Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining User Behaviour Using System Calls To Prevent Internal Intrusions

Today the most common method of authentication used in modern day systems is username and password authentication.It has been found that many coworkers share their login credentials with each other which basically makes username and password authentication as one of the most susceptible points that can be exploited. Most of the systems that are in use today protect the system against the external threats only by using firewalls and intrusion detection systems, which leaves the system susceptible to all the internal threats.Internal threats arise from the individuals who are valid and genuine users of the system and are hard to detect as nobody is looking for them. In order to solve the problem of internal attacks and prevent the system from data leaks that take place from the inside some work needs to be done.It has been found out that by analysing System calls generated by operations are helpful in determining the behaviour of a user. So, we propose a method to determine the behaviour of a user from system calls using data mining and forensic analysis to prevent the system from internal intrusions. The proposed system will keep a track of all the users in the system and make their profiles which will contain their usage habits which are used as forensic features to determine whether a user logged in the system is the valid user or not by comparing his current system usage behaviour with the patterns already collected in the users personal profile which is managed by the admin.
Event-triggered Adaptive Output Consensus Tracking Control of Uncertain Nonlinear Multi-agent Systems*

This paper investigates the distributed adaptive output consensus tracking problem for uncertain nonlinear multi-agent systems with event-triggered control inputs and event-triggered communication. A new event-based adaptive output consensus tracking scheme is proposed under directed graph condition. To compensate the effects of uncertain external disturbances and event-triggered measurement errors on the close-loop system stability, an additional parameter estimator is introduced in each agent to estimate the unknown lumped parameter consisting of the upper bound of external disturbances and the triggering threshold. Besides, both continuous monitoring of neighbors’ states and the information of global parameters are not needed in designing the triggering mechanism. It is shown that all the signals in the closed-loop system are globally uniformly bounded and the desired output consensus tracking can be achieved.
Multi Resonant Reactive Shield for Reducing the Electromagnetic Fields from Wireless Charging Electric Vehicle

The demand for wireless charging (WLC) electric vehicles (EVs) increase due to the EVs rely on limited capacity batteries to power their operation and to drive despite of the heavy weight and inconvenience of charging methods. However, the WLC systems use the time varying magnetic field to transfer the power wireless which can be a potential danger to user of other electronic devices. In this paper, we propose a topology and design methods for multi resonant reactive shield to suppress the EMF and electromagnetic interference (EMI) from WLC system. The proposed shield is located around the periphery of the WLC coil. The proposed shield contains two closed loop and two capacitors to generate the multi resonance. The proposed coil is applied to WLC EV and the shielding performance (SP) and the power transfer efficiency (PTE) are verified using measurements.
Defense Scheme to Avoid Voltage Collapse in Transmission Lines: A Case Study of Paiton- Situbondo Transmission Lines

Reliability, and continuity of service to customers is the top priority in the power system defense scheme. One important criterion is avoiding instability. Most conventional stability analysis is related to power swings due to frequency variations because the frequency is directly affected by power plant performance. However, in the transmission line, voltage instability is crucial because it practically give direct impact on customers. Especially for high voltage consumers, poor voltage quality is very influential on the performance of industrial equipment such as electric motors, textile machinery, and other electronic equipment, so the voltage quality must be maintained to avoid complaints from these consumers. The merge of frequency and voltage criteria instability is becoming the main focus in recent studies. In a large power system, voltage stability problem usually happens due to excess of electrical demand and deficiency of power generation. To avoid voltage instability, under-voltage load shedding can be an option to remove buses with unstable voltage in the power system. This paper focuses on the Paiton-Situbondo 150 kV transmission lines as the backbone of the power transfer from Java to Bali to investigating the role and effectiveness of the combination of frequency and voltage threshold to maintain the system stability when the faults occur on the transmission lines.
Cholecystokinin-Mediated Neuromodulation of Anxiety and Schizophrenia: A “Dimmer-Switch” Hypothesis

Cholecystokinin (CCK), the most abundant brain neuropeptide, is involved in relevant behavioral functions like memory, cognition, and reward through its interactions with the opioid and dopaminergic systems in the limbic system. CCK excites neurons by binding two receptors, CCK1 and CCK2, expressed at low and high levels in the brain, respectively. Historically, CCK2 receptors have been related to the induction of panic attacks in humans. Disturbances in brain CCK expression also underlie the physiopathology of schizophrenia, which is attributed to the modulation by CCK1 receptors of the dopamine flux in the basal striatum. Despite this evidence, neither CCK2 receptor antagonists ameliorate human anxiety nor CCK agonists have consistently shown neuroleptic effects in clinical trials. A neglected aspect of the function of brain CCK is its neuromodulatory role in mental disorders. Interestingly, CCK is expressed in pivotal inhibitory interneurons that sculpt cortical dynamics and the flux of nerve impulses across corticolimbic areas and the excitatory projections to mesolimbic pathways. At the basal striatum, CCK modulates the excitability of glutamate, the release of inhibitory GABA, and the discharge of dopamine. Here we focus on how CCK may reduce rather than trigger anxiety by regulating its cognitive component. Adequate levels of CCK release in the basal striatum may control the interplay between cognition and reward circuitry, which is critical in schizophrenia. Hence, it is proposed that disturbances in the excitatory/inhibitory interplay modulated by CCK may contribute to the imbalanced interaction between corticolimbic and mesolimbic neural activity found in anxiety and schizophrenia.
Predictive learning extracts latent space representations from sensory observations

Neural networks have achieved many recent successes in solving sequential processing and planning tasks. Their success is often ascribed to the emergence of the task’s low-dimensional latent structure in the network activity – i.e., in the learned neural representations . Similarly, biological neural circuits and in particular the hippocampus may produce representations that organize semantically related episodes. Here, we investigate the hypothesis that representations with low-dimensional latent structure, reﬂecting such semantic organization, result from learning to predict observations about the world. Speciﬁcally, we ask whether and when network mechanisms for sensory prediction coincide with those for extracting the underlying latent variables. Using a recurrent neural network model trained to predict a sequence of observations in a simulated spatial navigation task, we show that network dynamics exhibit low-dimensional but nonlinearly transformed representations of sensory inputs that capture the latent structure of the sensory environment. We quantify these results using nonlinear measures of intrinsic dimensionality which highlight the importance of the predictive aspect of neural representations, and provide mathematical arguments for when and why these representations emerge. We focus on how our results can aid the analysis and interpretation of experimental data.
Women health: Psychological and most prominent somatic problems in 3-year follow-up in Bosnian refugees

Background: Little is known about gender differences among people exposed to war related trauma. Aim of this study is to explore gender differences in health status and comorbidity of mental and physical disorders in a cohort of Bosnian refugees followed up for 3 years (1996–1999). Methods: This longitudinal study included 534 subjects followed up for 3 years. The interviews were conducted in refugee camps in Varaždin, Croatia in Bosnian language. Data were collected using Harvard Trauma Questionnaire (Bosnian version) and Hopkins Checklist-25, respectively. Physical health disorders were self-reported. Results: In both assessments there was a statistically significant difference between men and women in the number of physical health disorders, even when results were controlled for educational status. Although there was no difference in total number of symptoms in both assessments (F = 0.32; df = 1; p > 0.05 and F = 1.15; df = 1; p > 0.05), important physical health disorders were significantly more frequent among women than in men in different educational groups, namely high blood pressure and cardiovascular diseases, arthritis, and anaemia. Asthma, tuberculosis, cirrhosis of the liver, ulcer and epilepsy were more frequent in men than in women. There were no differences in frequencies of psychiatric disorders at baseline, but frequency of psychiatric disorders in women was higher at endpoint for uneducated respondents. There was significant difference compared to men in group of respondents without formal education, but only in comorbidity of PTSD and depression which was more often present in females (22.1%) than in males (3.6%). Conclusion: Our findings indicate the importance of gender and education on mental and physical health of people exposed to warrelated trauma. Long term health monitoring and programs, especially related to women’s health are needed in order to avoid lasting consequences.
Current Targeted Therapies for the Fight against Non-Small Cell Lung Cancer

Lung cancers contribute to the greatest number of cancer-related deaths worldwide and still pose challenges in response to current treatment strategies. Non-small cell lung cancer (NSCLC) accounts for over 85% of lung cancers diagnosed in the United States and novel therapeutics are needed for the treatment of this disease. First and second generation targeted therapies against specific mutated or rearranged oncogenes in NSCLCs show anti-tumor activity and also increase survival. However, many NSCLC patients eventually develop resistance to these therapies or do not properly respond if they have central nervous system metastases. Thus, this review summarizes recent developments and findings related to the generation of novel targeted therapies recently or currently being developed to tackle hurdles that prior therapies were not able to overcome.
Backstepping speed controller design for a multi-phase permanent magnet synchronous motor drive

This paper deals with the synthesis of a speed control strategy for a six-phase permanent magnet synchronous motor (PMSM) drive based on backstepping controller (BC). The Backstepping control is a systematic and recursive design methodology for nonlinear feedback control. The results demonstrate that backstepping controller has the advantage of rapid response, no overshoot and stability compared with PI controller.
Impact of Patch-Size on Classification Accuracy of Latent Fingerprint Image in Stacked Convolutional Auto-encoder based Segmentation and Detection

Latent fingerprints are (un)intentional finger skin impressions left as ridge patterns at crime scenes. The significant challenge in latent fingerprint segmentation is extracting complex, multiple, noisy foreground fingermarks while maintaining the performance of the system. The work presented in this paper provides a method to extract fingerprints from the latent fingerprint images dataset (IIIT-D) using a stack of convolutional auto-encoders. The idea is to early detect the structure of interest from the image using a color-based mask. These structures are divided into equal-sized patches and classification of these patches into fingermark or background class-labeling is achieved using staked convolutional autoencoders. To establish stable layered architecture and an optimal amount of information in patches as input to these layers, the impact of different patch-size is analyzed on various stacks of the layered architecture of the underlying deep neural network. Reduced feature learning of an autoencoder and pre- trained convolutional neural network improves the patch classification accuracy thereby increasing segmentation accuracy.
Neuroticism predicts informant reported cognitive problems through health behaviors

Abstract Objective Personality traits have been linked to cognitive impairment, though work is needed to understand the mechanisms involved. Research also needs to consider alternative markers of cognitive impairment, such as informant report measures. The aim of the current study was to examine the role of health behaviors and social engagement as mediators for the relationship between personality and informant reported cognitive problems. It was expected that neuroticism would predict cognitive problems through negative health behaviors, while conscientiousness might predict cognitive problems through positive health behaviors. Methods Using data from the St. Louis Personality and Aging Network study at three time points, spanning approximately 2.27 years (N = 829, M age = 65.95), correlations were computed between the Big Five personality traits and health behaviors at wave 1, social engagement at wave 2, and informant reported cognitive problems at wave 3. Mediation tests examined whether health behaviors and social engagement explained the relationships found between personality and informant reported cognitive problems. Results Findings showed that neuroticism at wave 1 significantly predicted informant reported cognitive problems at wave 3 and that health behaviors, specifically wellness maintenance, partially explained this relationship. No significant associations were found between informant reported cognitive problems and conscientiousness, agreeableness, extraversion, openness, or social engagement. Conclusion This study supports claims that neuroticism predicts later cognitive problems and expands on previous literature by demonstrating this relationship using an informant report measure. Furthermore, we found that health behaviors, and specifically wellness maintenance, account for some of the relationship between neuroticism and informant reported cognitive problems.
Cerebral aneurysm: De novo genesis and rupture within 15 days

We report the first case of de novo formation of a small 4‐ × 3‐mm symptomatic aneurysm with subsequent subarachnoid haemorrhage (SAH) within 15 days. A pre‐existing aneurysm was excluded by magnetic resonance angiography, as well as conventional angiography. In this case, there was no history of SAH, which is in contrast to other reported cases of de novo aneurysms that developed after previous aneurysm rupture.
DriveCare: A Real-Time Vision Based Driver Drowsiness Detection Using Multiple Convolutional Neural Networks With Kernelized Correlation Filters (MCNN-KCF)

Driver Drowsiness is one of the most significant safety issues facing the road transport industry today, and it reduces vigilance, alertness, and concentration to perform the attention-based activities such as driving. When the driver is in a fatigue state, the facial expressions are a bit different from those in the normal state. Here a new system called DriveCare, which determines the drivers’ fatigue state, like duration of eyes closure, blinking, and yawning, using video images, without equipping their bodies with any devices, is introduced. A novel face-tracking algorithm called Multiple Convolutional Neural Networks with Kernelized Correlation Filters (MCNN-KCF) is used to improve the tracking accuracy. Further, a detection method is used for extracting the facial features of the driver. Then with the help of these facial features, we evaluate the Driver’s state. By combining the features extracted by the eyes and mouth, DriveCare can alert the driver using a drowsiness warning tone. Different experimental results showed that the DriveCare achieved around a 95% accuracy.
Measurement of the human cadaver ossicle vibration amplitude by fiber-optic interferometry

In this paper we present the results obtained by contactless measurement of vibration of the incus inside the middle ear of a human cadaver, exposed to an external calibrated sound source. The measurement configuration was a Michelson fiber-optic interferometer based on a 3x3 fiber optic coupler, with a VCSEL source. The interferometric signal fading was solved using two quasiquadrature signals and specialized signal processing. The ossicle reflection was augmented by a very light piece of retroreflective material attached to the bone. We measured the amplitude of vibration at different sound pressure levels, independently monitored by a decibel meter placed in the proximity of the cadaver ear. The accuracy of the measurement technique was proved by using a calibrated piezo transducer exposed to vibrate with amplitude of 7nm at 1kHz. We performed the experiments on 8 cadavers in total. The measured amplitudes were found to be in the range between 1 pm and 1 nm, under the sound excitation of 40-90 dB SPL @ 1kHz, respectively.
6LoWPAN Protocol Based Infrared Sensor Network Human Target Locating System

This paper proposes an infrared sensor human target locating system for the Internet of Things. In this design, the wireless sensor network is designed and developed to detect human targets by using 6LoWPAN protocol and pyroelectric infrared (PIR) sensors. Based on the detection data acquired by multiple sensor nodes, K-means++ clustering algorithm combined with cost function is applied to complete human target location in a 10m×10m detection area. The experimental results indicate the human locating system works well and the user can view the location information on the terminal devices.
The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions (e.g., adverse cardiac events, pneumonia, and clinical deterioration) and stressors, including emotional stress, cognitive load, heat, cold, physical effort, and exercise-induced fatigue. The sensitivity of respiratory rate to these conditions is superior compared to that of most of the other vital signs, and the abundance of suitable technological solutions measuring respiratory rate has important implications for healthcare, occupational settings, and sport. However, respiratory rate is still too often not routinely monitored in these fields of use. This review presents a multidisciplinary approach to respiratory monitoring, with the aim to improve the development and efficacy of respiratory monitoring services. We have identified thirteen monitoring goals where the use of the respiratory rate is invaluable, and for each of them we have described suitable sensors and techniques to monitor respiratory rate in specific measurement scenarios. We have also provided a physiological rationale corroborating the importance of respiratory rate monitoring and an original multidisciplinary framework for the development of respiratory monitoring services. This review is expected to advance the field of respiratory monitoring and favor synergies between different disciplines to accomplish this goal.
Executive Functions and Attention in Childhood Epilepsies: A Neuropsychological Hallmark of Dysfunction?

Abstract Objective: Patients with epilepsy are at risk for several lifetime problems, in which neuropsychological impairments may represent an impacting factor. We evaluated the neuropsychological functions in children suffering from three main epilepsy categories. Further, we analyzed the longitudinal evolution of the neuropsychological profile over time. Methods: Patients undergoing neuropsychological evaluation at our Department from 2012 to 2018 were identified retrospectively. We selected patients aged 6–16 years and with at least two evaluations. Three epilepsy categories were considered: focal/structural, focal self-limited, and idiopathic generalized. Each evaluation included the same structured assessment of main neuropsychological domains. The effect of the epilepsy category, illness duration, seizure status, and medication was computed in multilevel models. Results: We identified 103 patients (focal self-limited = 27; focal/structural = 51; and idiopathic generalized = 25), for 233 evaluations. The majority of deficits were reported in attention and executive functions (>30% of patients); the results were dichotomized to obtain global indexes. Multilevel models showed a trend toward statistical significance of category of epilepsy on the global executive index and of illness duration on global attention index. Illness duration predicted the scores of executive and attention tasks, while category and medication predicted executive task performance. Focal/structural epilepsies mostly affected the executive domain, with deficits persisting over time. By contrast, an ameliorative effect of illness duration for attention was documented in all epilepsies. Conclusions: This study offers lacking information about the evolution of deficits in time, the role of epilepsy category, and possible psychological implications for high-order cognitive skills, central in several social and academic problems.
Reduced neuropathy target esterase in pre‐eclampsia suppresses tube formation of HUVECs via dysregulation of phospholipid metabolism

Recently, studies have shown that neuropathy target esterase (NTE) is essential to placental and normal blood vessel development. However, whether it is involved in abnormal placenta angiogenesis of pre‐eclampsia remains unknown. Thus, our aim was to observe the expression of NTE in pre‐eclamptic placentas and its effects and mechanism of NTE on the migration and the tube formation of human umbilical vein endothelial cells (HUVECs). Immunohistochemical staining showed that the NTE protein was intensely located in blood vessels of the normal pregnant placenta. However, western blot revealed that the expression level of NTE protein was significantly reduced in pre‐eclamptic placenta. The results indicated that overexpression of NTE significantly promoted the migration and the tube formation of HUVECs compared with those of the control and scramble short hairpin RNA (shRNA) group. Conversely, NTE shRNA obviously inhibited the migration and the tube formation of HUVECs. Additionally, chromatography assay evidenced that NTE overexpression significantly reduced the level of phosphatidylcholine (PC) of HUVECs, but NTE shRNA obviously increased the level of PC of HUVECs. Furthermore, exogenous PC and lysophosphatidylcholine (LPC) significantly inhibited the tube formation of HUVECs in a dose‐dependent manner. Collectively, our results suggest that reduced NTE in placenta may contribute to abnormal placenta angiogenesis of pre‐eclampsia via the dysregulation of PC and LPC metabolism.