nils-herrmann/clean_scirepeval huggingface dataset:
Detecting semantic social engineering attacks with the weakest link: Implementation and empirical evaluation of a human as a security sensor framework
Venue: Comput. Secur.
Year: 2018

The notion that the human user is the weakest link in information security has been strongly, and, we argue, rightly contested in recent years. Here, we take a step further showing that the human user can in fact be the strongest link for detecting attacks that involve deception, such as application masquerading, spearphishing, WiFi evil twin and other types of semantic social engineering. Towards this direction, we have developed a human as a security sensor framework and a practical implementation in the form of Cogni Sense, a Microsoft Windows prototype application, designed to allow and encourage users to actively detect and report semantic social engineering attacks against them. Experimental evaluation with 26 users of different profiles running Cogni Sense on their personal computers for a period of 45 days has shown that human sensors can consistently outperform technical security systems. Making use of a machine learning based approach, we also show that the reliability of each report, and consequently the performance of each human sensor, can be predicted in a meaningful and practical manner. In an organisation that employs a human as a security sensor implementation, such as Cogni Sense, an attack is considered to have been detected if at least one user has reported it. In our evaluation, a small organisation consisting only of the 26 participants of the experiment would have exhibited a missed detection rate below 10% down from 81% if only technical security systems had been used. The results strongly point towards the need to actively involve the user not only in prevention through cyber hygiene and user centric security design, but also in active cyber threat detection and reporting.
What can regulatory bodies do to help implement competency based medical education?
Venue: Medical teacher
Year: 2020

In response to the numerous challenges resident trainees currently face in their ability to competently acquire the requisite skills, knowledge and attitudes upon graduation, medical educators have looked to a competency based medical education (CBME) approach as a possible solution. As CBME has already been implemented in many jurisdictions around the world, certain challenges in implementation have been experienced. One important challenge identified relates to how regulatory bodies can either assist or unintentionally hinder implementation. By examining the varied experiences from Canada, the USA and the Netherlands in implementing CBME, this paper identifies how regulatory bodies can support and advance worldwide efforts of furthering its implementation. If regulatory bodies restructure accreditation and regulatory criteria to align with CBME principles, work together in a coordinated fashion to ensure alignment of vital regulatory meaures throughout the training and practice continuum of a physician, and allow for (if not incentivize) individuals and programs to be innovative in adapting CBME to meet their local environments, it is likely that the worldwide implementation of CBME will occur successfully.
Addiction molecular genetics: 639,401 SNP whole genome association identifies many "cell adhesion" genes
Venue: American journal of medical genetics. Part B, Neuropsychiatric genetics the official publication of the International Society of Psychiatric Genetics
Year: 2006

Addictions are substantially heritable complex disorders. We now report whole genome association studies that identify 89 genes likely to contain variants that contribute to addiction vulnerability, using previously and newly validated microarray based pooling assays. Each gene contains clustered single nucleotide polymorphisms (SNPs) that display significant allele frequency differences between abusers and controls in each of the two samples studied with 639,401 SNP arrays and confirmatory SNPs from each of two other abuser/control samples. These genes are implicated in interesting functions, including "cell adhesion" processes that help to establish and maintain neuronal connections of special relevance to addiction's memory like features.
Analisis Kompetensi Guru dalam Mempersiapkan Media Pembelajaran Berbasis Teknologi Informasi Era Revolusi Industri 4.0
Year: 2019

Indonesia faces the biggest challenge in education. Various problems have arisen including teachers not yet utilizing technology optimally in learning. Teachers in schools still maintain conventional learning. Actions to motivate teachers are needed so that integrated learning of information technology can be implemented well. This is in accordance with the demands of the industrial revolution 4.0 in the learning process where teachers and students together make innovations including the use of information technology based learning media. To find out how deep the teacher's understanding of the preparation of information technology based learning media in the revolution era 4.0, analysis in the form of quantitative data presented descriptively is needed.
RetinaFace: Single Shot Multi Level Face Localisation in the Wild
Venue: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020

Though tremendous strides have been made in uncontrolled face detection, accurate and efficient 2D face alignment and 3D face reconstruction in the wild remain an open challenge. In this paper, we present a novel single shot, multi level face localisation method, named RetinaFace, which unifies face box prediction, 2D facial landmark localisation and 3D vertices regression under one common target: point regression on the image plane. To fill the data gap, we manually annotated five facial landmarks on the WIDER FACE dataset and employed a semi automatic annotation pipeline to generate 3D vertices for face images from the WIDER FACE, AFLW and FDDB datasets. Based on extra annotations, we propose a mutually beneficial regression target for 3D face reconstruction, that is predicting 3D vertices projected on the image plane constrained by a common 3D topology. The proposed 3D face reconstruction branch can be easily incorporated, without any optimisation difficulty, in parallel with the existing box and 2D landmark regression branches during joint training. Extensive experimental results show that RetinaFace can simultaneously achieve stable face detection, accurate 2D face alignment and robust 3D face reconstruction while being efficient through single shot inference.
Adaptive Steganographic Algorithm using Cryptographic Encryption RSA Algorithms
Year: 2013

Cryptography is the art of securing information by applying encryption and decryption on transmission data which ensure that the secret can be understood only by the right person.[1] Steganography is the process of sharing information in an undetectable way by making sure that nobody else can even detect the presence of a secret. If these two methods could be combined, it would provide a fool proof security to information being communicated over a network. This paper propose two different steganographic algorithms using RSA cryptographic algorithms and with some random functions. The message is transformed into a cipher audio signal using a key, concealed into another cover data using Steganography by converting it into an intermediate encrypted and message using random number generator and with the help of RSA algorithms. The proposed method thus achieves a high degree of security for information.  Metamorphic cryptography, Cryptography, Steganography, Matrix Multiplication, Angular Encryption, Dynamic Encryption, Color Key, Cipher Image, Intermediate Text, Color Key Matrix, Data Matrix,
Diagnosis of acute respiratory distress syndrome by exhaled breath analysis.
Venue: Annals of translational medicine
Year: 2018

The acute respiratory distress syndrome (ARDS) is a complication of critical illness that is characterized by acute onset, protein rich, pulmonary edema. There is no treatment for ARDS, other than the reduction of additional ventilator induced lung injury. Prediction or earlier recognition of ARDS could result in preventive measurements and might decrease mortality and morbidity. Exhaled breath contains volatile organic compounds (VOCs) a collection of hundreds of small molecules linked to several physiological and pathophysiological processes. Analysis of exhaled breath through gas chromatography and mass spectrometry (GC MS) has resulted in an accurate diagnosis of ARDS in several studies. Most identified markers are linked to lipid peroxidation. Octane is one of the few markers that was validated as a marker of ARDS and is pathophysiologically likely to be increased in ARDS. None of the currently studied breath analysis methods is directly applicable in clinical practice. Two steps have to be taken before any breath test can be allowed into the intensive care unit. External validation in a multi center study is a prerequisite for any of the candidate breath markers and the breath test should outperform clinical prediction scores. Second, the technology for breath analysis should be adapted so that it is available at a decentralized lab inside the intensive care unit and can be operated by trained nurses, in order to reduce the analysis time. In conclusion, exhaled analysis might be used for the early diagnosis and prediction of ARDS in the near future but several obstacles have to be taken in the coming years. Most of the candidate markers can be linked to lipid peroxidation. Only octane has been validated in a temporal external validation cohort and is, at this moment, the top ranking breath biomarker for ARDS.
SPEAKER INDEPENDENT PHONEME RECOGNITION IN A CONTINUOUS SPEECH CONTEXT USING TIME DELAY FEED FORWARD NEURAL NETWORKS
Year: 2010

In scaling the problem of automatic speech recognition down to a smaller sub problem that of recognizing individual utterances this exploration into a connectionist model of phoneme recognition stems from the assumption that phonemes should be identifiable as part of a surrounding context. By including contextual clues in the form of multiple contiguous spectral speech frames, a feedforward neural network trained through error back propagation will have more information from which to derive phonemic classifications. An additional assumption is that multiple speaker data is not only desirable, but even necessary for proper training of a robust recognition system.
Use of Digital Human Model for ultrasound system design: A case study to minimize the risks of musculoskeletal disorders
Year: 2017

The aim of this paper is to present and discuss the use of Santos(tm) Digital Human Model for Ultrasound System (US) Design, in particular for the transducer design. The incidence of work related musculoskeletal disorders (WRMSD) among sonographers ranges between 82% and 88% The most frequent causes of musculoskeletal disorders in sonography include the US equipment design. Diagnostic Ultrasound systems are characterized by real time examination, which forces to manage the US Probe with one hand (usually the right one in most clinical applications) and the US system User Interface (UI) with the other hand. The transducer design seems to be the best predictor of hand wrist complaints and it is significantly related to the increasing severity of symptoms in the hand, wrist and forearm area. Sub optimally designed US systems UI and Probe ergonomics led to a high presence of WRMSD among US system users. Digital Human Models (DHM) have been developed in order to increase the level of quality of the Ergonomic Design Process. In this investigation, the relation between the geometry of a conventional designed probe model and a new designed appleprobe transducer (Esaote S.p.A. Firenze, Italy) has been evaluated. Results and Analysis are based on qualitative data produced by the simulations. The new design is well promising to minimize the risk of WRMSD at the wrist and at the hand level. This work discusses how these tools can be reliable in the assessment of the ergonomics of the US transducer, comparing simulated data with qualitative data collected from professional sonographers. Relevance to industry The interest in Ultrasound Ergonomics is of main importance among the Ultrasound producers worldwide. The capability to design ergonomically valid systems and probes represents a strong market plus of a certain producer with respect to competitors. DHM allows to perform the evaluation of the system and probe design well in advance of the marketing phase, enabling the simulation phase as soon as a CAD model is available.
GODFREY KNADLER, OF PEORIA, ILLINOIS, ASSIGNOR OF SEVEN EIGHTHS TO ALTON G. SEIBERLING AND CHARLES J. BUTLER, OF SAME PLACE. METHOD OF FoRMING PNEUMATic TIREs
Year: 2017

XXX XXXX XXXXXX XXX WXIX) %XX) %XXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXX X yXXXXXXXX8SE X 0X) XXXXXXXXXXX XXXXXXXXX XXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXSXXXX XXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXX XXXX) XXXXXXXXXX) XXXXXXX XXXXXXXXXXXXXX$ XXXXXXXXXXXXXXXXXXXXX (XXXXXXXXXXXXXXXXXXXX) XXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXX} (XXXXXXXXXXXXXXXXXXX XXXXXXX%XXXXXXXXXX? XXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXX8 ?XX E R r V
Transmission of drug resistant tuberculosis in HIV endemic settings.
Venue: The Lancet. Infectious diseases
Year: 2019

The emergence and expansion of the multidrug resistant tuberculosis epidemic is a threat to the global control of tuberculosis. Multidrug resistant tuberculosis is the result of the selection of resistance conferring mutations during inadequate antituberculosis treatment. However, HIV has a profound effect on the natural history of tuberculosis, manifesting in an increased rate of disease progression, leading to increased transmission and amplification of multidrug resistant tuberculosis. Interventions specific to HIV endemic areas are urgently needed to block tuberculosis transmission. These interventions should include a combination of rapid molecular diagnostics and improved chemotherapy to shorten the duration of infectiousness, implementation of infection control measures, and active screening of multidrug resistant tuberculosis contacts, with prophylactic regimens for individuals without evidence of disease. Development and improvement of the efficacy of interventions will require a greater understanding of the factors affecting the transmission of multidrug resistant tuberculosis in HIV endemic settings, including population based molecular epidemiology studies. In this Series article, we review what we know about the transmission of multidrug resistant tuberculosis in settings with high burdens of HIV and define the research priorities required to develop more effective interventions, to diminish ongoing transmission and the amplification of drug resistance.
Municipal composts reduce the transfer of Cd from soil to vegetables.
Venue: Environmental pollution
Year: 2016

Cadmium (Cd) is a non essential trace element that accumulates in agricultural soils through the application of Cd rich phosphate fertiliser. Vegetables can accumulate Cd to concentrations that sometimes exceed food safety standards. We investigated the potential of low cost soil amendments to reduce Cd uptake by spinach (Spinacia oleracea L. lettuce (Lactuca sativa L. and onion (Allium cepa L. Batch sorption experiments revealed the relative sorption of Cd by biosolids, charcoal, lignite, sawdust, two types of compost, bentonite and zeolite. Lignite and compost had the greatest ability to sorb Cd and were subsequently selected for pot trials, which elucidated their effect on Cd uptake by onions, spinach and lettuce in two market garden soils with native Cd concentrations of 1.45 mg/kg and 0.47 mg/kg. The addition of 2.5% (dry w/w) municipal compost reduced the Cd concentration in onions, spinach and lettuce by up to 60% in both soils. The addition of lignite gave variable results, which depended on the soil type and rate of addition. This Cd immobilisation was offset by soil acidification caused by the lignite. The results indicate that municipal compost is a low cost soil conditioner that is effective in reducing plant Cd uptake.
MEAL: Multi Model Ensemble via Adversarial Learning
Venue: AAAI
Year: 2019

Often the best performing deep neural models are ensembles of multiple base level networks. Unfortunately, the space required to store these many networks, and the time required to execute them at test time, prohibits their use in applications where test sets are large (e.g. ImageNet) In this paper, we present a method for compressing large, complex trained ensembles into a single network, where knowledge from a variety of trained deep neural networks (DNNs) is distilled and transferred to a single DNN. In order to distill diverse knowledge from different trained (teacher) models, we propose to use adversarial based learning strategy where we define a block wise training loss to guide and optimize the predefined student network to recover the knowledge in teacher models, and to promote the discriminator network to distinguish teacher vs. student features simultaneously. The proposed ensemble method (MEAL) of transferring distilled knowledge with adversarial learning exhibits three important advantages: (1) the student network that learns the distilled knowledge with discriminators is optimized better than the original model; (2) fast inference is realized by a single forward pass, while the performance is even better than traditional ensembles from multi original models; (3) the student network can learn the distilled knowledge from a teacher model that has arbitrary structures. Extensive experiments on CIFAR 10/100, SVHN and ImageNet datasets demonstrate the effectiveness of our MEAL method. On ImageNet, our ResNet 50 based MEAL achieves top 1/5 21.79%/5.99% val error, which outperforms the original model by 2.06%/1.14%
SIMULATION AND EMPIRICAL INVESTIGATION OF MICRO ELECTRO MECHANICAL PIEZORESISITIVE PRESSURE SENSOR PACKAGE BASED ON THE REQUIREMENTS OF PETROCHEMICAL INDUSTRY
Year: 2015

Navid SeyedKazem Viliani1* Mehdi Hashemi2 Hadi Vadizadeh3 Hassan Pourrostami1 Seyed Mohsen Mostafavi4 Faramand Hashemizadeh4 1Department of Engineering, Islamic Azad University, Abhar Branch, Abhar, Iran. 2Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran 3 Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran 4Department of Research and Development, Fanavaran Petrochemical Co. Mahshahr, Iran P.O.B. 1998664799 Tehran, Iran.
Assessment of Vitamin C and Antioxidant Profiles in Saliva and Serum in Patients with Periodontitis and Ischemic Heart Disease
Venue: Nutrients
Year: 2019

Vitamin C and antioxidants play a crucial role in endothelial function and may be a link for the known interaction of periodontitis and ischemic heart disease (CAD) This pilot study evaluates the association of gingival health, periodontitis, CAD, or both conditions with salivary and serum vitamin C and antioxidant levels. The clinical and periodontal characteristics, serum, and saliva samples were collected from 36 patients with periodontitis, 35 patients with CAD, 36 patients with periodontitis plus CAD, and 36 healthy controls. Levels of vitamin C, antioxidants, and C reactive protein (hs CRP) were assessed with a commercially available kit. The median concentrations of salivary and serum vitamin C and antioxidants (a tocopherol, b carotene, lutein, and lycopene) were significantly lower in the CAD group (p 0.001) and in the periodontitis plus CAD group (p 0.001) compared to periodontitis patients and controls. In univariate models, periodontitis (p 0.034) CAD (p 0.001) and hs CRP (p 0.001) were significantly negatively associated with serum vitamin C; whereas, in a multivariate model, only hs CRP remained a significant predictor of serum vitamin C (p 0.001) In a multivariate model, the significant predictors of salivary vitamin C levels were triglycerides (p 0.028) and hs CRP (p 0.001) Patients with CAD and periodontitis plus CAD presented lower levels of salivary and serum vitamin C compared to healthy subjects and periodontitis patients. hs CRP was a significant predictor of decreased salivary and serum vitamin C levels.
The Use of MATLAB's Computer Vision Function in the Field of lithic Analysis
Year: 2020

In ancient times most stone tools were manufactured by flaking off the excess stone from a tool stone until it is reduced to the correct size and shape. This flaking processes leaves behind a pattern of scars where stone flakes were removed. This project is designed to use MATLAB's Computer Vision functions, and clustering functions, in order identify characteristic edge patterns of flake scars on lithic artifacts. This will be done by preforming a series of experiments in which, functions designed for uses such as, fingerprint matching, facial recognition, and human identification (  2005) will be employed to extract data from photographs of artifact edges. The data gathered with these functions will be clustered using supervised and unsupervised clustering algorithms. The resulting clusters will be compared for similarities and deviation from analyst's clusters, in order to draw conclusions about proper classification of the artifact. Clusters will also be examined to determine if they correlate with known manufacturing techniques. Finally, clusters not correlating with analysts classifications or known manufacturing techniques will be scrutinized to find the cause for that clustering. Those causes will be examined to determine if they have cultural significance.
To be or not to be different: Exploration of norms and benefits of color differentiation in the marketplace
Year: 2013

Building a strong brand identity is central for increasing brand equity. As a result, oftentimes, brands develop differentiation strategies in order to stand out from competitors. Yet, is value always gained through differentiation, or is it possible that some value could be forfeited by straying too far from established product category associations? This research examines color norms within product categories and addresses the question of whether visual differentiation is always helpful. With data for 281 top brands, the authors calculate product category color homogeneity scores for 15 product categories and 40 subcategories to empirically explore color norms. Then, these calculations are used in conjunction with brand equity scores to examine the relationship between color differentiation and brand performance. The results show that while color differentiation is helpful for some product categories, it can also be harmful for others. Specifically, the investigation reveals that adhering to color norms may be beneficial for product categories containing a dominant market leader, especially high involvement categories. The results of this research highlight the existence of visual product category norms, and emphasize both the benefits and risks of visual differentiation should be considered.