nils-herrmann/clean_scirepeval huggingface dataset:
Towards Automated Penetration Testing for Cloud Applications
Venue: 2018 IEEE 27th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)
Year: 2018

The development of cloud applications raises several security concerns due to the lack of control over involved resources. Security testing is fundamental to identify the existing security issues and is particularly powerful when carried out by means of penetration testing techniques. Unfortunately, penetration testing requires a deep knowledge of the possible attacks and of the available hacking tools and is very energy demanding. In this paper, we present a methodology that allows to easily carry out a coarse grained security evaluation of a cloud application by automating the set up and execution of penetration tests. The methodology relies on the knowledge of the application architecture and on the availability of a catalogue including security related data collected from multiple sources and properly correlated.
Health of common bottlenose dolphins Tursiops truncatus in Barataria Bay, Louisiana, following the deepwater horizon oil spill.
Venue: Environmental science technology
Year: 2014

The oil spill resulting from the explosion of the Deepwater Horizon drilling platform initiated immediate concern for marine wildlife, including common bottlenose dolphins in sensitive coastal habitats. To evaluate potential sublethal effects on dolphins, health assessments were conducted in Barataria Bay, Louisiana, an area that received heavy and prolonged oiling, and in a reference site, Sarasota Bay, Florida, where oil was not observed. Dolphins were temporarily captured, received a veterinary examination, and were then released. Dolphins sampled in Barataria Bay showed evidence of hypoadrenocorticism, consistent with adrenal toxicity as previously reported for laboratory mammals exposed to oil. Barataria Bay dolphins were 5 times more likely to have moderate severe lung disease, generally characterized by significant alveolar interstitial syndrome, lung masses, and pulmonary consolidation. Of 29 dolphins evaluated from Barataria Bay, 48% were given a guarded or worse prognosis, and 17% were considered poor or grave, indicating that they were not expected to survive. Disease conditions in Barataria Bay dolphins were significantly greater in prevalence and severity than those in Sarasota Bay dolphins, as well as those previously reported in other wild dolphin populations. Many disease conditions observed in Barataria Bay dolphins are uncommon but consistent with petroleum hydrocarbon exposure and toxicity.
Recent advancements in PV cooling and efficiency enhancement integrating phase change materials based systems A comprehensive review
Year: 2020

In recent years, researchers are fascinated to counter problem of PV efficiency decline arising from high operating temperatures, especially in hot climates. This article conducts a comprehensive review of research activities performed in last 5 years, on cooling techniques with phase change materials (PCMs) nanofluids and their combined use, leading to efficiency enhancement. By passive cooling approach with PCMs, it is found that maximum enhancement up to 20% in PV efficiency can be achieved. Effectiveness of PCM for PV is more prominent in summer than in winter. Incorporations of fins inside PCM container at PV rear, results in much improved heat conduction within PCM. Now a days, researchers have grown interest in composite PCMs for PV cooling due to their enhanced thermal conductivity. Moreover, better heat regulation as well as PV surface temperature uniformity can be achieved with two PCMs at a time having different melting points. Studies suggest that combination of passive active cooling techniques helps in further lowering of PV cell temperature, leading to enhancement in PV efficiency with additional thermal power generation. PV efficiency of water based hybrid PV/T systems can be improved by 32% by integration with PCM. Although nanofluid based PV/T systems have been proved to enhance PV efficiency by more than 60% but combined use of PCM nanofluid is more effective approach for PV cooling than individual use of PCM or nanofluid. If combination is made between nanofluid nano PCM, electrical power efficiency can further be enhanced. Nanofluids can also be considered a good spectral filter alternative as they require small thickness and are able to be tuned by varying nanoparticles conc. Finally, environmental impacts economic viability of mentioned cooling techniques, were discussed. Studies show that PV/PCM systems become expensive less feasible when operated in single junction due to long payback period up to 20 years. Economic feasibility can be increased by combining passive active cooling techniques which can increase system compactness and lower its cost.
Technology Spatial Sorting and Job Polarization
Year: 2019

Job polarization and wage inequality have increased substantially in the last four decades, being largely attributed to technological change. But there are two competing drivers: Skill Biased Technological Change (SBTC) leading to a rising college premium; and automation, leading to the replacement of routine occupations and the "hollowing out" of the income distribution. Using a novel data set on Information Technology (IT) adoption, we exploit geographical variation and the sorting patterns of differentially skilled workers to infer the main driver of job and wage polarization. We find strong evidence that there is more automation in big cities; big cities also have a disproportionate decrease in the share of routine cognitive jobs (clerical workers and low level white collar workers) We propose an economic mechanism where the substitutability of routine workers by IT leads to higher IT adoption in large cities than in small cities. Wages and productivity are higher in large cities, whereas technology prices are constant across cities. This technology also generates thick tails in the skill distribution in large cities.
Sustainable Social Security: Four Options
Year: 2011

This paper presents four policy options to make Social Security sustainable under the coming demographic shift: 1) increase payroll taxes by 6 percentage points, 2) reduce the replacement rates of the benefit formula by one third, 3) raise the normal retirement age from sixty six to seventy three, or 4) means test the benefits and reduce them one to one with income. While all four policies achieve the same goal, their economic outcomes differ significantly. Options 2 and 3 encourage own savings, and capital stock is more than 10 percent higher than in the other two options. The payroll tax increase in option 1 discourages work effort, but means testing the benefits as outlined in option 4 yields the worst labor disincentives, especially among the elderly.
Model Development and Incremental Learning Based on Case Based Reasoning for Signal and Image Analysis
Venue: CompIMAGE
Year: 2016

Over the years, image mining and knowledge discovery gained importance to solving problems. They are used in developing systems for automatic signal analysis and interpretation. The issues of model building and adaption, allowing an automatic system to adjust to the changing environments and moving objects, became increasingly important. One method of achieving adaptation in model building and model learning is Case Based Reasoning (CBR) Case Based Reasoning can be seen as a reasoning method as well as an incremental learning and knowledge acquisition method. In this paper we provide an overview of the CBR process and its main features: similarity, memory organization, CBR learning, and case base maintenance. Then we review, based on applications, what has been achieved so far. The applications we are focusing on are meta learning for parameter selection, image interpretation, incremental prototype based classification, novelty detection and handling, and 1 D signal interpretation represented by a 0_1 sequence. Finally, we will summarize the overall concept of CBR usage for model development and learning.
Analysis and Control of a Novel Transformer Less Microinverter for PV Grid Interface
Venue: IEEE Journal of Photovoltaics
Year: 2018

In this paper, a novel single phase, transformer less microinverter configuration derived from the basic \ddot{\text C}$uk topology is proposed for photovoltaic (PV) grid interface applications. The topology has a direct connection between the grid neutral and the negative terminal of the PV array, which completely eliminates the ground leakage current. Further, the proposed topology has several appealing features such as high reliability due to the absence of shoot through problems, symmetrical operation, low grid current distortion, capability of processing reactive power, low voltage ride through capability, and simple control for tracking the output current reference. Small signal analysis has been employed to derive the topology's dynamic model using which a proportional resonant (PR) controller is designed and implemented. A step by step design procedure for obtaining the optimized proportional and integral gains of the PR controller, considering the effects of pulsewidth modulation transport and digital sampling delays, is presented. Theoretical claims and simulation results are corroborated by experimental studies carried out on a 200 W laboratory prototype controlled by using DSP TMS320F28335 controller.
Influence of electron beam treatment on naturally contaminated red pepper (Capsicum annuum L. powder: Kinetics of microbial inactivation and physicochemical quality changes
Year: 2021

This study reported the impact of electron beam (e beam) treatment on microbiota and mycotoxins naturally present in red pepper powder and physicochemical quality changes. Treatment at 6 kGy indicated significant (p 85% of total phenols, carotenoids and antioxidants activity. Moreover, treatment impact on total color difference (DE* indicated 'slight differences' Overall, e beam treatments up to 10 kGy were efficient in decontaminating the natural microbiota without detrimental effects on the physicochemical qualities of red pepper powder.
Stan: Spatio Temporal Adversarial Networks for Abnormal Event Detection
Venue: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2018

In this paper, we propose a novel abnormal event detection method with spatio temporal adversarial networks (STAN) We devise a spatio temporal generator which synthesizes an inter frame by considering spatio temporal characteristics with bidirectional ConvLSTM. A proposed spatio temporal discriminator determines whether an input sequence is real normal or not with 3D convolutional layers. These two networks are trained in an adversarial way to effectively encode spatio temporal features of normal patterns. After the learning, the generator and the discriminator can be independently used as detectors, and deviations from the learned normal patterns are detected as abnormalities. Experimental results show that the proposed method achieved competitive performance compared to the state of the art methods. Further, for the interpretation, we visualize the location of abnormal events detected by the proposed networks using a generator loss and discriminator gradients.
Using Hybrid Filter Wrapper Feature Selection With Multi Objective Improved Salp Optimization for Crack Severity Recognition
Venue: IEEE Access
Year: 2020

The emerging technology of Structural Health Monitoring (SHM) paved the way for spotting and continuous tracking of structural damage. One of the major defects in historical structures is cracking, which represents an indicator of potential structural deterioration according to its severity. This paper presents a novel crack severity recognition system using a hybrid filter wrapper with multi objective optimization feature selection method. The proposed approach comprises two main components, namely, (1) feature extraction based on hand crafted feature engineering and CNN based deep feature learning and (2) feature selection using hybrid filter wrapper with a multi objective improved salp swarm optimization. The proposed approach is trained and validated by utilizing 10 representative UCI datasets and 4 datasets of crack images. The obtained experimental results show that the proposed system enhances the performance of crack severity recognition with 37% and 31% increase in recognition average accuracy and F measure, respectively. Also, a reduction rate of 67% is achieved in the extracted feature set with all the tested datasets compared to the conventional classification approaches using the whole set of features. Moreover, the proposed approach outperforms other approaches with classical feature selection methods in terms of feature reduction rate and computational time. It is noticed that using VGG16 learned features outperforms using the fused hand crafted features by 17.7% 15.9% and 23.5% for fine, moderate, and severe crack recognition, respectively. The significance of this paper is to investigate and highlight the impact of applying multi feature dimensionality reduction through adopting hybrid filter wrapper with multi objective optimization methods for feature selection considering the case study of crack severity recognition for SHM.
Optimization of Control Agents Shifts in Public Transportation: Tackling Fare Evasion with Machine Learning
Venue: 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)
Year: 2018

In this article, we present a research project aiming at tackling fare evasion in public transportation by optimizing the action of control agents. We give an overview of an algorithm that combines reinforcement learning techniques with optimization methods in order to predict which are the areas of the network where fraud is particularly high and generate itineraries accordingly. The proposed solution combines public and private data and is intended to be suited for most transportation operators worldwide. Its first deployment territory will be in the region of Paris (2018)
Interviewing for the Purposes of Gathering Intelligence Unchartered Waters Social Science in Intelligence Interviewing Contexts
Year: 2015

Contrary to the harsh 'enhanced interrogation' techniques employed during CIA operations on the War on Terror, contemporary research demonstrates that rapport building is a more effective way of achieving positive outcomes in interviews with general offender populations. Comparatively less research has explored the role of rapport with recalcitrant suspects, particularly suspected terrorists. We define a 'terrorist suspect' as an individual that a law enforcement officer has reasonable suspicion of involvement in a terrorist offence, with an 'investigative interview''interrogation' as the formal questioning of the suspect by one or more police officers following the suspect's arrest. This paper aims to develop the internal coherence of rapport approaches in interviews with terrorist suspects by providing a review of how and why rapport underpins successful police interrogations with suspected terrorists. We examine the much misunderstood and ill defined concept of rapport and how recent recent research has observed and measured rapport in operational field settings. We then examine the conceptual basis of an emerging research and training tool, ORBIT, and outline the reasons why rapport and tools such as ORBIT should be effective in (i) reducing counter interrogation tactics and (ii) increasing the amount of evidence and information generated. The review concludes by acknowledging the practical implications this has for interrogation and human intelligence practices.
Autonomous Driving Ethics Case Study for Engineering Ethics Education
Venue: ICMET 2019
Year: 2019

With the development of autonomous driving technology, it is of great practical and educational significance to consider the ethics in the process of its technology realization. On the one hand, compared with foreign countries, engineering ethics education still needs to be actively promoted and innovated in China. On the other hand, carrying out case study of engineering ethics meets the practical needs of training engineering talents. Based on this, this paper adopts a case study strategy centered on the autonomous driving ethics, and transforms the traditional teacher centered teaching mode into a student centered teaching mode in the form of role playing and collaborative discussion. In addition, a curriculum design framework is proposed, which highlights initiative, participation and self study guidance. Then, a simulation case of autonomous driving with increasingly complex scenarios is designed, guiding students to consider ethical issues in a deeper and more comprehensive way. As a result, the described teaching plan deepens the ethical awareness of engineering students in the project, and improves their ability to analyze and solve multi dimensional engineering ethical problems.
Strategic Asset Allocation With Climate Change
Year: 2019

We develop a top down strategic pathway towards green investing and show that eco investing should not be a puzzle or a sacrifice if investors consider the unknown impact of climate change on different asset classes. We investigate the impact of climate change over a large universe of asset classes including stocks, bonds, alternatives and a list of green assets which have low carbon emissions, in terms of their time varying risk return trade offs using a factor based vector autoregression (VAR) model with climate risk as an additional state variable. Estimation results show that green assets are in general resilient against temperature change, while many grey assets are negatively related to the risk of warming. When simulating future investment opportunity set, we disentangle the climate effects from the return processes and introduce two climate scenarios: an optimistic scenario implied by the VAR estimates; and a pessimistic one based on the DICE model. We find a substantial demand for green assets under the pessimistic scenario, which verifies the hypothesis that investors with a decent awareness of climate change should find it profitable to invest in green.
The Role of Glucagon Like Peptide 1 (GLP1) in Type 3 Diabetes: GLP 1 Controls Insulin Resistance, Neuroinflammation and Neurogenesis in the Brain
Venue: International journal of molecular sciences
Year: 2017

Alzheimer's disease (AD) characterized by the aggregation of amyloid b (Ab) protein and neuroinflammation, is the most common neurodegenerative disease globally. Previous studies have reported that some AD patients show impaired glucose utilization in brain, leading to cognitive decline. Recently, diabetes induced dementia has been called "type 3 diabetes" based on features in common with those of type 2 diabetes and the progression of AD. Impaired glucose uptake and insulin resistance in the brain are important issues in type 3 diabetes, because these problems ultimately aggravate memory dysfunction in the brain. Glucagon like peptide 1 (GLP 1) has been known to act as a critical controller of the glucose metabolism. Several studies have demonstrated that GLP 1 alleviates learning and memory dysfunction by enhancing the regulation of glucose in the AD brain. However, the specific actions of GLP 1 in the AD brain are not fully understood. Here, we review evidences related to the role of GLP 1 in type 3 diabetes.
Motion estimation using the monogenic signal applied to ultrasound elastography
Venue: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology
Year: 2010

This paper presents a phase based method for estimating subpixel motion in medical ultrasound imaging. The main novelty is to propose a closed form expression for local motion estimation in space domain based on the model of the monogenic signal. First, the proposed method uses the properties of the monogenic signal in order to extract local orientation and phase data of ultrasound images. Second, using these image features, we show how the proposed method can analytically estimate the dense motion field between a pair of images. This way of estimating motion provides subpixel accuracy without interpolating the images. For this reason, we show that the proposed method has high computational efficiency. Results on simulated and experimental data show that this method provides a better Contrast to Noise Ratio than a classical block matching method applied to images that are 25 times larger (in number of pixels)
Active Contact Enhancements With Stretchable Soft Layers and Piezoresistive Tactile Array for Robotic Grippers*
Venue: 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)
Year: 2019

Intensive research in sensors and human robot interactions has made the social presence of robots safer and more acceptable. In any interaction of the robot with its surroundings, it is important to establish proper contact with the object to ensure complete control over its manipulation. The sense of touch and perception play a vital role to close the feedback loop of contact modulations. This paper discusses the design and development of a novel hybrid sensorized soft matter structure which has active contact modulation capabilities to ensure better and firm contact in rigid robotic grippers. An integrated multi taxel piezoresistive tactile sensor which can confer to both rigid and soft surfaces easily was developed as a feedback device. Experimental observations show enhancement in grip and demonstrate a simple slip detection routine under dynamic load condition successfully.
Unmasking text plagiarism using syntactic semantic based natural language processing techniques: Comparisons, analysis and challenges
Venue: Inf. Process. Manag.
Year: 2018

The proposed work aims to explore and compare the potency of syntactic semantic based linguistic structures in plagiarism detection using natural language processing techniques. The current work explores linguistic features, viz. part of speech tags, chunks and semantic roles in detecting plagiarized fragments and utilizes a combined syntactic semantic similarity metric, which extracts the semantic concepts from WordNet lexical database. The linguistic information is utilized for effective pre processing and for availing semantically relevant comparisons. Another major contribution is the analysis of the proposed approach on plagiarism cases of various complexity levels. The impact of plagiarism types and complexity levels, upon the features extracted is analyzed and discussed. Further, unlike the existing systems, which were evaluated on some limited data sets, the proposed approach is evaluated on a larger scale using the plagiarism corpus provided by PAN11http:/pan.webis.de. competition from 2009 to 2014. The approach presented considerable improvement in comparison with the top ranked systems of the respective years. The evaluation and analysis with various cases of plagiarism also reflected the supremacy of deeper linguistic features for identifying manually plagiarized data.