Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting malicious activities with user‐agent‐based profiles

Hypertext transfer protocol (HTTP) has become the main protocol to carry out malicious activities. Attackers typically use HTTP for communication with command‐and‐control servers, click fraud, phishing and other malicious activities, as they can easily hide among the large amount of benign HTTP traffic. The user‐agent (UA) field in the HTTP header carries information on the application, operating system (OS), device, and so on, and adversaries fake UA strings as a way to evade detection. Motivated by this, we propose a novel grammar‐guided UA string classification method in HTTP flows. We leverage the fact that a number of ‘standard’ applications, such as web browsers and iOS mobile apps, have well‐defined syntaxes that can be specified using context‐free grammars, and we extract OS, device and other relevant information from them. We develop association heuristics to classify UA strings that are generated by ‘non‐standard’ applications that do not contain OS or device information. We provide a proof‐of‐concept system that demonstrates how our approach can be used to identify malicious applications that generate fake UA strings to engage in fraudulent activities. Copyright © 2015 John Wiley & Sons, Ltd.
Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution

Depression is a major contributor to the overall global burden of diseases. Traditionally, doctors diagnose depressed people face to face via referring to clinical depression criteria. However, more than 70% of the patients would not consult doctors at early stages of depression, which leads to further deterioration of their conditions. Meanwhile, people are increasingly relying on social media to disclose emotions and sharing their daily lives, thus social media have successfully been leveraged for helping detect physical and mental diseases. Inspired by these, our work aims to make timely depression detection via harvesting social media data. We construct well-labeled depression and non-depression dataset on Twitter, and extract six depression-related feature groups covering not only the clinical depression criteria, but also online behaviors on social media. With these feature groups, we propose a multimodal depressive dictionary learning model to detect the depressed users on Twitter. A series of experiments are conducted to validate this model, which outperforms (+3% to +10%) several baselines. Finally, we analyze a large-scale dataset on Twitter to reveal the underlying online behaviors between depressed and non-depressed users.
Lay health worker led intervention for depressive and anxiety disorders in India: impact on clinical and disability outcomes over 12 months

Background Depressive and anxiety disorders (common mental disorders) are the most common psychiatric condition encountered in primary healthcare. Aims To test the effectiveness of an intervention led by lay health counsellors in primary care settings (the MANAS intervention) to improve the outcomes of people with common mental disorders. Method Twenty-four primary care facilities (12 public, 12 private) in Goa (India) were randomised to provide either collaborative stepped care or enhanced usual care to adults who screened positive for common mental disorders. Participants were assessed at 2, 6 and 12 months for presence of ICD-10 common mental disorders, the severity of symptoms of depression and anxiety, suicidal behaviour and disability levels. All analyses were intention to treat and carried out separately for private and public facilities and adjusted for the design. The trial has been registered with clinicaltrials.gov (NCT00446407). Results A total of 2796 participants were recruited. In public facilities, the intervention was consistently associated with strong beneficial effects over the 12 months on all outcomes. There was a 30% decrease in the prevalence of common mental disorders among those with baseline ICD-10 diagnoses (risk ratio (RR) = 0.70, 95% CI 0.53–0.92); and a similar effect among the subgroup of participants with depression (RR = 0.76, 95% CI 0.59–0.98). Suicide attempts/plans showed a 36% reduction over 12 months (RR = 0.64, 95% CI 0.42–0.98) among baseline ICD-10 cases. Strong effects were observed on days out of work and psychological morbidity, and modest effects on overall disability. In contrast, there was little evidence of impact of the intervention on any outcome among participants attending private facilities. Conclusions Trained lay counsellors working within a collaborative-care model can reduce prevalence of common mental disorders, suicidal behaviour, psychological morbidity and disability days among those attending public primary care facilities.
Hippocampal Place Cell Firing Patterns Can Induce Long-Term Synaptic Plasticity In Vitro

In the hippocampus, synaptic strength between pyramidal cells is modifiable by NMDA receptor (NMDAR)-dependent long-term potentiation (LTP) and long-term depression (LTD), both of which require coincident presynaptic and postsynaptic activity. In vivo, many pyramidal cells exhibit location-specific activity patterns and are known as “place cells. ” The combination of these factors suggests that synaptic plasticity will be induced at synapses connecting place cells with overlapping firing fields, because such cells fire coincidentally when the rat is in a specific part of the environment. However, this prediction, which is important for models of how long-term synaptic plasticity can be used to encode space in the hippocampal network, has not been tested. To investigate this, action potential time series recorded simultaneously from place cells in freely moving rats were replayed concurrently into postsynaptic CA1 pyramidal cells and presynaptic inputs during perforated patch-clamp recordings from adult hippocampal slices. Place cell firing patterns induced large, pathway-specific, NMDAR-dependent LTP that was rapidly expressed within a few minutes. However, place-cell LTP was induced only if the two place cells had overlapping firing fields and if the cholinergic tone present in the hippocampus during exploration was restored by bath application of the cholinergic agonist carbachol. LTD was never observed in response to place cell firing patterns. Our findings demonstrate that spike patterns from hippocampal place cells can robustly induce NMDAR-dependent LTP, providing important evidence in support of a model in which spatial distance is encoded as the strength of synaptic connections between place cells.
Set-Valued Extensions of Fuzzy Logic: Classification Theorems

Experts are often not 100% confident in their statements. In traditional fuzzy logic, the expert's degree of confidence in each of his or her statements is described by a number from the interval [0,1]. However, due to similar uncertainty, an expert often cannot describe his or her degree by a single number. It is therefore reasonable to describe this degree by, e.g., a set of numbers. In this paper, we show that under reasonable conditions, the class of such sets coincides either with the class of all 1-point sets (i.e., with the traditional fuzzy set set of all numbers), or with the class of all subintervals of the interval [0,1], or with the class of all closed subsets of the interval [0,1]. Thus, if we want to go beyond standard fuzzy logic and still avoid sets of arbitrary complexity, we have to use intervals. These classification results shows the importance of interval-valued fuzzy logics.
Intraoperative assessment of tumor margins during glioma resection by desorption electrospray ionization-mass spectrometry

Significance Gliomas infiltrate into surrounding healthy brain tissue. Microsurgical resection aims for maximal tumor resection while minimizing morbidity. Surgical margins are defined based on the surgeon’s experience, visual observation, and neuronavigation. Surgical margin assessment is rarely undertaken intraoperatively due to time constraints and unreliability of such evaluation. Routine, pathologic intraoperative examination provides no molecular information. Molecular measurements using mass spectrometry can be made rapidly on tissue during surgery to identify tissue types, estimate tumor infiltration, and recognize the presence of prognostic mutations by monitoring oncometabolites and phospholipids. This intraoperative study demonstrates the power of mass spectrometry in assessing diagnostic and prognostic information on discrete surgeon-defined points along the resection margins to improve tumor resection, even in regions without MRI contrast enhancement. Intraoperative desorption electrospray ionization-mass spectrometry (DESI-MS) is used to characterize tissue smears by comparison with a library of DESI mass spectra of pathologically determined tissue types. Measurements are performed in the operating room within 3 min. These mass spectra provide direct information on tumor infiltration into white or gray brain matter based on N-acetylaspartate (NAA) and on membrane-derived complex lipids. The mass spectra also indicate the isocitrate dehydrogenase mutation status of the tumor via detection of 2-hydroxyglutarate, currently assessed postoperatively on biopsied tissue using immunohistochemistry. Intraoperative DESI-MS measurements made at surgeon-defined positions enable assessment of relevant disease state of tissue within the tumor mass and examination of the resection cavity walls for residual tumor. Results for 73 biopsies from 10 surgical resection cases show that DESI-MS allows detection of glioma and estimation of high tumor cell percentage (TCP) at surgical margins with 93% sensitivity and 83% specificity. TCP measurements from NAA are corroborated by indirect measurements based on lipid profiles. Notably, high percentages (>50%) of unresected tumor were found in one-half of the margin biopsy smears, even in cases where postoperative MRI suggested gross total tumor resection. Unresected tumor causes recurrence and malignant progression, as observed within a year in one case examined in this study. These results corroborate the utility of DESI-MS in assessing surgical margins for maximal safe tumor resection. Intraoperative DESI-MS analysis of tissue smears, ex vivo, can be inserted into the current surgical workflow with no alterations. The data underscore the complexity of glioma infiltration.
Sociotechnical Human Factors Involved in Remote Online Usability Testing of Two eHealth Interventions

Background Research in the fields of human performance technology and human computer interaction are challenging the traditional macro focus of usability testing arguing for methods that help test moderators assess “use in context” (ie, cognitive skills, usability understood over time) and in authentic “real world” settings. Human factors in these complex test scenarios may impact on the quality of usability results being derived yet there is a lack of research detailing moderator experiences in these test environments. Most comparative research has focused on the impact of the physical environment on results, and rarely on how the sociotechnical elements of the test environment affect moderator and test user performance. Improving our understanding of moderator roles and experiences with conducting “real world” usability testing can lead to improved techniques and strategies Objective To understand moderator experiences of using Web-conferencing software to conduct remote usability testing of 2 eHealth interventions. Methods An exploratory case study approach was used to study 4 moderators’ experiences using Blackboard Collaborate for remote testing sessions of 2 different eHealth interventions. Data collection involved audio-recording iterative cycles of test sessions, collecting summary notes taken by moderators, and conducting 2 90-minute focus groups via teleconference. A direct content analysis with an inductive coding approach was used to explore personal accounts, assess the credibility of data interpretation, and generate consensus on the thematic structure of the results. Results Following the convergence of data from the various sources, 3 major themes were identified: (1) moderators experienced and adapted to unpredictable changes in cognitive load during testing; (2) moderators experienced challenges in creating and sustaining social presence and untangling dialogue; and (3) moderators experienced diverse technical demands, but were able to collaboratively troubleshoot with test users. Conclusions Results highlight important human-computer interactions and human factor qualities that impact usability testing processes. Moderators need an advanced skill and knowledge set to address the social interaction aspects of Web-based usability testing and technical aspects of conferencing software during test sessions. Findings from moderator-focused studies can inform the design of remote testing platforms and real-time usability evaluation processes that place less cognitive burden on moderators and test users.
Alpha-Synuclein Oligomers Interact with Metal Ions to Induce Oxidative Stress and Neuronal Death in Parkinson's Disease

Abstract Aims: Protein aggregation and oxidative stress are both key pathogenic processes in Parkinson's disease, although the mechanism by which misfolded proteins induce oxidative stress and neuronal death remains unknown. In this study, we describe how aggregation of alpha-synuclein (α-S) from its monomeric form to its soluble oligomeric state results in aberrant free radical production and neuronal toxicity. Results: We first demonstrate excessive free radical production in a human induced pluripotent stem-derived α-S triplication model at basal levels and on application of picomolar doses of β-sheet-rich α-S oligomers. We probed the effects of different structural species of α-S in wild-type rat neuronal cultures and show that both oligomeric and fibrillar forms of α-S are capable of generating free radical production, but that only the oligomeric form results in reduction of endogenous glutathione and subsequent neuronal toxicity. We dissected the mechanism of oligomer-induced free radical production and found that it was interestingly independent of several known cellular enzymatic sources. Innovation: The oligomer-induced reactive oxygen species (ROS) production was entirely dependent on the presence of free metal ions as addition of metal chelators was able to block oligomer-induced ROS production and prevent oligomer-induced neuronal death. Conclusion: Our findings further support the causative role of soluble amyloid oligomers in triggering neurodegeneration and shed light into the mechanisms by which these species cause neuronal damage, which, we show here, can be amenable to modulation through the use of metal chelation. Antioxid. Redox Signal. 24, 376–391.
Wireless real-time communication protocol for cooperating mobile units 1

Advances in wireless communication technology allow the development of distributed robotic applications consisting of teams of autonomous mobile units that can cooperate to achieve a common goal. Real-time communication among mobile wireless units, however, requires a careful management of the shared channel in order to achieve a certain level of predictability in message exchanging. This paper proposes a new MAC level protocol that allows nodes to enter and leave the communication area, avoiding collisions due to simultaneous transmissions. The algorithm used for accessing the communication channel is based on EDF, which allows an optimal exploitation of the communication channel while guaranteeing timing constraints on real-time messages.
Mechanism of Action of A-769662, a Valuable Tool for Activation of AMP-activated Protein Kinase*

We have studied the mechanism of A-769662, a new activator of AMP-activated protein kinase (AMPK). Unlike other pharmacological activators, it directly activates native rat AMPK by mimicking both effects of AMP, i.e. allosteric activation and inhibition of dephosphorylation. We found that it has no effect on the isolated α subunit kinase domain, with or without the associated autoinhibitory domain, or on interaction of glycogen with the β subunit glycogen-binding domain. Although it mimics actions of AMP, it has no effect on binding of AMP to the isolated Bateman domains of the γ subunit. The addition of A-769662 to mouse embryonic fibroblasts or primary mouse hepatocytes stimulates phosphorylation of acetyl-CoA carboxylase (ACC), effects that are completely abolished in AMPK-α1–/–α2–/– cells but not in TAK1–/– mouse embryonic fibroblasts. Phosphorylation of AMPK and ACC in response to A-769662 is also abolished in isolated mouse skeletal muscle lacking LKB1, a major upstream kinase for AMPK in this tissue. However, in HeLa cells, which lack LKB1 but express the alternate upstream kinase calmodulin-dependent protein kinase kinase-β, phosphorylation of AMPK and ACC in response to A-769662 still occurs. These results show that in intact cells, the effects of A-769662 are independent of the upstream kinase utilized. We propose that this direct and specific AMPK activator will be a valuable experimental tool to understand the physiological roles of AMPK.
Detecting Turn Shorts in Rotor Windings: A New Test Using Magnetic Flux Monitoring

Gradual aging of the turn insulation in salient pole rotor windings can eventually lead to shorted turns. These shorted turns may create the high vibration of the rotor, and, in severe cases where there are few poles and a number of shorted turns, the probability of a motor shutdown due to high vibration increases. The most widely applied method for detecting shorted turns in the rotor is the pole-drop test. Since this test is done with the rotor not spinning, the test may not find all the shorts that can occur in service. Also, the pole-drop test is usually performed infrequently, and thus the need for rotor refurbishment is often a surprise, which can extend the turnaround duration. Research has led to a new online test that can detect the shorted turns during normal service, by measuring the magnetic flux from each rotor pole using a stator-based magnetic flux sensor. Initial results with the new technology were presented.
Towards a Secure Logging Mechanism for Dynamic Systems

Logging is a central service in computing systems. It collects information about the events happening in (remote) devices and thereby provides a basis for other services within the system. However, in order to be useful, log data must be correct, for information derived from flawed sources is unreliable. In this paper, we report on ongoing work in developing a logging mechanism to securely store log data in marginally trusted remote collectors. To this end, we first characterize the security properties of log data and that of mechanisms to guarantee these properties. Further, we employ standard logging techniques to design protocols to achieve the security properties, and discuss the protocols’ adequacy and limitations in guaranteeing these properties.
Roles of Climate, Vegetation and Soil in Regulating the Spatial Variations in Ecosystem Carbon Dioxide Fluxes in the Northern Hemisphere

Climate, vegetation, and soil characteristics play important roles in regulating the spatial variation in carbon dioxide fluxes, but their relative influence is still uncertain. In this study, we compiled data from 241 eddy covariance flux sites in the Northern Hemisphere and used Classification and Regression Trees and Redundancy Analysis to assess how climate, vegetation, and soil affect the spatial variations in three carbon dioxide fluxes (annual gross primary production (AGPP), annual ecosystem respiration (ARE), and annual net ecosystem production (ANEP)). Our results showed that the spatial variations in AGPP, ARE, and ANEP were significantly related to the climate and vegetation factors (correlation coefficients, R = 0.22 to 0.69, P < 0.01) while they were not related to the soil factors (R = -0.11 to 0.14, P > 0.05) in the Northern Hemisphere. The climate and vegetation together explained 60 % and 58 % of the spatial variations in AGPP and ARE, respectively. Climate factors (mean annual temperature and precipitation) could account for 45 - 47 % of the spatial variations in AGPP and ARE, but the climate constraint on the vegetation index explained approximately 75 %. Our findings suggest that climate factors affect the spatial variations in AGPP and ARE mainly by regulating vegetation properties, while soil factors exert a minor effect. To more accurately assess global carbon balance and predict ecosystem responses to climate change, these discrepant roles of climate, vegetation, and soil are required to be fully considered in the future land surface models. Moreover, our results showed that climate and vegetation factors failed to capture the spatial variation in ANEP and suggest that to reveal the underlying mechanism for variation in ANEP, taking into account the effects of other factors (such as climate change and disturbances) is necessary.
Fast Calculation of the Knowledge Gradient for Optimization of Deterministic Engineering Simulations

A novel efficient method for computing the Knowledge-Gradient policy for Continuous Parameters (KGCP) for deterministic optimization is derived. The differences with Expected Improvement (EI), a popular choice for Bayesian optimization of deterministic engineering simulations, are explored. Both policies and the Upper Confidence Bound (UCB) policy are compared on a number of benchmark functions including a problem from structural dynamics. It is empirically shown that KGCP has similar performance as the EI policy for many problems, but has better convergence properties for complex (multi-modal) optimization problems as it emphasizes more on exploration when the model is confident about the shape of optimal regions. In addition, the relationship between Maximum Likelihood Estimation (MLE) and slice sampling for estimation of the hyperparameters of the underlying models, and the complexity of the problem at hand, is studied.
Brain-computer interface design based on slow cortical potentials using matlab/simulink

A brain-computer interface (BCI) is a communication system that translates brain-activity into commands for a computer or other electronic devices. In other words, a BCI allows users to act on their environment by using only brain-activity, without using peripheral nerves and muscles. The feature selection and classification methods of EEG signals are two key points of the BCI system. In this paper, we designed a simulation model based on slow cortical potentials (SCPs) using Matlab/Simulink. The simulation model mainly had three parts: preprocessing, feature selection, and classification. We obtained significant improvement on classification accuracy for data set Ia of BCI competition 2003, which was a typical BCI data. The classification accuracy rates were 70.90% (190/268) and 89.76% (263/293) for train and test data sets respectively. The result also showed that training could improve the classification accuracy since the test data were taken later in the recording sessions than the training data.