nils-herrmann/clean_scirepeval huggingface dataset:
Proto cooperation: group hunting sailfish improve hunting success by alternating attacks on grouping prey
Venue: Proceedings of the Royal Society B: Biological Sciences
Year: 2016

We present evidence of a novel form of group hunting. Individual sailfish (Istiophorus platypterus) alternate attacks with other group members on their schooling prey (Sardinella aurita) While only 24% of attacks result in prey capture, multiple prey are injured in 95% of attacks, resulting in an increase of injured fish in the school with the number of attacks. How quickly prey are captured is positively correlated with the level of injury of the school, suggesting that hunters can benefit from other conspecifics' attacks on the prey. To explore this, we built a mathematical model capturing the dynamics of the hunt. We show that group hunting provides major efficiency gains (prey caught per unit time) for individuals in groups of up to 70 members. We also demonstrate that a free riding strategy, where some individuals wait until the prey are sufficiently injured before attacking, is only beneficial if the cost of attacking is high, and only then when waiting times are short. Our findings provide evidence that cooperative benefits can be realized through the facilitative effects of individuals' hunting actions without spatial coordination of attacks. Such 'proto cooperation' may be the pre cursor to more complex group hunting strategies.
Multi scale linkages of winter drought variability to ENSO and the Arctic Oscillation: A case study in Shaanxi, North China
Year: 2018

Understanding the potential connections between climate indices such as the El Nino Southern Oscillation (ENSO) and Arctic Oscillation (AO) and drought variability will be beneficial for making reasonable predictions or assumptions about future regional droughts, and provide valuable information to improve water resources planning and design for specific regions of interest. This study is to examine the multi scale relationships between winter drought variability over Shaanxi (North China) and both ENSO and AO during the period 1960 2009. To accomplish this, we first estimated winter dryness/wetness conditions over Shaanxi based on the self calibrating Palmer drought severity index (PDSI) Then, we identified the spatiotemporal variability of winter dryness/wetness conditions in the study area by using the empirical orthogonal function (EOF) Two primary sub regions of winter dryness/wetness conditions across Shaanxi were identified. We further examined the periodical oscillations of dryness/wetness conditions and the multi scale relationships between dryness/wetness conditions and both ENSO and AO in winter using wavelet analysis. The results indicate that there are inverse multi scale relations between winter dryness/wetness conditions and ENSO (according to the wavelet coherence) for most of the study area. Moreover, positive multi scale relations between winter dryness/wetness conditions and AO are mainly observed. The results could be beneficial for making reasonable predictions or assumptions about future regional droughts and provide valuable information to improve water resources planning and design within this study area. In addition to the current study area, this study may also offer a useful reference for other regions worldwide with similar climate conditions.
A Novel Secure Image Steganography Method Based on Chaos Theory in Spatial Domain
Year: 2014

This paper presents a novel approach of building a secure data hiding technique in digital images. The image steganography technique takes the advantage of limited power of human visual system (HVS) It uses image as cover media for embedding secret message. The most important requirement for a steganographic algorithm is to be imperceptible while maximizing the size of the payload. In this paper a method is proposed to encrypt the secret bits of the message based on chaos theory before embedding into the cover image. A 3 3 2 LSB insertion method has been used for image steganography. Experimental results show a substantial improvement in the Peak Signal to Noise Ratio (PSNR) and Image Fidelity (IF) value of the proposed technique over the base technique of 3 3 2 LSB insertion.
A Novel Long and Short Term Memory Network with Time Series Data Analysis Capabilities
Year: 2020

Time series data are an extremely important type of data in the real world. Time series data gradually accumulate over time. Due to the dynamic growth in time series data, they tend to have higher dimensions and large data scales. When performing cluster analysis on this type of data, there are shortcomings in using traditional feature extraction methods for processing. To improve the clustering performance on time series data, this study uses a recurrent neural network (RNN) to train the input data. First, an RNN called the long short term memory (LSTM) network is used to extract the features of time series data. Second, pooling technology is used to reduce the dimensionality of the output features in the last layer of the LSTM network. Due to the long time series, the hidden layer in the LSTM network cannot remember the information at all times. As a result, it is difficult to obtain a compressed representation of the global information in the last layer. Therefore, it is necessary to combine the information from the previous hidden unit to supplement all of the data. By stacking all the hidden unit information and performing a pooling operation, a dimensionality reduction effect of the hidden unit information is achieved. In this way, the memory loss caused by an excessively long sequence is compensated. Finally, considering that many time series data are unbalanced data, the unbalanced K means (UK means) algorithm is used to cluster the features after dimensionality reduction. The experiments were conducted on multiple publicly available time series datasets. The experimental results show that LSTM based feature extraction combined with the dimensionality reduction processing of the pooling technology and cluster processing for imbalanced data used in this study has a good effect on the processing of time series data.
Founder retention as CEO at IPO in emerging economies: The role of private equity owners and national institutions
Venue: Journal of Business Venturing
Year: 2019

We integrate the institutional perspective with research on the governance role of private equity firms in an investigation of Founder CEO successions in Initial Public Offerings (IPOs) in emerging markets. Using a unique, hand collected and comprehensive sample of 191 firms having undertaken IPOs in 21 markets across the African continent between January 2000 and August 2016, we apply instrumental variable (IV) Probit methodology and find that higher levels of private equity ownership are positively associated with the probability of the founder's retention as CEO, especially in the context of low quality formal institutions. Further, in societies with high tribalism, higher private equity ownership is associated with an increased likelihood of founder retention. Voids in the institutional architecture underscore the importance of the founder as a key organizational resource for the firm and a source of institutionalized legitimacy, which in turn confers on the firm an ability to access required resources.
Salidroside mitigates hypoxia/reoxygenation injury by alleviating endoplasmic reticulum stress induced apoptosis in H9c2 cardiomyocytes
Venue: Molecular medicine reports
Year: 2018

Endoplasmic reticulum (ER) stress induced apoptosis serves a crucial role in the development of myocardial ischemia/reperfusion (I/R) injury. Salidroside is a phenylpropanoid glycoside isolated from Rhodiola rosea L. which is a plant often used in traditional Chinese medicine. It possesses multiple pharmacological actions and protects against myocardial I/R injury in vitro and in vivo. However, it is not yet clear whether ER stress or ER stress induced apoptosis contributes to the cardioprotective effects of salidroside against myocardial I/R injury. Hence, hypoxia/reoxygenation (H/R) treated H9c2 cardiomyocytes were used in the current study to mimic myocardium I/R injury in vivo. It was hypothesized that salidroside alleviates ER stress and ER stress induced apoptosis, thereby reducing H/R injury in H9c2 cells. The results demonstrated that salidroside attenuated H/R induced H9c2 cardiomyocyte injury, as cell viability was increased, lactate dehydrogenase release was decreased, morphological changes in apoptotic cells were ameliorated and the apoptosis ratio was reduced compared with the H/R group. ER stress was reversed, indicated by the downregulation of glucose regulated protein 78 and C/EBP homologous protein following pretreatment with salidroside. In addition, salidroside attenuated ER stress induced apoptosis, as the expression of cleaved caspase 12 and pro apoptotic protein Bcl 2 associated X protein and activity of caspase 3 was decreased, while the expression of anti apoptotic protein Bcl 2 was increased following pretreatment with salidroside. Furthermore, the results indicated that salidroside decreases the activation of the ER stress associated signaling pathway, as the expression of phosphorylated protein kinase RNA (PKR) like ER kinase (p PERK) and phosphorylated inositol requiring enzyme 1a (p IRE1a) proteins were decreased following pretreatment with salidroside. These results demonstrate that salidroside protects against H/R injury via regulation of the PERK and IRE1a pathways, resulting in alleviation of ER stress or ER stress induced apoptosis in H9c2 cardiomyocytes.
Diversity in thermal conductivity of aqueous Al 2 O 3 and Ag nanofluids measured by transient hot wire and laser flash methods
Year: 2018

Transient hot wire and laser flash methods are the two commonly used techniques for measuring the thermal conductivity of nanofluids. However, a comparative study of these two techniques considering identical nanofluids has not been carried out. In this regard, the current study aims at comparison of two methods with identical distribution of Al2O3 and Ag nanoparticles in water. The study also accounts quantitatively for the wide difference in the measured thermal conductivities on the basis of the flux of nanoparticles across the thermal boundary layer resulting from the Brownian movement. It has been observed that the collision flux of the nanoparticle increases with increase in the initial perpendicular distance of the nanoparticle from the heat source wall. Such an increase results in higher thermal conductivity in case of transient hot wire compared to laser flash method.
Multi View Spectral Clustering via ELM AE Ensemble Features Representations Learning
Venue: IEEE Access
Year: 2020

Spectral cluster based on multi view data has proven effective for clustering multi source real world data because consensus and complementary information of multi view data ensure the result of clustering. Feature learning is the vital step in spectral clustering, and excellent feature representations can effectively improve spectral clustering performance. In this article, we propose Multi View Spectral Clustering via ELM Autoencoder Ensemble Features Representations Learning (MvSC EF ELM) First, Extreme Learning Machine as an Autoencoder(ELM AE) learns feature representations by adopting a singular value decomposition, which reconstructs the inputs using the embedding feature space. Second, the single view and multi view spectral clustering algorithm are applied embedding feature representations space into the eigenspace and cluster's them, respectively. Experiments on benchmark datasets demonstrate that our approach empirically validates the power of ELM AE for feature learning from raw data and may effectively facilitate multi view spectral clustering and induce superior clustering results.
Emotion Detection and Sentiment Analysis in Text Corpus: A Differential Study with Informal and Formal Writing Styles
Year: 2014

Text, either online or offline can be presented in two different writing styles: formal and informal writing style. A Piece of text may contain a lot of emotion, ideas or feelings. Various techniques and methods are present in the field of Opinion Mining and Sentiment Analysis to extract the emotions from text. This paper presents a differential analysis of Formal and Informal text pieces in the field of Sentiment Classification. This paper presents a study and analysis of differences of approaches used for Emotion Detection and Sentiment Analysis for both cases. In this study, 10 formal text pieces in form of poetry, proverbs, essay and document are analyzed. These text pieces are present in 7 different International languages (i.e. Persian, Spanish, Chinese, Arabic, Malaysian, English, Ottoman) Informal text, in form of chats, emails, review sites and micro blogs, written in different International languages (Korean, Persian and English) are considered in this study. Various machine learning based methods Support Vector Machine(SVM) Naive Bayes (NB) Decision Tree are more often used in classification of literary arts especially poetry. Statistical Machine learning approach Support Vector Machine outperforms all other methods in case of poetry and NB performed well in case of Informal Writing Style.
Midori: A Block Cipher for Low Energy
Venue: ASIACRYPT
Year: 2015

In the past few years, lightweight cryptography has become a popular research discipline with a number of ciphers and hash functions proposed. The designers' focus has been predominantly to minimize the hardware area, while other goals such as low latency have been addressed rather recently only. However, the optimization goal of low energy for block cipher design has not been explicitly addressed so far. At the same time, it is a crucial measure of goodness for an algorithm. Indeed, a cipher optimized with respect to energy has wide applications, especially in constrained environments running on a tight power/energy budget such as medical implants. This paper presents the block cipher Midorii 3/4 ?The name of the cipher is the Japanese translation for the word Green. that is optimized with respect to the energy consumed by the circuit per bt in encryption or decryption operation. We deliberate on the design choices that lead to low energy consumption in an electrical circuit, and try to optimize each component of the circuit as well as its entire architecture for energy. An added motivation is to make both encryption and decryption functionalities available by small tweak in the circuit that would not incur significant area or energy overheads. We propose two energy efficient block ciphers Midori128i 3/4 ?and Midori64i 3/4 ?with block sizes equal to 128 and 64 bits respectively. These ciphers have the added property that a circuit that provides both the functionalities of encryption and decryption can be designed with very little overhead in terms of area and energy. We compare our results with other ciphers with similar characteristics: it was found that the energy consumptions ofi 3/4 ?Midori64i 3/4 and Midori128i 3/4 are by far better when compared ciphers like PRINCE and NOEKEON.
Highly efficient oxygen electrode catalyst derived from chitosan biomass by molten salt pyrolysis for zinc air battery
Year: 2020

The highly efficient electrocatalysts with foam structure for oxygen transformation reaction were prepared by the pyrolysis of chitosan biomass in molten KCl at 800 degC and heating treatment at 800 degC (CK 800 800, where C refers to chitosan, K means molten KCl and 800 stands for temperature of pyrolysis and heat treatment) The dispersive effect and causticity of molten salt as well as isolation function of solid salt contribute to the formation of foam structure with excellent capacity of mass transmission. After heating treatment, N content, defect content and specific surface area of catalyst are raised, which results in high active site density. The obtained catalyst CK 800 800 exhibits better catalytic activity (Ehalf wave 0.86 V vs. RHE) stability and methanol resistance than commercial Pt/C for oxygen reduction reaction (ORR) in alkaline media. Zinc air battery assembled with CK 800 800 shows more excellent peak power density (178 mW cm 2) specific capacity (780 mAh gZn 1) and cycling stability than that with Pt/C. Therefore, CK 800 800 is a promising alternative to Pt/C in energy storage/conversion devices.
Social Upliftment of Oraon Tribes through Education
Year: 2019

The researcher intends to study the impact of education on social status of Oraon tribe, in the area of Bilaspur, Jashpur and Sarguja districts of Chhattisgarh. The tribal areas opens in the wake of developmental process have brought in combination of two distinct value systems one based on tradition and ignorance and the other one technology and innovations. The Oraon tribe depends mostly on the agriculture and forest produce for their livelihood. They are very shy in nature. The researcher tries to focus the Impact of education on social status of Chhattisgarh's Oraon tribe. The researcher wants to enchain the attention of society and government for the upliftment and development of the Oraon tribe over there. They are mostly unaware about the government welfare programmes. The Oraon tribes are not advanced more socially but accept due to educational impact it helps to uplift their status on society and helping to uplift status of Oraon tribe.
Reflections on My 1969 Smith College School for Social Work Thesis: Children's Use of Humor in Psychotherapy
Year: 2017

I had wanted to do my thesis on family's use of humor in therapy. My first lesson was that that was too complicated a project a lesson in practicality, which influenced my entire future career. My thesis also gave me the chance to focus on children's resourcefulness and resilience, rather than just on their problems and psychopathology. After graduation, my advisor proposed that I publish my thesis. Her generosity in mentoring me toward publication was an extraordinary boon. I had no idea how to go about submitting something for publication, and she guided me through the process, giving me confidence to proceed professionally. I believe practitioners need to be encouraged, as I was, to push their ideas and insights into expanding theory and practice for the benefit of our communities for the future. We have a great need to push the boundaries of support, change, healing, and transformation.
Optimal Decision Making Under Strategic Behavior
Venue: ArXiv
Year: 2019

We are witnessing an increasing use of data driven predictive models to inform decisions. As decisions have implications for individuals and society, there is increasing pressure on decision makers to be transparent about their decision policies, models, and the features they use. At the same time, individuals may use knowledge, gained by transparency, to invest effort strategically in order to maximize their chances of receiving a beneficial decision. In this paper, our goal is to find decision policies that are optimal in terms of utility in such a strategic setting. To this end, we first use the theory of optimal transport to characterize how strategic investment of effort by individuals leads to a change in the feature distribution at a population level. Then, we show that, in contrast with the non strategic setting, optimal decision policies are stochastic, and we cannot expect to find them in polynomial time. Finally, we derive an efficient greedy algorithm that is guaranteed to find locally optimal decision policies in polynomial time. Experiments on synthetic and real lending data illustrate our theoretical findings and show that the decision policies found by our greedy algorithm achieve higher utility than deterministic threshold rules, which are optimal policies in a non strategic setting.
Robustness of Autoencoders for Anomaly Detection Under Adversarial Impact
Venue: IJCAI
Year: 2020

Detecting anomalies is an important task in a wide variety of applications and domains. Deep learning methods have achieved state of the art performance in anomaly detection in recent years; unsupervised methods being particularly popular. However, deep learning methods can be fragile to small perturbations in the input data. This can be exploited by an adversary to deliberately hinder model performance; an adversarial attack. This phenomena has been widely studied in the context of supervised image classification since its discovery, however such studies for an anomaly detection setting are sorely lacking. Moreover, the plethora of defense mechanisms that have been proposed are often not applicable to unsupervised anomaly detection models. In this work, we study the effect of adversarial attacks on the performance of anomaly detecting autoencoders using real data from a Cyber physical system (CPS) testbed with intervals of controlled, physical attacks as anomalies. An adversary would attempt to disguise these points as normal through adversarial perturbations. To combat this, we propose the Approximate Projection Autoencoder (APAE) which incorporates two defenses against such attacks into a general autoencoder. One of these involves a novel technique to improve robustness under adversarial impact by optimising latent representations for better reconstruction outputs.
The Impact of the Covid 19 Pandemic on Greek Tourism
Year: 2020

This paper examines the economic impact of the 2020 pandemic on international Greek tourism receipts. The uncertainty and risk surrounding the epidemiological conditions make any reliable prognosis of the 2020 and beyond almost impossible. This study relies on information regarding the international pre bookings of European destinations. According the to the European Union's Travel and Tourism Industry, pre booking have fallen by 60 to 90 percent. Using these two extreme values and the average of the two, this study develops three scenarios to examine the impact of the pandemic on Greek tourism. In the best of the three scenarios, the contribution of Greek tourism to GDP will drop from 16% to 6.6% The output loss amounts to EUR16.8 billion.
Joint 3 D tomographic imaging of Vp, Vs and Vp/Vs and hypocenter relocation at Sinabung volcano, Indonesia from November to December 2013
Venue: Journal of Volcanology and Geothermal Research
Year: 2019

We conducted travel time tomography using P and S wave arrival times of volcanic tectonic (VT) events that occurred between November and December 2013 to determine the three dimensional (3D) seismic velocity structure (Vp, Vs, and Vp/Vs) beneath Sinabung volcano, Indonesia in order to delineate geological subsurface structure and to enhance our understanding of the volcanism itself. This was a time period when phreatic explosions became phreatomagmatic and then magma migrated to the surface forming a summit lava dome. We used 4846 VT events with 16,138 P and 16,138 S wave arrival time phases recorded by 6 stations for the tomographic inversion. The relocated VTs collapse into three clusters at depths from the surface to sea level, from 2 to 4 km below sea level, and from 5 to 8.5 km below sea level. The tomographic inversion results show three prominent regions of high Vp/Vs 1.8) beneath Sinabung volcano at depths consistent with the relocated earthquake clusters. We interpret these anomalies as intrusives associated with previous eruptions and possibly surrounding the magma conduit, which we cannot resolve with this study. One anomalous region might contain partial melt, at sea level and below the eventual eruption site at the summit. Our results are important for the interpretation of a conceptual model of the "plumbing system" of this hazardous volcano.