in defense signal transduction. Moreover, by making use of a protoplast co-transfection system overexpressing wild-type and loss-of-function MAPK mutants, we show an essential role for post-translational phosphorylation and activation of MAPKs for oxidative burst-independentPR promoter activation.
Optimal Modulation of Flying Capacitor and Stacked Multicell Converters using a State Machine Decoder

Modulation of flying capacitor and stacked multicell converters (SMC) is complicated by the fact that these converters have redundant states that achieve the same phase leg voltage output. Hence a modulator must use some secondary criteria such as cell voltage balancing to fully define the converter switched state. Alternatively, the modulator can be adapted to directly specify the cell states, such as has been proposed for the harmonically optimal phase disposition (PD) strategy. However the techniques reported to date can lead to uneven distribution of switching transitions between cells, and the synthesis of narrow switched phase leg pulses. This paper presents an improved strategy that decouples the tasks of voltage level selection and switching event distribution. Conventional PD and CSVPWM strategies are used to define the target voltage level for the converter, and a finite state machine is then used to distribute the transitions to the converter cells in a cyclical fashion. Experimental results for a four level flying capacitor inverter are presented, verifying that the natural balancing properties of this converter has been preserved, the cell switching utilization is equal and the expected harmonic gains of PD and CSVPWM compared to phase shifted carrier PWM have been achieved
Computing the Linguistic-Based Cues of Fake News in the Philippines Towards its Detection

Fake news deliberately beguiles its consumers to accept false or biased ideologies. But as menacing as it sounds to the society, it poses a good classification problem to computer science. This paper presents the disparity in writing style between legitimate news and fake news in the Philippines, and how effective these are as machine learning features. To capacitate this, credible news samples, as well as fake news samples in the Philippines were harvested online. The best feature set was consistent across all experiments and attained a precision of 94% on both the feature selection process and the test set of the final model. It also attained a precision of 93% on a more recent data collected, which was not part of the initial corpus constructed. Furthermore, this paper exhibits how legitimate and fake news in the Philippines can be differentiated just by their headline or content at a precision of 87% and 88%, respectively.
An Effective Deep Learning Framework for Detecting Misconduct of the Trucker

The traffic risks include malfunction of gears in the vehicle and some human misconducts while on the road. These misconducts of driver contain using the mobile phone, smoking, watching videos, reading books, and so forth. To prevent drivers from danger, in this paper, we will present an effective system to detect whether the drivers are doing those behaviors while driving by using the deep learning technologies. The proposed system adopts two different neural network architectures to improve its accuracy rate that are multilayer perceptron (MLP) and convolutional neural network (CNN). As expected, by training the video data from cameras installed in the truck, the proposed system could possibly distinguish the misconduct if the drivers are doing danger behaviors. The result of this paper shows that the proposed system practically provides a solution to recognize misconducts of the trucker with 90% accuracy of detecting abnormal situations.
Online Signature Verification Based on Writer Specific Feature Selection and Fuzzy Similarity Measure

Online Signature Verification (OSV) is a widely used biometric attribute for user behavioral characteristic verification in digital forensics. In this manuscript, owing to large intra-individual variability, a novel method for OSV based on an interval symbolic representation and a fuzzy similarity measure grounded on writer specific parameter selection is proposed. The two parameters, namely, writer specific acceptance threshold and optimal feature set to be used for authenticating the writer are selected based on minimum equal error rate (EER) attained during parameter fixation phase using the training signature samples. This is in variation to current techniques for OSV, which are primarily writer independent, in which a common set of features and acceptance threshold are chosen. To prove the robustness of our system, we have exhaustively assessed our system with four standard datasets i.e. MCYT-100 (DB1), MCYT-330 (DB2), SUSIG-Visual corpus and SVC-2004- Task2. Experimental outcome confirms the effectiveness of fuzzy similarity metric-based writer dependent parameter selection for OSV by achieving a lower error rate as compared to many recent and state-of-the art OSV models.
Primary Tumors of the Spine

Abstract Spinal tumors consist of a large spectrum of various histologic entities. Multiple spinal lesions frequently represent known metastatic disease or lymphoproliferative disease. In solitary lesions primary neoplasms of the spine should be considered. Primary spinal tumors may arise from the spinal cord, the surrounding leptomeninges, or the extradural soft tissues and bony structures. A wide variety of benign neoplasms can involve the spine including enostosis, osteoid osteoma, osteoblastoma, aneurysmal bone cyst, giant cell tumor, and osteochondroma. Common malignant primary neoplasms are chordoma, chondrosarcoma, Ewing sarcoma or primitive neuroectodermal tumor, and osteosarcoma. Although plain radiographs may be useful to characterize some spinal lesions, magnetic resonance imaging is indispensable to determine the extension and the relationship with the spinal canal and nerve roots, and thus determine the plan of management. In this article we review the characteristic imaging features of extradural spinal lesions.
Sitting Posture Recognition in Real-Time Combined with Index Map and BLS

The development of modern science and technology has seriously changed people's living and working habits. Bad sitting habit undoubtedly has an important impact on human health. Therefore, this paper proposes a real-time sitting posture recognition algorithm based on index map and BLS model. Firstly, use Kinect to collect body index maps and build self-built sitting posture database; then use SSIM to supervise the change of sitting posture, the BLS is used to recognize the category of sitting posture in real-time; finally, as for the excessive frames in video, this paper proposes double thresholds cascade algorithm to detect excessive frames. The experimental results show that the algorithm proposed can effectively recognize 8 types of sitting postures with the accuracy of 98.58%, the speed of 162fps and effectively detect the excessive frames.
Dynamic Threshold Reconfiguration Mechanisms for Green IP-Over-WDM Networks

IP-over-WDM optical mesh backbone networks handle most of the high-volume traffic that is exchanged between access networks. Internet growth and the increase in bandwidth demand per user render the power consumption equally important to the performance of the network. To achieve energy efficiency, virtual topology reconfiguration has emerged as a method of adjusting the network resources to the current traffic demand of optical backbone networks in real time. Deactivating underutilized lightpaths during the network operation can greatly reduce total power consumption. Threshold-based reconfiguration algorithms employ load thresholds to monitor the state of the network and act upon it. In this paper, prediction-based dynamic thresholds-virtual topology reconfiguration, a new dynamic threshold-based reconfiguration algorithm, is introduced, which calculates individual dynamic high thresholds by using the output of a load prediction module. In this way, the adaptivity of the algorithm and the lightpath capacity utilization are improved, leading to a significant increase of the energy saving.
IOT Security Issues Via Blockchain: A Review Paper

In the past few years block chain has gained lot of popularity because blockchain is the core technology of bitcoin. Its utilization cases are growing in number of fields such as security of Internet of Things (IoT), banking sector, industries and medical centres. Moreover, IoT has expanded its acceptance because of its deployment in smart homes and city developments round the world. Unfortunately, IoT network devices operate on limited computing power with low storage capacity and network bandwidth. Thus, they are extra close to attacks than other end-point devices such as cell phones, tablets, or PCs. This paper focus on addressing significant security issues of IoT and maps IoT security issues in contradiction of existing solutions found in the literature. Moreover issues that are not solved after implementation of blockchain are highlighted.
Rapid phase-correlated rescanning irradiation improves treatment time in carbon-ion scanning beam treatment under irregular breathing

To shorten treatment time in pencil beam scanning irradiation, we developed rapid phase-controlled rescanning (rPCR), which irradiates two or more isoenergy layers in a single gating window. Here, we evaluated carbon-ion beam dose distribution with rapid and conventional PCR (cPCR). 4 dimensional computed tomography (4DCT) imaging was performed on 12 subjects with lung or liver tumors. To compensate for intrafractional range variation, the field-specific target volume (FTV) was calculated using 4DCT within the gating window (T20–T80). We applied an amplitude-based gating strategy, in which the beam is on when the tumor is within the gating window defined by treatment planning. Dose distributions were calculated for layered phase-controlled rescanning under an irregular respiratory pattern, although a single 4DCT data set was used. The number of rescannings was eight times. The prescribed doses were 48 Gy(RBE)/1 fr (where RBE is relative biological effectiveness) delivered via four beam ports to the FTV for the lung cases and 45 Gy(RBE)/2 fr delivered via two beam ports to the FTV for the liver cases. In the liver cases, the accumulated dose distributions showed an increased magnitude of hot/cold spots with rPCR compared with cPCR. The results of the dose assessment metrics for the cPCR and rPCR were very similar. The D95, Dmax, and Dmin values (cPCR/rPCR) averaged over all the patients were 96.3 ± 0.9%/96.0 ± 1.2%, 107.3 ± 3.6%/107.1 ± 2.9%, and 88.8 ± 3.2%/88.1 ± 3.1%, respectively. The treatment times in cPCR and rPCR were 110.7 s and 53.5 s, respectively. rPCR preserved dose conformation under irregular respiratory motion and reduced the total treatment time compared with cPCR.
Is volcanic air pollution associated with decreased heart-rate variability?

Objectives To determine the autonomic cardiovascular control among residents of Hawaii who are exposed to varying levels of volcanic air pollution (vog), which consists largely of sulfur dioxide (SO2) and acid aerosols. Methods In a cross-sectional study between April 2006 and June 2008, the authors measured cardiovagal autonomic function by heart-rate variability (HRV) in 72 healthy individuals who lived in four exposure zones on Hawaii Island: vog-free (n=18); episodic exposure to SO2 >200 ppb and acid aerosol (n=19); chronic exposure to SO2 ≥30 ppb and acid aerosol (n=15); and chronic exposure to acid aerosols (n=20). Individuals with diabetes or heart disease, or who had smoked in the preceding month were excluded. HRV was measured in all subjects during rest, paced breathing and active standing (Ewing manoeuvre). HRV was analysed in time and frequency domains and compared between the four exposure zones. Results There were no significant differences between exposure zones in HRV, in either time or frequency domains, even after adjustment for age, gender, ethnicity and body mass index. There was no significant HRV change in three individuals in whom HRV was measured before and during an exposure to combined SO2 100–250 ppb and concentration of respirable particles of diameter ≤2.5 μ (PM2.5) >500 μg/m3. Age was significantly correlated with time-domain parameters during paced breathing and the Ewing manoeuvre. Conclusions This study of healthy individuals found no appreciable effects of vog on the autonomic nervous system.
Cerebral structural abnormalities in obsessive-compulsive disorder. A quantitative morphometric magnetic resonance imaging study.

BACKGROUND A previous pilot study of only posterior brain regions found lower white-matter volume in patients with obsessive-compulsive disorder than in normal control subjects. We used new cohorts of patients and matched normal control subjects to study whole-brain volume differences between these groups with magnetic resonance imaging-based morphometry. METHODS Ten female patients with obsessive-compulsive disorder and 10 female control subjects, matched for handedness, age, weight, education, and verbal IQ, underwent magnetic resonance imaging with a 3-dimensional volumetric protocol. Scans were blindly normalized and segmented by means of well-characterized semiautomated intensity contour mapping and differential intensity contour algorithms. Brain structures investigated included the cerebral hemispheres, cerebral cortex, diencephalon, caudate, putamen, globus pallidus, hippocampus amygdala, third and fourth ventricles, corpus callosum, operculum, cerebellum, and brain stem. Anterior to posterior neocortical regions, including precallosum, anterior pericallosum, posterior pericallosum, and retrocallosum, with adjacent white matter were also measured. Volumes found different between groups were correlated with Yale-Brown Obsessive Compulsive Scale score and Rey-Osterieth Complex Figure Test measures. RESULTS Confirming results of our earlier pilot study and expanding the findings to the whole brain, patients with obsessive-compulsive disorder had significantly less total white matter but, in addition, significantly greater total cortex and opercular volumes. Severity of obsessive-compulsive disorder and nonverbal immediate memory correlated with opercular volume. CONCLUSIONS Replication of volumetric white-matter differences suggests a widely distributed structural brain abnormality in obsessive-compulsive disorder. Whereas determining the etiogenesis may require research at a microscopic level, understanding its functional significance can be further explored via functional neuroimaging and neuropsychological studies.
Gender and clinical presentation in adult celiac disease.

BACKGROUND Celiac disease may present in various forms. This study aimed to investigate whether gender affects the clinical presentation of the disease in adult celiac patients from the Mediterranean area. METHODS This study retrospectively analyzes data collected in all adult patients with celiac disease (n = 195) seen during the past 13 years at the Gastrointestinal Unit of the Federico II University of Naples, Italy. RESULTS In these series of patients the ratio of women to men was 3.33. Age at diagnosis was lower in women that in men (p < 0.05). Except for asthenia, all signs and symptoms were more frequent in women than in men. Hypochromic anemia was the most commonest finding in women and was 40% more frequent in women than in men (p < 0.001). Dyspepsia was twice as frequent in women as in men (p < 0.05); genital disorders were reported by 44% of women and by no men. Recent weight loss or low body mass index was the commonest finding in men. About 60% of men and women reported diarrhea; among patients without diarrhea, the prevalence of hypochromic anemia differed between sexes (p < 0.05), occurring in about 80% of women. CONCLUSION This study shows that the clinical presentation of celiac disease is not the same in men and women. The disease is not only more frequent in women than in men but is also more severe and more rapid. The data also suggest the need to look for celiac disease in patients with unexplained hypochromic anemia.
Flame and Smoke Detection in Substation Based on Wavelet Analysis and Convolution Neural Network

In this paper, a fire detection method based on color features, wavelet analysis, and convolution neural network is proposed. Firstly, the candidate region of flame is extracted by color segmentation method, and then the candidate region of smoke is generated by the background fuzzy model based on wavelet analysis. Then the candidate region is filtered by the trained CNN model, and the position of flame and smoke in a picture is located. Finally, a large number of fire pictures in different scenes are used to test the algorithm. The results show that this method can detect the location of flame and smoke accurately and quickly from images or videos, and can be applied to fire detection tasks in substation scenarios.
Left But Not Right Temporal Involvement in Opaque Idiom Comprehension: A Repetitive Transcranial Magnetic Stimulation Study

It has been suggested that figurative language, which includes idioms, is controlled by the right hemisphere. We tested the right hemisphere hypothesis by using repetitive transcranial magnetic stimulation (rTMS) to transiently disrupt the function of the frontal and temporal areas of the right versus left hemisphere in a group of normal participants involved in a task of opaque idiom versus literal sentence comprehension. Forty opaque, nonambiguous idioms were selected. Fifteen young healthy participants underwent rTMS in two sessions. The experiment was run in five blocks, corresponding to the four stimulated scalp positions (left frontal and temporal and right frontal and temporal) and a baseline. Each block consisted of 16 trials8 trials with idioms and 8 trials with literal sentences. In each trial, the subject was presented with a written sentence, which appeared on the screen for 2000 msec, followed by a pair of pictures for 2500 msec, one of which corresponded to the sentence. The alternative corresponded to the literal meaning for idioms and to a sentence differing in a detail in the case of literal sentences. The subject had to press a button corresponding to the picture matching the string. Reaction times increased following left temporal rTMS, whereas they were unaffected by right hemisphere rTMS, with no difference between idiomatic and literal sentences. Left temporal rTMS also reduced accuracy without differences between the two types of sentences. These data suggest that opaque idiom and literal sentence comprehension depends on the left temporal cortex.
A PSO Approach In The 3D Object Reconstruction Using Supershapes

In this paper, we propose an approach for the 3D object reconstruction from a points cloud using supershapes. Three-dimensional reconstruction is considered as an important problem of computer vision. Several research projects have targeted this domain, but rare are those who approach this problem as an optimization problem and using supershapes. In our work, we focalized on metaheuristic methods based on a population of solutions. Our choice was focused on the PSO 'Particle Swarm Optimiation' method for its ease of implementation, its speed of execution and its efficiency. The difficulty of the work lies in the good definition of the error function. It is this function that we will minimize using the PSO method until convergence. So we have a good reconstruction of the supershape.
Deep Learning Powered Question-Answering Framework for Organizations Digital Transformation

In the context of digital transformation by governments, the public sector and other organizations, many information is moving to digital platforms. Chatbots and similar question-answering systems are becoming popular to answer information queries, opposed to browsing online repositories or webpages. State-of-the-art approaches for these systems may be laborious to implement, hard to train and maintain, and also require a high level of expertise. This work explores the definition of a generic framework to systematically build question-answering systems. A sandbox implementation of this framework enables the deployment of turnkey systems, directly from already existing collections of documents. These systems can then be used to provide a question-answering system communication channel to enrich the organization digital presence.