nils-herrmann/clean_scirepeval huggingface dataset:
Commerce A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications

This paper discusses some aspects of selecting and testing random and pseudorandom number generators. The outputs of such generators may be used in many cryptographic apphcations, such as the generation of key material. Generators suitable for use in cryptographic applications may need to meet stronger requirements than for other applications. In particular, their outputs must be unpredictable in the absence of knowledge of the inputs. Some criteria for characterizing and selecting appropriate generators are discussed in this document. The subject of statistical testing and its relation to cryptanalysis is also discussed, and some recommended statistical tests are provided. These tests may be useful as a first step in determining whether or not a generator is suitable for a particular cryptographic application. However, no set of statistical tests can absolutely certify a generator as appropriate for usage in a particular application, i.e., statistical testing cannot serve as a substitute for cryptanalysis. The design and cryptanalysis of generators is outside the scope of this paper.
The factors associated with self-reported physical activity in older adults living in the community

despite the vast literature in the area, few studies examined the large range of factors associated with physical activity (PA) in the older adult collectively, information that could help develop a supportive culture for healthy ageing.  to identify the socio-demographic, social connectedness, physical environment and physical and mental healthrelated factors associated with PA.  adults aged 65 and over living in the community (n = 3499).  this study used a cross-sectional design. Self-report, interview and physical assessment were the data collection methods used. The International Physical Activity Questionnaire (short form) measured PA in metabolic equivalents.  31.8% of older people did not meet the recommended guidelines. The regression model was significant, explaining 31.3% of the variance in PA (F = 34.32, P < 0.001). In order of strength of relationship, the β coefficients, the variables time
Cyber security: challenges for society- literature review

Cyber security is the activity of protecting information and information systems (networks, computers, data bases, data centres and applications) with appropriate procedural and technological security measures. Firewalls, antivirus software, and other technological solutions for safeguarding personal data and computer networks are essential but not sufficient to ensure security. As our nation rapidly building its CyberInfrastructure, it is equally important that we educate our population to work properly with this infrastructure. Cyber-Ethics, Cyber-Safety, and Cyber-Security issues need to be integrated in the educational process beginning at an early age. Security counter measures help ensure the confidentiality, availability, and integrity of information systems by preventing or mitigating asset losses from Cyber security attacks. Recently cyber security has emerged as an established discipline for computer systems andinfrastructures with a focus on protection of valuable information stored on those systems fromadversaries who want to obtain, corrupt, damage, destroy or prohibit access to it. An Intrusion Detection System (IDS) is a program that analyses what happens or has happened during an execution and tries to find indications that the computer has been misused. A wide range of metaphors was considered, including those relating to: military and other types of conflict, biological, health care, markets, three-dimensional space, and physical asset protection. These in turn led to consideration of a variety of possible approaches for improving cyber security in the future. These approaches were labelled “Heterogeneity” ,“Motivating Secure Behaviour” and “Cyber Wellness” . Cyber Security plays an important role in the development of information technology as well as Internet services. Our attention is usually drawn on “Cyber Security” when we hear about “Cyber Crimes”. Our first thought on “National Cyber Security” therefore starts on how good is our infrastructure for handling “Cyber Crimes”. This paper focus on cyber security emerging trends while adopting new technologies such as mobile computing, cloud computing, e-commerce, and social networking. The paper also describes the challenges due to lack of coordination between Security agencies and the Critical IT Infrastructure.
Magnetic nanoparticles: synthesis, protection, functionalization, and application.

This review focuses on the synthesis, protection, functionalization, and application of magnetic nanoparticles, as well as the magnetic properties of nanostructured systems. Substantial progress in the size and shape control of magnetic nanoparticles has been made by developing methods such as co-precipitation, thermal decomposition and/or reduction, micelle synthesis, and hydrothermal synthesis. A major challenge still is protection against corrosion, and therefore suitable protection strategies will be emphasized, for example, surfactant/polymer coating, silica coating and carbon coating of magnetic nanoparticles or embedding them in a matrix/support. Properly protected magnetic nanoparticles can be used as building blocks for the fabrication of various functional systems, and their application in catalysis and biotechnology will be briefly reviewed. Finally, some future trends and perspectives in these research areas will be outlined.
Empower Sequence Labeling with Task-Aware Neural Language Model

Linguistic sequence labeling is a general approach encompassing a variety of problems, such as part-of-speech tagging and named entity recognition. Recent advances in neural networks (NNs) make it possible to build reliable models without handcrafted features. However, in many cases, it is hard to obtain sufficient annotations to train these models. In this study, we develop a neural framework to extract knowledge from raw texts and empower the sequence labeling task. Besides word-level knowledge contained in pretrained word embeddings, character-aware neural language models are incorporated to extract character-level knowledge. Transfer learning techniques are further adopted to mediate different components and guide the language model towards the key knowledge. Comparing to previous methods, these task-specific knowledge allows us to adopt a more concise model and conduct more efficient training. Different from most transfer learning methods, the proposed framework does not rely on any additional supervision. It extracts knowledge from self-contained order information of training sequences. Extensive experiments on benchmark datasets demonstrate the effectiveness of leveraging character-level knowledge and the efficiency of co-training. For example, on the CoNLL03 NER task, model training completes in about 6 hours on a single GPU, reaching F1 score of 91.71±0.10 without using any extra annotations.
A novel channel pruning method for deep neural network compression

In recent years, deep neural networks have achieved great success in the field of computer vision. However, it is still a big challenge to deploy these deep models on resource-constrained embedded devices such as mobile robots, smart phones and so on. Therefore, network compression for such platforms is a reasonable solution to reduce memory consumption and computation complexity. In this paper, a novel channel pruning method based on genetic algorithm is proposed to compress very deep Convolution Neural Networks (CNNs). Firstly, a pre-trained CNN model is pruned layer by layer according to the sensitivity of each layer. After that, the pruned model is fine-tuned based on knowledge distillation framework. These two improvements significantly decrease the model redundancy with less accuracy drop. Channel selection is a combinatorial optimization problem that has exponential solution space. In order to accelerate the selection process, the proposed method formulates it as a search problem, which can be solved efficiently by genetic algorithm. Meanwhile, a two-step approximation fitness function is designed to further improve the efficiency of genetic process. The proposed method has been verified on three benchmark datasets with two popular CNN models: VGGNet and ResNet. On the CIFAR-100 and ImageNet datasets, our approach outperforms several state-of-the-art methods. On the CIFAR-10 and SVHN datasets, the pruned VGGNet achieves better performance than the original model with 8× parameters compression and 3× FLOPs reduction.
Milkweed loss in agricultural fields because of herbicide use

1. The size of the Mexican overwintering population of monarch butterflies has decreased over the last decade. Approximately half of these butterflies come from the U.S. Midwest where larvae feed on common milkweed. There has been a large decline in milkweed in agricultural fields in the Midwest over the last decade. This loss is coincident with the increased use of glyphosate herbicide in conjunction with increased planting of genetically modified (GM) glyphosate-tolerant corn (maize) and soybeans (soya). 2. We investigate whether the decline in the size of the overwintering population can be attributed to a decline in monarch production owing to a loss of milkweeds in agricultural fields in the Midwest. We estimate Midwest annual monarch production using data on the number of monarch eggs per milkweed plant for milkweeds in different habitats, the density of milkweeds in different habitats, and the area occupied by those habitats on the landscape. 3. We estimate that there has been a 58% decline in milkweeds on the Midwest landscape and an 81% decline in monarch production in the Midwest from 1999 to 2010. Monarch production in the Midwest each year was positively correlated with the size of the subsequent overwintering population in Mexico. Taken together, these results strongly suggest that a loss of agricultural milkweeds is a major contributor to the decline in the monarch population. 4. The smaller monarch population size that has become the norm will make the species more vulnerable to other conservation threats.
Do Stock Market Investors Understand the Risk Sentiment of Corporate Annual Reports ? ∗

I test the stock market efficiency with respect to the information in the texts of annual reports. More specifically, I examine the implications of corporate annual reports’ risk sentiment for future earnings and stock returns. I measure the risk sentiment of annual reports by counting the frequency of words related to risk or uncertainty in the 10-K filings. I find that an increase in risk sentiment is associated with lower future earnings: Firms with a larger increase in risk sentiment have more negative earnings changes in the next year. Risk sentiment of annual reports can predict future returns in a cross-sectional setting: Firms with a large increase in risk sentiment experience significantly negative returns relative to those firms with little increase in risk sentiment in the twelve months after the annual report filing date. A hedge portfolio based on buying firms with a minor increase in risk sentiment of annual reports and shorting firms with a large increase in risk sentiment generates an annual Alpha of more than 10% measured using the four-factor model including the Fama-French three factors and the momentum factor.
Unsupervised Video Summarization with Adversarial LSTM Networks

This paper addresses the problem of unsupervised video summarization, formulated as selecting a sparse subset of video frames that optimally represent the input video. Our key idea is to learn a deep summarizer network to minimize distance between training videos and a distribution of their summarizations, in an unsupervised way. Such a summarizer can then be applied on a new video for estimating its optimal summarization. For learning, we specify a novel generative adversarial framework, consisting of the summarizer and discriminator. The summarizer is the autoencoder long short-term memory network (LSTM) aimed at, first, selecting video frames, and then decoding the obtained summarization for reconstructing the input video. The discriminator is another LSTM aimed at distinguishing between the original video and its reconstruction from the summarizer. The summarizer LSTM is cast as an adversary of the discriminator, i.e., trained so as to maximally confuse the discriminator. This learning is also regularized for sparsity. Evaluation on four benchmark datasets, consisting of videos showing diverse events in first-and third-person views, demonstrates our competitive performance in comparison to fully supervised state-of-the-art approaches.
Adding Semantic Annotation to the Penn TreeBank

This paper presents our basic approach to creating Proposition Bank, which involves adding a layer of semantic annotation to the Penn English TreeBank. Without attempting to confirm or disconfirm any particular semantic theory, our goal is to provide consistent argument labeling that will facilitate the automatic extraction of relational data. An argument such asthe window in John broke the window and in The window brokewould receive the same label in both sentences. In order to ensure reliable human annotation, we provide our annotators with explicit guidelines for labeling all of the syntactic and semantic frames of each particular verb. We give several examples of these guidelines and discuss the inter−annotator agreement figures. We also discuss our current experiments on the automatic expansion of our verb guidelines based on verb class membership. Our current rate of progress and our consistency of annotation demonstrate the feasibility of the task.
Improving Model Performance for Plant Image Classification With Filtered Noisy Images

The training of convolutional neural networks for image recognition usually requires large image datasets to produce favorable results. Those large datasets can be acquired by web crawlers that accumulate images based on keywords. Due to the nature of data in the web, these image sets display a broad variation of qualities across the contained items. In this work, a filtering approach for noisy datasets is proposed, utilizing a smaller trusted dataset. Hereby a convolutional neural network is trained on the trusted dataset and then used to construct a filtered subset from the noisy datasets. The methods described in this paper were applied to plant image classification and the created models have been submitted to the PlantCLEF 2017 competition.
Effect of neem leaf extract on freshwater fishes and zooplankton community.

Neem (Azadirachta indica) is a medicinal plant of containing diverse chemical active substances of several biological properties. So, the aim of the current investigation was to assess the effects of water leaf extract of neem plant on the survival and healthy status of Nile tilapia (Oreochromis niloticus), African cat fish (Clarias gariepinus) and zooplankton community. The laboratory determinations of lethal concentrations (LC 100 and LC50) through a static bioassay test were performed. The 24 h LC100 of neem leaf extract was estimated as 4 and 11 g/l, for juvenile's O. niloticus and C. gariepinus, respectively, while, the 96-h LC50 was 1.8 and 4 g/l, respectively. On the other hand, the 24 h LC100 for cladocera and copepoda were 0.25 and 0.45 g/l, respectively, while, the 96-h LC50 was 0.1 and 0.2 g/l, respectively. At the highest test concentrations, adverse effects were obvious with significant reductions in several cladoceran and copepod species. Some alterations in glucose levels, total protein, albumin, globulin as well as AST and ALT in plasma of treated O. niloticus and C. gariepinus with /2 and /10 LC50 of neem leaf water extract compared with non-treated one after 2 and 7 days of exposure were recorded and discussed. It could be concluded that the application of neem leaf extract can be used to control unwanted organisms in ponds as environment friendly material instead of deleterious pesticides. Also, extensive investigations should be established for the suitable methods of application in aquatic animal production facilities to be fully explored in future.
Formal aspects of model validity and validation in system dynamics

Model validation constitutes a very important step in system dynamics methodology. Yet, both published and informal evidence indicates that there has been little effort in system dynamics community explicitly devoted to model validity and validation. Validation is a prolonged and complicated process, involving both formal/quantitative tools and informal/ qualitative ones. This paper focuses on the formal aspects of validation and presents a taxonomy of various aspects and steps of formal model validation. First, there is a very brief discussion of the philosophical issues involved in model validation, followed by a flowchart that describes the logical sequence in which various validation activities must be carried out. The crucial nature of structure validity in system dynamics (causal-descriptive) models is emphasized. Then examples are given of specific validity tests used in each of the three major stages of model validation: Structural tests.
Telecommunication subscribers' churn prediction model using machine learning

During the last two decades, we have seen mobile communication becoming the dominant medium of communication. In numerous countries, especially the developed ones, the market is saturated to the extent that each new customer must be won over from the competitors. At the same time, public policies and standardization of mobile communication now allow customers to easily switch over from one carrier to another, resulting in a fluid market. Since the cost of winning a new customer is far greater than the cost of retaining an existing one, mobile carriers have now shifted their focus from customer acquisition to customer retention. As a result, churn prediction has emerged as the most crucial Business Intelligence (BI) application that aims at identifying customers who are about to transfer their business to a competitor i.e. to churn. This paper aims to present commonly used data mining techniques for the identification of customers who are about to churn. Based on historical data, these methods try to find patterns which can identify possible churners. Some of the well-known algorithms used during this research are Regression analysis, Decision Trees and Artificial Neural Networks (ANNs). The data set used in this study was obtained from Customer DNA website. It contains traffic data of 106,000 customers and their usage behavior for 3 months. We also discuss the use of re-sampling method in order to solve the problem of class imbalance. Our results show that in case of the data set used, decision trees is the most accurate classifier algorithm while identifying potential churners.
Efficient Capital Markets : II

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Both aerobic endurance and strength training programmes improve cardiovascular health in obese adults.

Regular exercise training is recognized as a powerful tool to improve work capacity, endothelial function and the cardiovascular risk profile in obesity, but it is unknown which of high-intensity aerobic exercise, moderate-intensity aerobic exercise or strength training is the optimal mode of exercise. In the present study, a total of 40 subjects were randomized to high-intensity interval aerobic training, continuous moderate-intensity aerobic training or maximal strength training programmes for 12 weeks, three times/week. The high-intensity group performed aerobic interval walking/running at 85-95% of maximal heart rate, whereas the moderate-intensity group exercised continuously at 60-70% of maximal heart rate; protocols were isocaloric. The strength training group performed 'high-intensity' leg press, abdominal and back strength training. Maximal oxygen uptake and endothelial function improved in all groups; the greatest improvement was observed after high-intensity training, and an equal improvement was observed after moderate-intensity aerobic training and strength training. High-intensity aerobic training and strength training were associated with increased PGC-1alpha (peroxisome-proliferator-activated receptor gamma co-activator 1alpha) levels and improved Ca(2+) transport in the skeletal muscle, whereas only strength training improved antioxidant status. Both strength training and moderate-intensity aerobic training decreased oxidized LDL (low-density lipoprotein) levels. Only aerobic training decreased body weight and diastolic blood pressure. In conclusion, high-intensity aerobic interval training was better than moderate-intensity aerobic training in improving aerobic work capacity and endothelial function. An important contribution towards improved aerobic work capacity, endothelial function and cardiovascular health originates from strength training, which may serve as a substitute when whole-body aerobic exercise is contra-indicated or difficult to perform.
Deep Convolutional Neural Networks for Large-scale Speech Tasks

Convolutional Neural Networks (CNNs) are an alternative type of neural network that can be used to reduce spectral variations and model spectral correlations which exist in signals. Since speech signals exhibit both of these properties, we hypothesize that CNNs are a more effective model for speech compared to Deep Neural Networks (DNNs). In this paper, we explore applying CNNs to large vocabulary continuous speech recognition (LVCSR) tasks. First, we determine the appropriate architecture to make CNNs effective compared to DNNs for LVCSR tasks. Specifically, we focus on how many convolutional layers are needed, what is an appropriate number of hidden units, what is the best pooling strategy. Second, investigate how to incorporate speaker-adapted features, which cannot directly be modeled by CNNs as they do not obey locality in frequency, into the CNN framework. Third, given the importance of sequence training for speech tasks, we introduce a strategy to use ReLU+dropout during Hessian-free sequence training of CNNs. Experiments on 3 LVCSR tasks indicate that a CNN with the proposed speaker-adapted and ReLU+dropout ideas allow for a 12%-14% relative improvement in WER over a strong DNN system, achieving state-of-the art results in these 3 tasks.
Graph Models for Knowledge Representation and Reasoning for Contemporary and Emerging Needs – A survey

Reasoning is the fundamental capability which requires knowledge. Various graph models have proven to be very valuable in knowledge representation and reasoning. Recently, explosive data generation and accumulation capabilities have paved way for Big Data and Data Intensive Systems. Knowledge Representation and Reasoning with large and growing data is extremely challenging but crucial for businesses to predict trends and support decision making. Any contemporary, reasonably complex knowledge based system will have to consider this onslaught of data, to use appropriate and sufficient reasoning for semantic processing of information by machines. This paper surveys graph based knowledge representation and reasoning, various graph models such as Conceptual Graphs, Concept Graphs, Semantic Networks, Inference Graphs and Causal Bayesian Networks used for representation and reasoning, common and recent research uses of these graph models, typically in Big Data environment, and the near future needs and challenges for graph based KRR in computing systems. Observations are presented in a table, highlighting suitability of the surveyed graph models for contemporary scenarios.