Percentage Of False Positives Research Articles

Research on Methods for Development of Software System for Emotions Recognition and State of Human Health Determination

To determine the general state of human health using emotion recognition, a method based on machine learningwas chosen, the classifier is trained on the dataset “fer2013” and “PAB-F”. negative emotions and the use of a dataset totrain the neural network, with images of people divided into classes “painful” - “not painful”. For the “fer2013” dataset,it is necessary to determine the presence of pain after data processing. As a rule, pain is expressed in the intense andprolonged presence of emotions of anger and sadness. I suggest measuring the intensity of emotion by the probabilityfactor of determining the emotion, which returns the neural network. It has been experimentally determined that aspecialized data set better copes with the task, despite the fact that it has a sufficiently small number of images. “Fer2013”has a high percentage of false positives. This can be explained by the fact that the photos also showed obvious negativeemotions, which were regarded as pain. The number of learning epochs has a positive effect on the accuracy of theneural network, and the increase in learning speed has a negative effect. To check the accuracy of the neural network atthe entrance to the module to check the emotional state at the entrance were images found by the search query “Paingrimace” on the resource depositphotos.

Sensing Device for Colorectal Cancer Preventive Screening through Fecal Odor Analysis: Clinical Validation Outcomes

Sensing Device for Colorectal Cancer Preventive Screening through Fecal Odor Analysis: Clinical Validation Outcomes

REMOTE: Robust External Malware Detection Framework by Using Electromagnetic Signals

Cyber-physical systems (CPS) are controlling many critical and sensitive aspects of our physical world while being continuously exposed to potential cyber-attacks. These systems typically have limited performance, memory, and energy reserves, which limits their ability to run existing advanced malware protection, and that, in turn, makes securing them very challenging. To tackle these problems, this paper proposes, REMOTE, a new robust framework to detect malware by externally observing Electromagnetic (EM) signals emitted by an electronic computing device (e.g., a microprocessor) while running a known application, in real-time and with a low detection latency, and without any a priori knowledge of the malware. REMOTE does not require any resources or infrastructure on, or any modifications to, the monitored system itself, which makes REMOTE especially suitable for malware detection on resource-constrained devices such as embedded devices, CPSs, and Internet of Things (IoT) devices where hardware and energy resources may be limited. To demonstrate the usability of REMOTE in real-world scenarios, we port two real-world programs (an embedded medical device and an industrial PID controller), each with a meaningful attack (a code-reuse and a code-injection attack), to four different hardware platforms. We also port shellcode-based DDoS and Ransomware attacks to five different standard applications on an embedded system. To further demonstrate the applicability of REMOTE to commercial CPS, we use REMOTE to monitor a Robotic Arm. Our results on all these different hardware platforms show that, for all attacks on each of the platforms, REMOTE successfully detects each instance of an attack and has <; 0.1 percent false positives. We also systematically evaluate the robustness of REMOTE to interrupts and other system activity, to signal variation among different physical instances of the same device design, to changes overtime, and to plastic enclosures and nearby electronic devices. This evaluation includes hundreds of measurements and shows that REMOTE achieves excellent accuracy (<; 0.1 percent false positive and > 99.9 percent true positive rates) under all these conditions. We also compare REMOTE to prior work EDDIE [1] and SYNDROME [2], and demonstrate that these prior work are unable to achieve high accuracy under these variations.

Sustainable, Alginate-Based Sensor for Detection of Escherichia coli in Human Breast Milk.

There are no existing affordable diagnostics for sensitive, rapid, and on-site detection of pathogens in milk. To this end, an on-site colorimetric-based sustainable assay has been developed and optimized using an L16 (54) Taguchi design to obtain results in hours without PCR amplification. To determine the level of Escherichia coli (E. coli) contamination, after induction with 150 µL of breast milk, the B-Per bacterial protein extraction kit was added to a solution containing an alginate-based microcapsule assay. Within this 3 mm spherical novel sensor design, X-Gal (5-Bromo-4-Chloro-3-Indolyl β-d-Galactopyranoside) was entrapped at a concentration of 2 mg/mL. The outward diffusing X-Gal was cleaved by β-galactosidase from E. coli and dimerized in the solution to yield a blue color after incubation at 40 °C. Color intensity was correlated with the level of E. coli contamination using a categorical scale. After an 8 h incubation period, a continuous imaging scale based on intensity normalization was used to determine a binary lower limit of detection (LOD), which corresponded to 102 colony forming unit per mL (CFU/mL) and above. The cost of the overall assay was estimated to be $0.81 per sample, well under the $3 benchmark for state-of-the-art immune-based test kits for pathogen detection in biofluids. Considering the reported binary LOD cutoff of 102 CFU/mL and above, this proposed hydrogel-based assay is suited to meet global requirements for screening breast milk or milk for pathogenic organisms of 104 CFU/mL, with a percentage of false positives to be determined in future efforts.

Tailored Algorithms for Anomaly Detection in Photovoltaic Systems

The fastest-growing renewable source of energy is solar photovoltaic (PV) energy, which is likely to become the largest electricity source in the world by 2050. In order to be a viable alternative energy source, PV systems should maximise their efficiency and operate flawlessly. However, in practice, many PV systems do not operate at their full capacity due to several types of anomalies. We propose tailored algorithms for the detection of different PV system anomalies, including suboptimal orientation, daytime and sunrise/sunset shading, brief and sustained daytime zero-production, and low maximum production. Furthermore, we establish simple metrics to assess the severity of suboptimal orientation and daytime shading. The proposed detection algorithms were applied to a set of time-series of electricity production in Portugal, which are based on two periods with distinct weather conditions. Under favourable weather conditions, the algorithms successfully detected most of the time-series labelled with either daytime or sunrise/sunset shading, and with either sustained or brief daytime zero-production. There was a relatively low percentage of false positives, such that most of the anomaly detections were correct. As expected, the algorithms tend to be more robust under favourable rather than under adverse weather conditions. The proposed algorithms may prove to be useful not only to research specialists, but also to energy utilities and owners of small- and medium-sized PV systems, who may thereby effortlessly monitor their operation and performance.

Malicious Node Identification Strategy With Environmental Parameters

Wireless sensor network (WSN) works in a complex environment where it is difficult for people to reach or work. The openness of nodes leads to security threats vulnerable to various attacks. The trust and reputation model can be applied in WSN to reduce damage caused by malicious nodes. However, there is a high false-positive rate in trust and reputation models because a node with less reputation due to the communication environment is judged as a malicious one directly. This paper presents a trust & reputation-based malicious node identification strategy with environmental parameters (TRS&EP) to interdict the malicious nodes, such as interrupt attack nodes and selective forwarding attack nodes. Using the linear regression of machine learning and combining the energy of nodes, data volume, number of adjacent nodes, the node sparsity and other deterministic parameters can solve environmental parameters. Then TRS&EP estimates benchmark trust according to the environmental parameters. The Gaussian radial basis function is simplified to calculate the similarity between the benchmark trust sequence and cycle reputation sequence. Furthermore, TRS&EP sets three reputation intervals and an adoptive threshold span to identify the malicious nodes by dynamically considering the work environment and states of nodes. The simulated results show that TRS&EP improves the recognition of malicious nodes above 1% compared to comparison algorithms and reduces the false-positive percentage by more than 1%.

Development and evaluation of swine vesicular disease isotype-specific antibody ELISAs based on recombinant virus-like particles.

Swine vesicular disease (SVD) is a contagious viral disease of pigs. The clinical signs of SVD are indistinguishable from other vesicular diseases, such as senecavirus A infection (SVA) and foot-and-mouth disease (FMD). Rapid and accurate diagnostic tests of SVD are considered essential in countries free of vesicular diseases. Competitive ELISA (cELISA) is the serological test used routinely. However, although cELISA is the standard test for SVD antibody testing, this test produces a small number of false-positive results, which caused problems in international trade. The current project developed a SVD isotype antibody ELISA using recombinant SVD virus-like particles (VLP) and an SVD-specific monoclonal antibody (mAb) to reduce the percentage of false positives. The diagnostic specificities of SVD-VLP isotype ELISAs were 98.7% and 99.6% for IgM and IgG. The SVD isotype ELISAs were SVD-specific, without cross-reactivity to other vesicular diseases. A panel of 16 SVD-positive reference sera was evaluated using the SVD-VLP isotype ELISAs. All sera were correctly identified as positive by the two combined SVD-VLP isotype ELISAs. Comparison of the test results showed a high level of correlation between the SVDV antigen isotype ELISAs and SVD-VLP isotype ELISAs. 303 sera from animals lacking clinical signs and history of SVDV exposure were identified positive using SVD cELISA. These samples were examined using SVD-VLP isotype ELISAs. Of the 303 serum samples, five were positive for IgM, and five of 303 were positive for IgG. Comparable to virus neutralization test results, SVD isotype ELISAs significantly reduced the false-positive samples. Based on above test results, the combined use of cELISA and isotype ELISAs can reduce the number of false-positive samples and the use of time-consuming virus neutralization tests, with benefit for international trade in swine and related products.

Chemoresistive sensors for colorectal cancer preventive screening through fecal odor: Double-blind approach

Colorectal cancer screening is fundamental to decreased mortality related to the target disease. This pathology is diffused worldwide for both men and women and its curability rate, when identified at stage I, is up to 90%. Fecal occult blood test is currently the methodology adopted in many countries as a screening on population, but it shows a very high percentage of false positives, leading to non-operative colonoscopies performed on people who are often elderly and already debilitated. The use of an economic and easy-to-use method alongside fecal occult blood test would improve effectiveness of the screening. Here, the results so far obtained in the clinical validation protocol, started in May 2016, using a patented device with a core of chemoresistive gas sensors, are shown. The device can identify the difference between the fecal exhalation odor of two categories of subjects: healthy and those affected by high-risk adenomas or tumors. Moreover, further calibration of the instrument for the recognition of low-risk adenomas is on-going. The tests are compared to fecal occult blood test positives and colonoscopy results. In this work the most recent results as well as improvements in screening method are presented, useful for future large-scale production of the device.

Multiplex real-time PCR for detection and quantification of Colletotrichum abscissum and C. gloeosporioides on Citrus leaves

Colletotrichum abscissum and C. gloeosporioides are the causal agents of citrus Postbloom Fruit Drop (PFD), a major disease in several countries of the American continent. These pathogens infect only floral structures; however, in the absence of floral tissue, they survive asymptomatically on the leaf tissue. Thus, detecting and quantifying pathogens on leaves are critical for epidemiological studies. Colletotrichum species are detected through isolation, serological methods and conventional PCR, but these techniques are not effective for accurate detection of PFD agents. This study aimed to develop specific, more sensitive, faster and less laborious techniques than the traditional ones routinely used. We developed, standardized and validated a multiplex real-time PCR with high sensitivity (98–99.3%) and specificity (94.3–97.1%) for the detection and quantification of both pathogens. This technique was 1000 and 10,000 times more sensitive than Nested-PCR and PCR, respectively for the detection of C. abscissum. Similarly, the multiplex qPCR was 100 and 1000 times more sensitive than Nested-PCR and PCR, respectively for the detection of C. gloeosporioides. The diagnostic parameters used to validate the multiplex qPCR showed a high positive (17–35) and low negative (0.006–0.95) likelihood ratios, low percentage of false positives (2.8–5.6%) and false negatives (0.6–1.9%), good agreement between the results generated between qPCR and multiplex qPCR, revealed by the Kappa index (0.91–0.98) and coincidental results (95–99%). All parameters indicated that the multiplex qPCR was effectively validated to detect and quantify the causal agents of PFD.

Relevance of lung ultrasound in the diagnostic algorithm of respiratory diseases in a real-life setting: A multicentre prospective study.

The aim of this study was to assess the role of lung ultrasound (LUS) in a diagnostic algorithm of respiratory diseases, and to establish the accuracy of LUS compared with chest radiography (CXR). Over a period of 2 years, 509 consecutive patients admitted for respiratory-related symptoms to both emergency and general medicine wards were enrolled and evaluated using LUS and CXR. LUS was conducted by expert operators who were blinded to the medical history and laboratory data. Computed tomography (CT) of the chest was performed in case of discordance between the CXR and LUS, suspected lung cancer and an inconclusive diagnosis. Diagnosis made by CT was considered the gold standard. The difference in sensitivity and specificity between LUS and CXR as demonstrated by ROC curve analyses (LUS-AUROC: 0.853; specificity: 81.6%; sensitivity: 93.9% vs CXR-AUROC: 0.763; specificity: 57.4%; sensitivity: 96.3%) was significant (P = 0.001). Final diagnosis included 240 cases (47.2%) of pneumonia, 44 patients with cancer (8.6%), 20 patients with chronic obstructive pulmonary disease (COPD, 3.9%), 24 patients with heart failure (4.7%) and others (6.1%). In 108 patients (21.2%) with any lung pathology, a CT scan was performed with a positive diagnosis in 96 cases (88.9%); we found that CXR and LUS detected no abnormality in 24 (25%) and 5 (5.2%) cases, respectively. LUS was concordant with the final diagnosis (P < 0.0001), and in healthy patients, there was a low percentage of false positives (5.9%). The results support the routine use of LUS in the clinical context.

Can we use short recordings for assessment of dynamic cerebral autoregulation? A sensitivity analysis study in acute ischaemic stroke and healthy subjects

Objective: It is unclear whether the duration of recordings influences estimates of dynamic cerebral autoregulation (dCA). Therefore, we performed a retrospective study of the effects of reducing recording durations on dCA estimates; with the potential to inform recording duration for reliable estimates in challenging clinical populations. Approach: Seventy-eight healthy control subjects and 79 acute ischaemic stroke (AIS) patients were included. Cerebral blood flow (CBF) velocity was recorded with transcranial Doppler (TCD) and continuous blood pressure (BP) with a Finapres device. The autoregulation index (ARI), derived with transfer function analysis (TFA), was calculated for recording durations at one-minute intervals between 1 and 5 min using the same starting point of each recording. Main results: Though recording duration did not affect the overall ARI value, when compared to control subjects, AIS patients had significantly lower ARI values for durations between 3 and 5 min (p < 0.0001), but not 1 and 2 min. The intraclass correlation coefficient of all participants, for reproducibility of the five recording durations, was 0.69. AIS patients classified as having impaired cerebral autoregulation (CA; ARI ⩽ 4) at 5 min, had a 7.1% rate of false negatives for both 4 and 3 min recordings, reaching 42.9% for 1 min recording. The percentage of false-positives also increased with reduced recording durations (from 0% at 5 to 16.2% at 1 min). Significance: Reducing recording durations from 5 to 3 min can still provide reliable estimates of ARI, and may facilitate CA studies in potentially medically unstable AIS patients, as well as in other patient groups.

Warning criteria for MEP monitoring during carotid endarterectomy: a retrospective study of 571 patients.

Monitoring of transcranial electrical motor evoked potentials (tcMEP) during carotid endarterectomy (CEA) has been shown to effectively detect intraoperative cerebral ischemia. The unique purpose of this study was to evaluate changes of MEP amplitude (AMP), area under the curve (AUC) and signal morphology (MOR) as additional MEP warning criteria for clamping-associated ischemia during CEA. Therefore, the primary outcome was the number of MEP alerts (AMP, AUC and MOR) in the patients without postoperative motor deficit (false positives). We retrospectively reviewed data from 571 patients who received CEA under general anesthesia. Monitoring of somatosensory evoked potentials (SSEP) and tcMEP was performed in all cases (all-or-none MEP warning criteria). The percentages of false positives (primary parameter) of AMP, AUC and MOR were evaluated according to the postoperative motor outcome. In the cohort of 562 patients, we found significant SSEP/MEP changes in 56 patients (9.96%). In 44 cases (7.83%) a shunt was inserted. Nine patients (1.57%) were excluded due to MEP recording failure. False positives were registered for AMP, AUC and MOR changes in 121 (24.01%), 148 (29.36%) and 165 (32.74%) patients, respectively. In combination of AMP/AUC and AMP/AUC/MOR false positives were found in 9.52% and 9.33% of the patients. This study is the first to evaluate the correctness of the MEP warning criteria AMP, AUC and MOR with regard to false positive monitoring results in the context of CEA. All additional MEP warning criteria investigated produced an unacceptably high number of false positives and therefore may not be useful in carotid surgery for adequate detection of clamping-associated ischemia.

User-centered fall detection using supervised, on-line learning and transfer learning

There is no need to explain the relevance of fall detection (FD) in the elderly population: the faster they receive help, the higher the probabilities of recovery. FD has been widely studied, and a number of solutions have been proposed in the literature, including commercial products with well-known trademarks. Basically, the majority of the solutions include an Event Detection method followed by a feature extraction block as the inputs to a classifier that labels the sample as Fall or as Not Fall. Nevertheless, the proposals always rely on user feedback because of the relatively high percentage of false positives. This study proposes two supervised user-centered solutions for FD which are evaluated using two publicly available data sets that include simulated falls with the sensory system placed on a wrist. Instead of focusing on learning a generalized model, in this study we focus on learning specific models for each user. To overcome the lack of data from the Fall class, an on-line learning method to group the events arising from the activities of daily life and the consequent analysis of how fall events can then be detected is included. In addition, a transfer learning stage produces a priori knowledge from previous experiences that can be introduced into the on-line learning classifier in order to enhance its performance. Complete experimentation is carried out on two publicly available data sets, analyzing the performance of the supervised classifiers, and the performance of the whole detection system composed of the fall detection plus the classifier. In the case of on-line learning, only the results for the classifier, either with or without transfer learning, are included. The results suggest that FD user-centered solutions have the capacity to adapt to the information of a specific user, mostly when using both on-line learning and transfer learning. Nevertheless, the transfer learning stage needs refinements in order to avoid the complexity that this might introduce.

Management of ASC-US/HPV positive post-menopausal woman

BackgroundThe aim of our study was to determine which diagnostic course is best to identify women at risk of CIN2+ among post-menopausal women with cytological diagnosis of ASCUSMethodsWe selected women who had been post-menopausal for at least one year , and who had completed the entire diagnostic-therapeutic course that they had undertaken. The sample was divided into two arms: in the first arm, we considered 146 ASCUS positive women who had undergone the HPV test, colposcopy and then underwent more detailed diagnostics by means of LEEP or a scraping of the cervical canal. The second arm was made up of 124 ASCUS positive women who had undergone a vaginal administration of estriolo, the HPV test and colposcopy. Estriol was administered for 5 weeks: the first week one vaginal suppository every evening, the other four weeks the administration was twice a week. Then, the patients underwent colposcopy. In cases of positivity a biopsy was carried out, the patients positive for CIN2+ at the biopsy underwent excisional therapy using LEEP and were followed up. The patients, who were negative at colposcopy or with histological diagnosis of CIN1, were examined again at 1 year.ResultsIn the first arm the HPV test had an SE of 94%, an SP of 68%, NPV of 99%, and PPV of 28%. The PPV is very low because of the elevated percentage of false positives that the HPV test gave (71%). In the second arm the HPV test maintained its high SE (100%), an SP of 74%, a NPV of 100%, and a PPV of 43%. The use of estrogen increased the specificity of the test.ConclusionIt is important to say that the second arm indicates the use of local estrogen therapy only for ASCUS/HPV positive postmenopausal women. Therefore, the HPV test should be used as the first diagnostic possibility in cases of ASCUS in post-menopausal women, associating local estrogen therapy only with HPV positive women.

An unambiguous definition of pediatric hypotension is still lacking: Gaps between two percentile-based definitions and Pediatric Advanced Life Support/Advanced Trauma Life Support guidelines.

Data are lacking to provide cutoffs for hypotension in children based on outcome studies and Pediatric Advanced Life Support (PALS), and Advanced Trauma Life Support (ATLS) definitions are based on normal populations. The goal of this study was to compare different normal population based cutoffs including fifth percentile of systolic blood pressure (P5-SBP) in children and adolescents from the German Health Examination Survey for Children and Adolescents (KiGGS), US population data (Fourth Report), and cutoffs from PALS and ATLS guidelines. Fifth percentile of systolic blood pressure according to age, sex, and height was modeled based on standardized resting oscillometric BP measurements (12,199 children aged 3-17 years) from KiGGS 2003-2006. In addition, we applied the age-adjusted pediatric shock index in the KiGGS study. The KiGGS P5-SBP was on average 7 mm Hg higher than Fourth Report P5-SBP (5-10 mm Hg depending on age-sex group). For children aged 3 to 9 years, KIGGS P5-SBP at median height follows the formula 82 mm Hg + age; for age 10 to 17 years, the increase was not linear and is presented in a simplified table. Pediatric Advanced Life Support/ATLS thresholds were between KiGGS and Fourth Report until age of 11 years. The adult threshold of 90 mm Hg was reached by KiGGS P5-SBP median height at 8 years, PALS/ATLS at age of 10 years, and Fourth Report P5-SBP at 12 years. The pediatric shock index, which is supposed to identify severely injured children, was exceeded by 2.3% nonacutely ill KiGGS participants. Our study shows that percentile cutoffs vary by reference population. The 90 mm Hg cutoff for adolescents targets only those in the less than 1% of the low SBP range and represents an undertriage compared with P5 at younger ages according to both KiGGS and Fourth Report. Finally, current pediatric shock index cutoffs when applied to a healthy cohort lead to a relevant percentage of false positives. Epidemiologic/prognostic, level III.

IDEA: Intrusion Detection through Electromagnetic-Signal Analysis for Critical Embedded and Cyber-Physical Systems

We propose a novel framework called IDEA that exploits electromagnetic (EM) side-channel signals to detect malicious activity on embedded and cyber-physical systems (CPS). IDEA first records EM emanations from an uncompromised reference device to establish a baseline of reference EM patterns. IDEA then monitors the target device's EM emanations. When the observed EM emanations deviate from the reference patterns, IDEA reports this as an anomalous or malicious activity. IDEA does not require any resource or infrastructure on, or any modification to, the monitored system itself. In fact, IDEA is isolated from the target device, and monitors the device without any physical contact. We evaluate IDEA by monitoring the target device while it is executing embedded applications with malicious code injections such as Distributed Denial of Service (DDoS), Ransomware and code modification. We further implement a control-flow hijack attack, an advanced persistent threat, and a firmware modification on three CPSs: an embedded medical device called SyringePump, an industrial Proportional-Integral-Derivative (PID) Controller, and a Robotic Arm, using a popular embedded system, Arduino UNO. The results demonstrate that IDEA can detect different attacks with excellent accuracy (AUC > 99.5%, and 100 percent detection with less than 1 percent false positives) from distances up to 3 m.

Feeding ecological knowledge: the underutilised power of faecal DNA approaches for carnivore diet analysis

Abstract Accurate analyses of the diets of predators are key to understand trophic interactions and defining conservation strategies. Diets are commonly assessed through analysis of non‐invasively collected scats, and the use of faecal DNA (fDNA) analysis can reduce the species misidentifications that could lead to biased ecological inference. We review the scientific literature since publication of the first paper on amplifying fDNA, in order to assess trends in the use of genetic non‐invasive sampling (gNIS) for predator species identification in scat‐based diet studies of North American and European terrestrial mammalian carnivores (Carnivora). We quantify error rates in morphology‐based predator species identification. We then provide an overview of how applying gNIS would improve research on trophic interactions and other areas of carnivore ecology. We found that carnivore species identity was verified by using gNIS in only 8% of 400 studies of carnivore diets based on scats. The median percentage of false positives (i.e. samples wrongly identified as belonging to the target species) in morphology‐based studies was 18%, and was consistent regardless of species’ body size. We did not find an increasing trend in the use of gNIS over time, despite the existing technical capability to identify almost all carnivore species. New directions for fDNA studies include employing high‐throughput sequencing (HTS) and DNA metabarcoding to identify the predator species, the individual predator, the entire assemblage of consumed items, and the microbiome of the predator and pathogens. We conclude that HTS protocols and metagenomic approaches hold great promise for elevating gNIS as a fundamental cornerstone for future research in ecology and conservation biology of mammals.

You click, I steal: analyzing and detecting click hijacking attacks in web pages

Click Hijacking (clickjacking) is emerging as a web-based threat on the Internet. The prime objective of clickjacking is stealing user clicks. An attacker can carry out a clickjacking attack by tricking the victim into clicking an element that is barely visible or completely hidden. By stealing the victim’s clicks, an attacker could entice the victim to perform an unintended action from which the attacker can benefit. These actions include online money transactions, sharing malicious website links, initiate social networking links, etc. This paper presents an anatomy of advanced clickjacking attacks not yet reported in the literature. In particular, we propose new class of clickjacking attacks that employ SVG filters and create various effects with SVG filters. We demonstrate that current defense techniques are ineffective to deal with these sophisticated clickjacking attacks. Furthermore, we develop a novel detection method for such attacks based on the behavior (response) of a website active content against the user clicks (request). In our experiments, we found that our method can detect advanced Scalable Vector Graphics (SVG)-based attacks where most of the contemporary tools fail. We explore and utilize various common and distinguishing characteristics of malicious and legitimate web pages to build a behavioral model based on Finite State Automaton. We evaluate our proposal with a sample set of 78,000 web pages from various sources, and 1000 web pages known to involve clickjacking. Our results demonstrate that the proposed solution enjoys good accuracy and a negligible percentage of false positives (i.e., 0.28%), and zero false negatives in distinguishing clickjacking and legitimate websites.

16 False-positive rate after anticipation of early pregnancy diagnosis for resynchronization of ovulation in Bos indicus heifers

The objective was to determine the incidence of false-positive pregnancy diagnosis following the use of colour Doppler ultrasonography 20 and 22 days after fixed-time AI (FTAI) in Bos indicus heifers submitted to resynchronization 14 days after first FTAI. In the study, 512 Nellore heifers at 24.5±0.8 months of age and body condition score of 3.4±0.1 were used beginning 14 days after FTAI. On Day 14, heifers received 50mg of short-acting progesterone (Afisterone®, CEVA, São Paulo, Brazil) and a progesterone device (Prociclar®, CEVA). Eight days later (Day 22), the progesterone device was removed. A duplex B-mode (grayscale) and pulse-wave colour Doppler ultrasound instrument (M5, Mindray, Shenzhen, China) equipped with a multifrequency linear transducer was used for the examination of luteal blood flow on Days 20 and 22. The percentage of luteal area with colour Doppler signals of blood flow at each examination was determined as previously described (Ginther 2007) and was classified as low [corpus luteum (CL) with less than 25% vascularized area], intermediate (CL with 25-75% vascularized area), or high (CL with more than 75% vascularized area). Heifers with intermediate and high luteal blood flow were diagnosed as pregnant by Doppler ultrasonography, and heifers with low luteal blood flow were diagnosed as nonpregnant. Heifers diagnosed as pregnant by Doppler ultrasonography were examined 30 days after FTAI by B-mode ultrasound examination for pregnancy diagnosis and to determine the number of false positives (heifers pregnant by Doppler ultrasonography and nonpregnant by B-mode ultrasonography). Statistical analysis was performed by the GLIMMIX procedure of SAS (SAS Institute Inc., Cary, NC, USA). Pregnancy rate by Doppler ultrasonography [Day 20=60.5% (310/512), Day 22=55.3% (283/512); P=0.10] and false-positive percentage [Day 20=29.7% (92/310), Day 22=23.0% (65/2830); P=0.06] were similar on pregnancy diagnoses by Doppler ultrasonography performed 20 and 22 days after FTAI. Furthermore, in the period from 20 to 22 days after FTAI, luteolysis was verified in 5.8% (30/512) of heifers. In conclusion, anticipation of pregnancy diagnosis in 2 days by evaluation of luteal blood flow with colour Doppler ultrasonography (20 days after FTAI) does not interfere with pregnancy rate by Doppler ultrasound and percentage of false positives in Bos indicus heifers.

Role-based Association of Verbs, Actions, and Sentiments with Entities in Political Discourse

ABSTRACTA crucial challenge in measuring how text represents an entity is the need to associate each representative expression with a relevant entity to generate meaningful results. Common solutions to this problem are usually based on proximity methods that require a large corpus to reach reasonable levels of accuracy. We show how such methods for the association between an entity and a representation yield a high percentage of false positives at the expression level and low validity at the document level. We introduce a solution that combines syntactic parsing, semantic role labeling logic, and a machine learning approach—the role-based association method. To test our method, we compared it with prevalent methods of association on the news coverage of two entities of interest—the State of Israel and the Palestinian Authority. We found that the role-based association method is more accurate at the expression and the document levels.