Miracle-dz/UniFAR-FTUs huggingface dataset:
Five Months Follow-up Study of School-based Crisis Intervention for Korean High School Students Who Experienced a Peer Suicide

Background The purpose of this study was to examine post-traumatic stress, depression, anxiety, and complicated grief in Korean high school students who experienced a peer suicide. Methods A total of 956 students were included in the statistical analysis. One week after a peer suicide, a school-based crisis intervention program was conducted. The cohort was followed-up at one week and five months after a peer suicide. The program consists of screening tests, educational sessions, and further interview with psychiatric specialists for the selected group. Screening tests were conducted for all students to measure the Child Report of Post-traumatic Symptoms (CROPS), the post-traumatic stress symptoms (The University of California at Los Angeles post-traumatic stress disorder [PTSD] reaction index; UCLA-PTSD-RI), the anxiety symptoms (The Korean-Beck Anxiety Inventory; K-BAI), the depressive symptoms (The Korean-Beck Depression Inventory-II; K-BDI-II), and the complicated grief reaction (The Inventory of Complicated Grief; ICG). For statistical analysis, the SPSS Statistics 21.0 program was used. Results At baseline and five months follow-ups, 8.6% and 2.9% of the students showed post-traumatic stress symptoms. At five months follow-up, there was a statistically significant decline in the post-traumatic stress symptoms, anxiety, depression, and complicated grief among the ‘trauma group. ’ A higher proportion of the female students showed post-traumatic stress symptoms after the incident of peer suicide than the male students. Conclusion School-based crisis intervention helps improvement of trauma-related symptoms. It might be an effective way to prevent suicide spreading among students by alleviating trauma-related symptoms.
Security and privacy of big data for social networking services in cloud

Big Data (BD) is of great importance especially in wireless telecommunications field. Social Networking (SNg) is one more fast-growing technology that allows users to build their profile and could be described as web applications. Both of them face privacy and security issues. In this paper, we survey SNg, BD and Cloud Computing (CC) technology and their basic characteristics, by concentrating on the security issues of those technologies. Specifically, we aim at combining the functionality of these two technologies (i.e Big Data and Social Networking) in a CC environment, so that we can analyze the common features and ascertain the advantages of their integration related to security issues. Through this research, we present a new system-framework-network in Cloud Environment through which users of various Social Networks (SNs) will be able to exchange data and information, and primarily large-scale data (Big Data). With our proposed system, we can achive greatly improve of the communication of SN users, and thus become more safe and accurate in a Cloud environment. More specifically, this system could be established as an intermediate communication node that could be utilized in order to improve the security of SNg's users through the use of algorithms that can provide more privacy in the data related to BD technology. Also, in this work we present some measurements and results relative to our proposed system use. Finally, the opportunity to create a database through which each user can view the statistics of his interaction with the SNg is further discussed.
Learning Spatial Aversion Is Sensory-Specific in the Hematophagous Insect Rhodnius prolixus

Even though innate behaviors are essential for assuring quick responses to expected stimuli, experience-dependent behavioral plasticity confers an advantage when unexpected conditions arise. As being rigidly responsive to too many stimuli can be biologically expensive, adapting preferences to time-dependent relevant environmental conditions provide a cheaper and wider behavioral reactivity. According to their specific life habits, animals prioritize different sensory modalities to maximize environment exploitation. Besides, when mediating learning processes, the salience of a stimulus usually plays a relevant role in determining the intensity of an association. Then, sensory prioritization might reflect an heterogeneity in the cognitive abilities of an individual. Here, we analyze in the kissing bug Rhodnius prolixus if stimuli from different sensory modalities generate different cognitive capacities under an operant aversive paradigm. In a 2-choice walking arena, by registering the spatial distribution of insects over an experimental arena, we evaluated firstly the innate responses of bugs confronted to mechanical (rough substrate), visual (green light), thermal (32°C heated plate), hygric (humidified substrate), gustatory (sodium chloride), and olfactory (isobutyric acid) stimuli. In further experimental series bugs were submitted to an aversive operant conditioning by pairing each stimulus with a negative reinforcement. Subsequent tests allowed us to analyze if the innate behaviors were modulated by such previous aversive experience. In our experimental setup mechanical and visual stimuli were neutral, the thermal cue was attractive, and the hygric, gustatory and olfactory ones were innately aversive. After the aversive conditioning, responses to the mechanical, the visual, the hygric and the gustatory stimuli were modulated while responses to the thermal and the olfactory stimuli remained rigid. We present evidences that the spatial learning capacities of R. prolixus are dependent on the sensory modality of the conditioned stimulus, regardless their innate valence (i.e., neutral, attractive, or aversive). These differences might be given by the biological relevance of the stimuli and/or by evolutionary aspects of the life traits of this hematophagous insect.
MotionHacker: Motion sensor based eavesdropping on handwriting via smartwatch

Motion sensors may impose the danger of hand-writing leakage if the smartwatch installs a malicious app. By presenting a proof-of-concept system: MotionHacker, we show the usage of smartwatch app to record the motions and extract handwriting-specific features for machine learning based analysis. MotionHacker is targeted on user-independent handwriting recognition and word-level estimation for the content of the victim's handwriting, where the only limit is that victim's handwriting follows print style. Furthermore, the targeted handwriting is lowercase letters, which are more difficult than capital letters. Our experimental results show that the average accuracy of word recognition is 32.8% for 5 victims writing two graphs from a novel and a research paper each. When counting the word prediction results of top-5 items, the word recognition accuracy reaches 48.8%. The results confirm the danger of handwriting content leakage from smartwatches' motion sensors.
Determining complexity within the crowd movements

The segmentation of the moving and non-moving parts in an image within the crowd analysis studies is a crucial result in terms of understanding the behaviour. However, in many studies carried out, moving areas are assessed as making a set of movement that are similar in terms of location and of acceleration. In this study, what kind of interaction there exists among the people in a high-density crowd movement was studied. Initially, the motion data is represented as trajectories by using the optical flow and particle advection methods. The image is divided into equal hexagons and each area's topological entropy value is determined through Finite Time Braid Entropy (FTBE). This value depends on the complexity of the spiral structure that the trajectories generate throughout the movement and this value shows how much effective it is in the area. Eventually, through the complexity map that was generated, the behaviour of the crowd could be better understood and assessed. Also, in this way, possible abnormal conditions in the area can be predicted by monitoring each region and by setting off from the changes in the values of Braid entropy in time. The method was tested using the high-density crowd movements in the UCF database and results were presented.
Structure-Based Design of Porcine Circovirus Type 2 Chimeric VLPs (cVLPs) Displays Foreign Peptides on the Capsid Surface

Although porcine circovirus-like particles can function as a vector to carry foreign peptides into host cells, displaying foreign peptides on the surface of virus-like particles (VLPs) remains challenging. In this study, a plateau, consisting of the middle portion of Loop CD (MP-Lcd) from two neighboring subunits of PCV2 capsid protein (Cap), was identified as an ideal site to insert various foreign peptides or epitopes and display them on the surface of PCV2 VLPs. One of the goals of this work is to determine if the surface pattern of this plateau can be altered without compromising the neutralizing activity against PCV2 infections. Therefore, biological roles of MP-Lcd regarding VLPs assembly, cell entry, and antigenicity were investigated to determine whether this was a universal site for insertion of foreign functional peptides. Three-dimensional (3D) structure simulations and mutation assays revealed MP-Lcd was dispensable for PCV2 Cap assembly into VLPs and their entry into host cells. Notably, substitution of MP-Lcd with a foreign peptide, caused surface pattern changes around two-fold axes of PCV2 VLPs based on 3D structure simulation, but was not detrimental to VLPs assembly and cell entry. Moreover, this substitution had no adverse effect on eliciting neutralizing antibodies (NAbs) against PCV2 infection in pigs. In conclusion, MP-Lcd of the PCV2 Cap was a promising site to accommodate and display foreign epitopes or functional peptides on the surface of PCV2 VLPs. Furthermore, chimeric VLPs (cVLPs) would have potential as bivalent or multivalent vaccines and carriers to deliver functional peptides to target cells.
Evolution of patients’ socio-behavioral characteristics in the context of DAA: Results from the French ANRS CO13 HEPAVIH cohort of HIV-HCV co-infected patients

Background Direct-acting antivirals (DAA) have dramatically increased HCV cure rates with minimal toxicity in HIV-HCV co-infected patients. This study aimed to compare the socio-behavioral characteristics of patients initiating pegylated-interferon (PEG-IFN)-based HCV treatment with those of patients initiating DAA-based treatment. Methods ANRS CO13 HEPAVIH is a national multicenter prospective cohort started in 2005, which enrolled 1,859 HIV-HCV co-infected patients followed up in French hospital outpatient units. Both clinical/biological and socio-behavioral data were collected during follow-up. We selected patients with socio-behavioral data available before HCV treatment initiation. Results A total of 580 patients were included in this analysis. Of these, 347 initiated PEG-IFN-based treatment, and 233 DAA-based treatment. There were significant differences regarding patient mean age (45 years±6 for the PEG-IFN group vs. 52 years±8 for the DAA group, p<0.001), unstable housing (21.4% vs. 11.2%, p = 0.0016), drug use (44.7% vs. 29.6%, p = 0.0003), regular or daily use of cannabis (24.3% vs. 15.6%, p = 0.0002), a history of drug injection (68.9% vs 39.0%, p<0.0001) and significant liver fibrosis (62.4% vs 72.3%, p = 0.0293). In multivariable analysis, patients initiating DAA-based treatment were older than their PEG-IFN-based treatment counterparts (aOR = 1.17; 95%CI [1.13; 1.22]). Patients receiving DAA treatment were less likely to report unstable housing (0.46 [0.24; 0.88]), cannabis use (regular or daily use:0.50 [0.28; 0.91]; non-regular use: 0.41 [0.22; 0.77]), and a history of drug injection (0.19 [0.12; 0.31]). Conclusion It is possible that a majority of patients who had socio-economic problems and/or a history of drug injection and/or a non-advanced disease stage were already treated for HCV in the PEG-IFN era. Today, patients with unstable housing conditions are prescribed DAA less frequently than other populations. As HCV treatment is prevention, improving access to DAA remains a major clinical and public health strategy, in particular for individuals with high-risk behaviors.
Knee/Elbow Estimation Based on First Derivative Threshold

Estimating the knee/elbow point in error curves is a challenging task. However, most of the time these points represent ideal compromises or ideal parameters for several tasks, methods and algorithms. Our focus is determining the ideal number of clusters autonomously. In this paper, we formalize the notion of knee/elbow point, discuss known methods to determine it and propose our own method. Contrary to most methods, ours is resilient to long tails in the error curve. This behaviour is especially important when considering autonomous methods. The proposed method outperformed the competition on five datasets from UCI Machine Learning Repository.
Finite-time tracking control of underactuated overhead cranes

In this paper, the finite time tracking control problem is addressed for the underactuated overhead cranes, compared with many existing results which aim to achieve the asymptotic stable as time goes to infinity. Firstly, the cranes dynamic is changed into a quasi-chain-of-integrators form by introducing a coordinate transform. Then, based on the transformed systems, a finite time tracking control algorithm via backstepping method in conjunction a dynamic surface control (DSC) technique applying a nonlinear first-order filter at each step is proposed which not only avoids the problem of explosion of complexity but also achieves the finite-time tracking. According to finite time theory and Lyapunov stability theory, it is proven that under the proposed control scheme, the tracking error can converge into arbitrarily small neighborhoods around zero in finite time. Finally, a simulation is done to verify the feasibility of the proposed control method.
Research on Analysis and Suppression Strategy of Subsynchronous Oscillation in Wind Power Grid-Connected System

With the development and utilization of wind energy, as well as the large number of power electronic devices access to power system, subsynchronous oscillation has a serious threat to the safe and stable operation of wind power grid-connected system. In view of this phenomenon, this paper has carried on the research of analysis and suppression strategy of subsynchronous oscillation in wind power grid-connected system. Firstly, the model of wind power grid-connected system is established by using double-fed induction generator (DFIG). Then, the subsynchronous oscillation is simulated by PSCAD/EMTDC, and the mechanism of subsynchronous oscillation is analyzed by frequency scanning method and time domain simulation method. The subsynchronous oscillation suppression strategy based on bypass current compensation is verified by simulation.
Methods and Tools for GDPR Compliance Through Privacy and Data Protection Engineering

In this position paper we posit that, for Privacy by Design to be viable, engineers must be effectively involved and endowed with methodological and technological tools closer to their mindset, and which integrate within software and systems engineering methods and tools, realizing in fact the definition of Privacy Engineering. This position will be applied in the soon-to-start PDP4E project, where privacy will be introduced into existent general-purpose software engineering tools and methods, dealing with (risk management, requirements engineering, model-driven design, and software/systems assurance).
Transmission electron microscopy of topochemical conversion interface between La 2 Ti 2 O 7 reactive template and perovskite product Li 0 . 16 La 0 . 62 TiO 3 electrolyte

Article history: Received 13 June 2016 Received in revised form 4 September 2016 Accepted 5 September 2016 Available online 11 September 2016 The topochemical conversion interface between a layered perovskite La2Ti2O7 and an ordered regular perovskite Li0.16La0. 62TiO3, (LLTO) electrolyte was investigated using high-resolution transmission electron microscopy and scanning-transmission electronmicroscopy (STEM) for platelike La2Ti2O7 particles semi-reacted (at 1273K)with the complementary reactants Li4Ti5O12 and TiO2. High-temperatureX-ray diffraction analysis confirmed that regular perovskite-type product was formed in situ at 1173 K and above without the formation of any intermediate phases. The crystallographic relationship between the product and the template at the interfacewas determined by selected area electron diffraction to be orthorhombic LLTO [001](110) || monoclinic La2Ti2O7 [001](100). STEM revealed that the alternating La-rich and La-poor planes in the LLTO phase were aligned perpendicular to the interface, which was parallel to the interlayers, i.e., the (200) planes of the La2Ti2O7 template. At the interface, a unit-cell-thick La2Ti2O7 or a block of four-layered TiO6 octahedra between two interlayers is a basic unit converted into a regular perovskite-type product by shifting the block with La along the c-axis. This microscopic observation of the interface suggests the possibility of designing domain structures with improved ionic conductivity. © 2016 Elsevier B.V. All rights reserved.
Modeling and analysis of a bionic flexible arm inspired by octopus arm

Compared with traditional robots, flexible robots have higher degrees of freedom and greater flexibility. As one of the sources of inspiration for flexible robots, octopus has very flexible arms, so the imitating octopus arm robot has become the focus of people's research. In order to analyze the operation of octopus arm, it is very important to establish its model ahead of time. However, the inherent high degree of freedom, underactuation and nonlinearity of octopus arm lead to very difficult modeling of bionic flexible arm. Therefore, according to the physiological structure of the octopus arm, put forward a method of flexible arm geometric segmentation modeling, we use SolidWorks to set up a flexible arm of the physical model, and then import it into Matlab are analyzed, the simulation results show that the flexible manipulator system is established and effective.
Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform

Sputum sounds are biological signals used to evaluate the condition of sputum deposition in a respiratory system. To improve the efficiency of intensive care unit (ICU) staff and achieve timely clearance of secretion in patients with mechanical ventilation, we propose a method consisting of feature extraction of sputum sound signals using the wavelet transform and classification of sputum existence using artificial neural network (ANN). Sputum sound signals were decomposed into the frequency subbands using the wavelet transform. A set of features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN system, trained using the Back Propagation (BP) algorithm, was implemented to recognize the existence of sputum sounds. The maximum precision rate of automatic recognition in texture of signals was as high as 84.53%. This study can be referred to as the optimization of performance and design in the automatic technology for sputum detection using sputum sound signals.
Machine learning based smartphone spectrometer for harmful dyes detection in water

The development of low cost and portable instrumentations to monitor water quality without access to sophisticated laboratory tools has attracted an increasing amount of attention recently. In this study, we design a portable, cost-effective, field-deployable, plastic fiber based smartphone spectrometer system to detect harmful dyes in water. The proposed system uses a custom cradle to transmit built-in flash light via fiber cable, and to capture the visible absorbance spectroscopy by camera of smartphone. The captured images were trained and tested with machine learning algorithms using color features. Experimental investigations for detection of methylene blue (MB) absorbance in water shows that the proposed system can detect the MB with % 100 accuracy. With advanced microfabrication and data processing tools, our proposed system will be more compact, lighter, cost-efficient and will be enhanced with more features for the next generation of sustainable water management applications.
Reduced Effort Does Not Imply Slacking: Responsiveness to Error Increases With Robotic Assistance

In both neurorehabilitation and functional augmentation, the patient or the user’s muscular effort diminishes when the movement of their limb is supported by a robot. Is this relaxation a result of “slacking” by letting the robot take-over the movement, resulting in less responsiveness in the task? To address this question, we tested subjects who controlled a virtual cursor isometrically to track a moving target without and with different assistants. We measured the force applied by the subject as a metric for effort and estimated their control gain as the metric for responsiveness in the task. Although subjects applied less force with position assistance, the norm of the control gain increased with all assistants, i.e., they applied proportionately larger forces for the same difference between the cursor and the target states. Furthermore, assisting velocity errors improved baseline performance without reducing effort. Though all assistants improved task performance, the control gain adapted differently to position and velocity assistance. Position assistance was exploited to accurately track the target, whereas velocity assistance was treated as a disturbance, and was effectively nullified as it prevented submovements that minimized positional error. Our results show that robotic assistance increases task responsiveness in healthy individuals and that assisting velocity errors could boost patient performance without reducing their motor effort.
Construction of Lyapunov-Krasovskii functionals for a class of nonlinear difference systems with delay

Nonlinear systems of difference equations with delay and nonlinearities of a sector type are studied. New constructions of Lyapunov-Krasovskii functionals are proposed for such sys-tems. With the aid of these functionals, conditions are obtained under which solutions of the considered systems are uniformly ultimately bounded for any value of delay. An example is given to demonstrate the effectiveness of the proposed approaches.
Artifacts and noise removal for electroencephalogram (EEG): A literature review

Electroencephalogram (EEG) is a signal collected from the human brain to study and analyze the brain activities. However, raw EEG may be contaminated with unwanted components such as noises and artifacts caused by power source, environment, eye blinks, heart rate and muscle movements, which are unavoidable. These unwanted components will effect the analysis of EEG and provide inaccurate information. Therefore, researchers have proposed all kind of approaches to eliminate unwanted noises and artifacts from EEG. In this paper, a literature review is carried out to study the works that have been done for noise and artifact removal from year 2010 up to the present. It is found that conventional approaches include ICA, wavelet based analysis, statistical analysis and others. However, the existing ways of artifacts removal cannot eliminate certain noise and will cause information lost by directly discard the contaminated components. From the study, it is shown that combination of conventional with other methods is popularly used, as it is able to improve the removal of artifacts. The current trend of artifacts removal makes use of machine learning to provide an automated solution with higher efficiency.