nils-herrmann/clean_scirepeval huggingface dataset:
Using Social Media to Understand Cyber Attack Behavior

As networked and computer technologies continue to pervade all aspects of our lives, the threat from cyber attacks has also increased. However, detecting attacks, much less predicting them in advance, is a non-trivial task due to the anonymity of cyber attackers and the ambiguity of network data collected within an organization; often, by the time an attack pattern is recognized, the damage has already been done. Evidence suggests that the public discourse in external sources, such as news and social media, is often correlated with the occurrence of larger phenomena, such as election results or violent attacks. In this paper, we propose an approach that uses sentiment polarity as a sensor to analyze the social behavior of groups on social media as an indicator of cyber attack behavior. We developed an unsupervised sentiment prediction method that uses emotional signals to enhance the sentiment signal from sparse textual indicators. To explore the efficacy of sentiment polarity as an indicator of cyberattacks, we performed experiments using real-world data from Twitter that corresponds to attacks by a well-known hacktivist group.
The diabetes risk score: a practical tool to predict type 2 diabetes risk.

Interventions to prevent type 2 diabetes should be directed toward individuals at increased risk for the disease. To identify such individuals without laboratory tests, we developed the Diabetes Risk Score.



A random population sample of 35- to 64-year-old men and women with no antidiabetic drug treatment at baseline were followed for 10 years. New cases of drug-treated type 2 diabetes were ascertained from the National Drug Registry. Multivariate logistic regression model coefficients were used to assign each variable category a score. The Diabetes Risk Score was composed as the sum of these individual scores. The validity of the score was tested in an independent population survey performed in 1992 with prospective follow-up for 5 years.



Age, BMI, waist circumference, history of antihypertensive drug treatment and high blood glucose, physical activity, and daily consumption of fruits, berries, or vegetables were selected as categorical variables. Complete baseline risk data were found in 4435 subjects with 182 incident cases of diabetes. The Diabetes Risk Score value varied from 0 to 20. To predict drug-treated diabetes, the score value >or=9 had sensitivity of 0.78 and 0.81, specificity of 0.77 and 0.76, and positive predictive value of 0.13 and 0.05 in the 1987 and 1992 cohorts, respectively.



The Diabetes Risk Score is a simple, fast, inexpensive, noninvasive, and reliable tool to identify individuals at high risk for type 2 diabetes.
Skill Inference with Personal and Skill Connections

Personal skill information on social media is at the core of many interesting applications. In this paper, we propose a factor graph based approach to automatically infer skills from personal profile incorporated with both personal and skill connections. We first extract personal connections with similar academic and business background (e.g. co-major, co-university, and co-corporation). We then extract skill connections between skills from the same person. To well integrate various kinds of connections, we propose a joint prediction factor graph (JPFG) model to collectively infer personal skills with help of personal connection factor, skill connection factor, besides the normal textual attributes. Evaluation on a large-scale dataset from LinkedIn.com validates the effectiveness of our approach.
High gain 2.45 GHz 2×2 patch array stacked antenna

This paper presents 2×2 patch array antenna for 2.45 GHz industrial, scientific and medical (ISM) band application. In this design, four array radiating elements interconnected with a transmission line and excited by 50Ω subminiature (SMA). The proposed antenna structure is combined with a reflector in order to investigate the effect of air gap between radiating element and reflector in terms of reflection coefficient (S11) bandwidth and realized gain. The analysis on the effect of air gap has significantly achieved maximum reflection coefficient and realized gain of -16 dB and 19.29 dBi respectively at 2.45 GHz.
A compact printed UWB Pacman-shaped MIMO antenna with two frequency rejection bands

In this paper, the design and analysis of microstrip-fed low-profile, compact ultra-wideband (UWB) monopole antenna with its dual diversity multiple-input multiple-output (MIMO) configuration and two band-notches characteristics are carried out. The antenna is mounted on a low cost FR-4 substrate with dimensions of 38×60×1.6 mm3, and relative permittivity of 4.4. The original shape of the single antenna element is circular with radius of 11.5 mm, and then a sector is removed from the patch (making it a Pacman-shaped antenna) to improve the impedance bandwidth. The proposed antenna provides an impedance bandwidth between 2.9-15 GHz with better than 10 dB return loss and isolation less than -17 dB. Additionally, the antenna can reject the interferences from WiMAX (3.5 GHz center frequency) and WLAN (5.5 GHz center frequency).
Cellular angiofibroma: a benign neoplasm distinct from angiomyofibroblastoma and spindle cell lipoma.

Four cases of a distinctive soft-tissue tumor of the vulva are described. They were characterized by occurrence in middle-aged women (39-50 years), small size (< 3 cm), and a usually well-circumscribed margin. The preoperative clinical diagnosis was that of a labial or Bartholin gland cyst in three of the four cases. The microscopic appearance was remarkably consistent and was characterized by a cellular neoplasm composed of uniform, bland, spindled stromal cells, numerous thick-walled and often hyalinized vessels, and a scarce component of mature adipocytes. Mitotic activity was brisk in three cases (up to 11 mitoses per 10 high power fields). The stromal cells were positive for vimentin and negative for CD34, S-100 protein, actin, desmin, and epithelial membrane antigen, suggesting fibroblastic differentiation. Two patients with follow-up showed no evidence of recurrence. The differential diagnosis of this distinctive tumor includes aggressive angiomyxoma, angiomyofibroblastoma, spindle cell lipoma, solitary fibrous tumor, perineurioma, and leiomyoma. The designation of "cellular angiofibroma" is chosen to emphasize the two principal components of this tumor: the cellular spindle cell component and the prominent blood vessels.
Latent Space Policies for Hierarchical Reinforcement Learning

We address the problem of learning hierarchical deep neural network policies for reinforcement learning. In contrast to methods that explicitly restrict or cripple lower layers of a hierarchy to force them to use higher-level modulating signals, each layer in our framework is trained to directly solve the task, but acquires a range of diverse strategies via a maximum entropy reinforcement learning objective. Each layer is also augmented with latent random variables, which are sampled from a prior distribution during the training of that layer. The maximum entropy objective causes these latent variables to be incorporated into the layer’s policy, and the higher level layer can directly control the behavior of the lower layer through this latent space. Furthermore, by constraining the mapping from latent variables to actions to be invertible, higher layers retain full expressivity: neither the higher layers nor the lower layers are constrained in their behavior. Our experimental evaluation demonstrates that we can improve on the performance of single-layer policies on standard benchmark tasks simply by adding additional layers, and that our method can solve more complex sparse-reward tasks by learning higher-level policies on top of high-entropy skills optimized for simple low-level objectives.
Elisa to quantify hexanal-protein adducts in a meat model system.

Monoclonal antibodies (MAb) were produced to hexanal-bovine serum albumin conjugates. An indirect competitive ELISA was developed with a detection range of 1-50 ng of hexanal/mL. Hexanal conjugated to three different proteins was recognized, whereas free hexanal and the native proteins were not detected. The antibody cross-reacted with pentanal, heptanal, and 2-trans-hexenal conjugated to chicken serum albumin (CSA) with cross-reactivities of 37.9, 76.6, and 45.0%, respectively. There was no cross-reactivity with propanal, butanal, octanal, and nonanal conjugated to CSA. The hexanal content of a meat model system was determined using MAb and polyclonal antibody-based ELISAs and compared with analysis by a dynamic headspace gas chromatographic (HS-GC) method and a thiobarbituric acid reactive substances (TBARS) assay. Both ELISAs showed strong correlations with the HS-GC and TBARS methods. ELISAs may be a fast and simple alternative to GC for monitoring lipid oxidation in meat.
An analysis of a packed bed latent heat thermal energy storage system using PCM capsules : Numerical investigation

This paper is aimed at analyzing the behavior of a packed bed latent heat thermal energy storage system. The packed bed is composed of spherical capsules filled with paraffin wax as PCM usable with a solar water heating system. The model developed in this study uses the fundamental equations similar to those of Schumann, except that the phase change phenomena of PCM inside the capsules are analyzed by using enthalpy method. The equations are numerically solved, and the results obtained are used for the thermal performance analysis of both charging and discharging processes. The effects of the inlet heat transfer fluid temperature (Stefan number), mass flow rate and phase change temperature range on the thermal performance of the capsules of various radii have been investigated. The results indicate that for the proper modeling of performance of the system the phase change temperature range of the PCM must be accurately known, and should be taken into account.
Anemia in pregnancy: Factors influencing knowledge and attitudes among mothers in southeastern Nigeria

Explored the factors that influence knowledge and attitudes toward anemia in pregnancy (AIP) in southeastern Nigeria. We surveyed 1500 randomly selected women who delivered babies within 6 months of the survey using a questionnaire. Twelve focus group discussions were held with the grandmothers and fathers of the new babies, respectively. Six in-depth interviews were held with health workers in the study communities. Awareness of AIP was high. Knowledge of its prevention and management was poor with a median score of 10 points on a 50-point scale. Living close to a health facility (p = 0.031), having post-secondary education (p <0.001), being in paid employment (p = 0.017) and being older (p = 0.027) influenced knowledge of AIP. Practices for the prevention and management of AIP were affected by a high level of education (p = 0.034) and having good knowledge of AIP issues (p <0.001). The qualitative data revealed that unorthodox means were employed in response to anemia in pregnancy. This is often delayed until complications set in. Many viewed anemia as a normal phenomenon among pregnant women. AIP awareness is high among the populations. However, management is poor because of poor knowledge of signs and timely appropriate treatment. Prompt and appropriate management of AIP is germane for positive pregnancy outcomes. Anemia-related public education is an urgent need in Southeast Nigeria. Extra consideration of the diverse social development levels of the populations should be taken into account when designing new and improving current prevention and management programs for anemia in pregnancy.
Human interactions and personal space in collaborative virtual environments

As humans start to spend more time in collaborative virtual environments (CVEs) it becomes important to study their interactions in such environments. One aspect of such interactions is personal space. To begin to address this, we have conducted empirical investigations in a non immersive virtual environment: an experiment to investigate the influence on personal space of avatar gender, and an observational study to further explore the existence of personal space. Experimental results give some evidence to suggest that avatar gender has an influence on personal space although the participants did not register high personal space invasion anxiety, contrary to what one might expect from personal space invasion in the physical world. The observational study suggests that personal space does exist in CVEs, as the users tend to maintain, in a similar way to the physical world, a distance when they are interacting with each other. Our studies provide an improved understanding of personal space in CVEs and the results can be used to further enhance the usability of these environments.
Human capital investment and economic growth in Nigeria : the role of education and health

This study looked at Human Capital Investment and Economic Growth in Nigeria – the Role of Education. Even though there are different perspectives to economic growth, there is a general consensus that growth will lead to a good change manifested in increased capacity of people to have control over material assets, intellectual resources and ideology, and obtain physical necessities of life like food, clothing, shelter, employment, e.t.c. This is why some people have argued that the purpose of growth is to improve peoples’ lives by expanding their choices, freedom and dignity. The belief in human capital as a necessity for growth started in Nigeria during the implementation of the 1955-60 Development Plan and today, with the importance of knowledge in the economy, human capital has increasingly attracted both academic and public interest. This study made use of the Unit Root and Augmented Dickey Fuller (ADF) tests and found out that a positive relationship exists between government expenditure on education and economic growth while a negative relationship exists between government expenditure on health and economic growth. Therefore, based on these findings, the study recommended that the Government should increase not just the amount of expenditure made on the education and health sectors, but also the percentage of its total expenditure accorded to these sectors. The ten percent benchmark proffered by the present national plan should be adopted.
Review: probiotics and their beneficial effects against various diseases.

's consultation report defines probiotics as: Live microorganisms which when administered in adequate amounts confer a health benefit on the host. Most commonly used probiotics are Lactic acid bacteria (LAB) and bifidobacteria. There are other examples of species used as probiotics (certain yeasts and bacilli). Probiotic supplements are popular now a days. From the beginning of 2000, research on probiotics has increased remarkably. Probiotics are now day's widely studied for their beneficial effects in treatment of many prevailing diseases. Here we reviewed the beneficiary effects of probiotics in some diseases.
A fully homomorphic encryption scheme with symmetric keys with application to private data processing in clouds

With single computer's computation power not sufficing, need for sharing resources to manipulate and manage data through clouds is increasing rapidly. Hence, it is favorable to delegate computations or store data with a third party, the cloud provider. However, delegating data to third party poses the risks of data disclosure during computation. The problem can be addressed by carrying out computation without decrypting the encrypted data. The results are also obtained encrypted and can be decrypted at the user side. This requires modifying functions in such a way that they are still executable while privacy is ensured or to search an encrypted database. Homomorphic encryption provides security to cloud consumer data while preserving system usability. We propose a symmetric key homomorphic encryption scheme based on matrix operations with primitives that make it easily adaptable for different needs in various cloud computing scenarios.
Bacterial impregnation of mineral fertilizers improves yield and nutrient use efficiency of wheat.

The fertilizer use efficiency (FUE) of agricultural crops is generally low, which results in poor crop yields and low economic benefits to farmers. Among the various approaches used to enhance FUE, impregnation of mineral fertilizers with plant growth-promoting bacteria (PGPB) is attracting worldwide attention. The present study was aimed to improve growth, yield and nutrient use efficiency of wheat by bacterially impregnated mineral fertilizers.



Results of the pot study revealed that impregnation of diammonium phosphate (DAP) and urea with PGPB was helpful in enhancing the growth, yield, photosynthetic rate, nitrogen use efficiency (NUE) and phosphorus use efficiency (PUE) of wheat. However, the plants treated with F8 type DAP and urea, prepared by coating a slurry of PGPB (Bacillus sp. strain KAP6) and compost on DAP and urea granules at the rate of 2.0 g 100 g-1 fertilizer, produced better results than other fertilizer treatments. In this treatment, growth parameters including plant height, root length, straw yield and root biomass significantly (P ≤ 0.05) increased from 58.8 to 70.0 cm, 41.2 to 50.0 cm, 19.6 to 24.2 g per pot and 1.8 to 2.2 g per pot, respectively. The same treatment improved grain yield of wheat by 20% compared to unimpregnated DAP and urea (F0). Likewise, the maximum increase in photosynthetic rate, grain NP content, grain NP uptake, NUE and PUE of wheat were also recorded with F8 treatment.



The results suggest that the application of bacterially impregnated DAP and urea is highly effective for improving growth, yield and FUE of wheat.
Investigating the "Wisdom of Crowds" at Scale

In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been termed the "wisdom of the crowd." Yet, perhaps surprisingly, there is still little consensus on how generally the phenomenon holds, how best to aggregate crowd judgements, and how social influence affects estimates. We investigate these questions by taking a meta wisdom of crowds approach. With a distributed team of over 100 student researchers across 17 institutions in the United States and India, we develop a large-scale online experiment to systematically study the wisdom of crowds effect for 1,000 different tasks in 50 subject domains. These tasks involve various types of knowledge (e.g., explicit knowledge, tacit knowledge, and prediction), question formats (e.g., multiple choice and point estimation), and inputs (e.g., text, audio, and video). To examine the effect of social influence, participants are randomly assigned to one of three different experiment conditions in which they see varying degrees of information on the responses of others. In this ongoing project, we are now preparing to recruit participants via Amazon?s Mechanical Turk.
Uneven dietary development: linking the policies and processes of globalization with the nutrition transition, obesity and diet-related chronic diseases

In a "nutrition transition", the consumption of foods high in fats and sweeteners is increasing throughout the developing world. The transition, implicated in the rapid rise of obesity and diet-related chronic diseases worldwide, is rooted in the processes of globalization. Globalization affects the nature of agri-food systems, thereby altering the quantity, type, cost and desirability of foods available for consumption. Understanding the links between globalization and the nutrition transition is therefore necessary to help policy makers develop policies, including food policies, for addressing the global burden of chronic disease. While the subject has been much discussed, tracing the specific pathways between globalization and dietary change remains a challenge. To help address this challenge, this paper explores how one of the central mechanisms of globalization, the integration of the global marketplace, is affecting the specific diet patterns. Focusing on middle-income countries, it highlights the importance of three major processes of market integration: (I) production and trade of agricultural goods; (II) foreign direct investment in food processing and retailing; and (III) global food advertising and promotion. The paper reveals how specific policies implemented to advance the globalization agenda account in part for some recent trends in the global diet. Agricultural production and trade policies have enabled more vegetable oil consumption; policies on foreign direct investment have facilitated higher consumption of highly-processed foods, as has global food marketing. These dietary outcomes also reflect the socioeconomic and cultural context in which these policies are operating. An important finding is that the dynamic, competitive forces unleashed as a result of global market integration facilitates not only convergence in consumption habits (as is commonly assumed in the "Coca-Colonization" hypothesis), but adaptation to products targeted at different niche markets. This convergence-divergence duality raises the policy concern that globalization will exacerbate uneven dietary development between rich and poor. As high-income groups in developing countries accrue the benefits of a more dynamic marketplace, lower-income groups may well experience convergence towards poor quality obseogenic diets, as observed in western countries. Global economic policies concerning agriculture, trade, investment and marketing affect what the world eats. They are therefore also global food and health policies. Health policy makers should pay greater attention to these policies in order to address some of the structural causes of obesity and diet-related chronic diseases worldwide, especially among the groups of low socioeconomic status.
Time-Dependent Representation for Neural Event Sequence Prediction

Existing sequence prediction methods are mostly concerned with time-independent sequences, in which the actual time span between events is irrelevant and the distance between events is simply the difference between their order positions in the sequence. While this time-independent view of sequences is applicable for data such as natural languages, e.g., dealing with words in a sentence, it is inappropriate and inefficient for many real world events that are observed and collected at unequally spaced points of time as they naturally arise, e.g., when a person goes to a grocery store or makes a phone call. The time span between events can carry important information about the sequence dependence of human behaviors. In this work, we propose a set of methods for using time in sequence prediction. Because neural sequence models such as RNN are more amenable for handling token-like input, we propose two methods for time-dependent event representation, based on the intuition on how time is tokenized in everyday life and previous work on embedding contextualization. We also introduce two methods for using next event duration as regularization for training a sequence prediction model. We discuss these methods based on recurrent neural nets. We evaluate these methods as well as baseline models on five datasets that resemble a variety of sequence prediction tasks. The experiments revealed that the proposed methods offer accuracy gain over baseline models in a range of settings.