nils-herrmann/clean_scirepeval huggingface dataset:
Financial Cryptography in 7 Layers

Financial Cryptography is substantially complex, requiring skills drawn from diverse and incompatible, or at least, unfriendly, disciplines. Caught between Central Banking and Cryptography, or between accountants and programmers, there is a grave danger that efforts to construct Financial Cryptography systems will simplify or omit critical disciplines. This paper presents a model that seeks to encompass the breadth of Financial Cryptography (at the clear expense of the depth of each area). By placing each discipline into a seven layer model of introductory nature, where the relationship between each adjacent layer is clear, this model should assist project, managerial and requirements people. Whilst this model is presented as efficacious, there are limits to any model. This one does not propose a methodology for design, nor a checklist for protocols. Further, given the young heritage of the model, and of the field itself, it should be taken as a hint of complexity rather than a defining guide.
Exploring the Spatial Hierarchy of Mixture Models for Human Pose Estimation

Human pose estimation requires a versatile yet well-constrained spatial model for grouping locally ambiguous parts together to produce a globally consistent hypothesis. Previous works either use local deformable models deviating from a certain template, or use a global mixture representation in the pose space. In this paper, we propose a new hierarchical spatial model that can capture an exponential number of poses with a compact mixture representation on each part. Using latent nodes, it can represent high-order spatial relationship among parts with exact inference. Different from recent hierarchical models that associate each latent node to a mixture of appearance templates (like HoG), we use the hierarchical structure as a pure spatial prior avoiding the large and often confounding appearance space. We verify the effectiveness of this model in three ways. First, samples representing human-like poses can be drawn from our model, showing its ability to capture high-order dependencies of parts. Second, our model achieves accurate reconstruction of unseen poses compared to a nearest neighbor pose representation. Finally, our model achieves state-of-art performance on three challenging datasets, and substantially outperforms recent hierarchical models.
Beyond TFIDF Weighting for Text Categorization in the Vector Space Model

KNN and SVM are two machine learning approaches to Text Categorization (TC) based on the Vector Space Model. In this model, borrowed from Information Retrieval, documents are represented as a vector where each component is associated with a particular word from the vocabulary. Traditionally, each component value is assigned using the information retrieval TFIDF measure. While this weighting method seems very appropriate for IR, it is not clear that it is the best choice for TC problems. Actually, this weighting method does not leverage the information implicitly contained in the categorization task to represent documents. In this paper, we introduce a new weighting method based on statistical estimation of the importance of a word for a specific categorization problem. This method also has the benefit to make feature selection implicit, since useless features for the categorization problem considered get a very small weight. Extensive experiments reported in the paper shows that this new weighting method improves significantly the classification accuracy as measured on many categorization tasks.
E-government adoption in public administration organizations: integrating institutional theory perspective and resource-based view

27 June 2010 Revised: 25 April 2011 2nd  5 April 2012  We develop and test a theoretical model to investigate the adoption of government-to-government (G2G) information systems in public administration organizations. Specifically, this model explains how top management commitment (TMC) mediates the impact of external institutional pressures on internal organizational resource allocation, which finally leads to the adoption decision. The hypotheses were tested using survey data from public administration organizations in China. Results from partial least squares analyses suggest that coercive and normative pressures positively affect TMC, which then positively affects financial and information technology (IT) human resources in the G2G adoption process. In turn, financial and IT human resources are confirmed to positively affect the intention to adopt G2G. Surprisingly, we do not find support for our hypothesis that mimetic pressures directly influence TMC. Rather, a post hoc analysis implies that mimetic pressures indirectly influence TMC via the influence of coercive pressures. Our findings provide important managerial implications for public administration organizations.  (2013) 22, 221–234. doi:10.1057/ejis.2012.28; published online 19 June 2012
An efficient privacy mechanism for electronic health records

Electronic health records (EHRs), digitization of patients’ health record, offer many advantages over traditional ways of keeping patients’ records, such as easing data management and facilitating quick access and real-time treatment. EHRs are a rich source of information for research (e.g. in data analytics), but there is a risk that the published data (or its leakage) can compromise patient privacy. The k-anonymity model is a widely used privacy model to study privacy breaches, but this model only studies privacy against identity disclosure. Other extensions to mitigate existing limitations in k-anonymity model include p-sensitive k-anonymity model, p+-sensitive k-anonymity model, and (p, α)-sensitive k-anonymity model. In this paper, we point out that these existing models are inadequate in preserving the privacy of end users. Specifically, we identify situations where p+sensitive k-anonymity model is unable to preserve the privacy of individuals when an adversary can identify similarities among the categories of sensitive values. We term such attack as Categorical Similarity Attack (CSA). Thus, we propose a balanced p+-sensitive k-anonymity model, as an extension of the p+-sensitive k-anonymity model. We then formally analyze the proposed model using High-Level Petri Nets (HLPN) and verify its properties using SMT-lib and Z3 solver.We then evaluate the utility of release data using standard metrics and show that our model outperforms its counterparts in terms of privacy vs. utility tradeoff.
Making recombinant proteins in filamentous fungi- are we expecting too much?

Hosts used for the production of recombinant proteins are typically high-protein secreting mutant strains that have been selected for a specific purpose, such as efficient production of cellulose-degrading enzymes. Somewhat surprisingly, sequencing of the genomes of a series of mutant strains of the cellulolytic Trichoderma reesei, widely used as an expression host for recombinant gene products, has shed very little light on the nature of changes that boost high-level protein secretion. While it is generally agreed and shown that protein secretion in filamentous fungi occurs mainly through the hyphal tip, there is growing evidence that secretion of proteins also takes place in sub-apical regions. Attempts to increase correct folding and thereby the yields of heterologous proteins in fungal hosts by co-expression of cellular chaperones and foldases have resulted in variable success; underlying reasons have been explored mainly at the transcriptional level. The observed physiological changes in fungal strains experiencing increasing stress through protein overexpression under strong gene promoters also reflect the challenge the host organisms are experiencing. It is evident, that as with other eukaryotes, fungal endoplasmic reticulum is a highly dynamic structure. Considering the above, there is an emerging body of work exploring the use of weaker expression promoters to avoid undue stress. Filamentous fungi have been hailed as candidates for the production of pharmaceutically relevant proteins for therapeutic use. One of the biggest challenges in terms of fungally produced heterologous gene products is their mode of glycosylation; fungi lack the functionally important terminal sialylation of the glycans that occurs in mammalian cells. Finally, exploration of the metabolic pathways and fluxes together with the development of sophisticated fermentation protocols may result in new strategies to produce recombinant proteins in filamentous fungi.
Deep Learning of Representations for Unsupervised and Transfer Learning

Deep learning algorithms seek to exploit the unknown structure in the input distribution in order to discover good representations, often at multiple levels, with higher-level learned features defined in terms of lower-level features. The objective is to make these higherlevel representations more abstract, with their individual features more invariant to most of the variations that are typically present in the training distribution, while collectively preserving as much as possible of the information in the input. Ideally, we would like these representations to disentangle the unknown factors of variation that underlie the training distribution. Such unsupervised learning of representations can be exploited usefully under the hypothesis that the input distribution P (x) is structurally related to some task of interest, say predicting P (y|x). This paper focusses on why unsupervised pre-training of representations can be useful, and how it can be exploited in the transfer learning scenario, where we care about predictions on examples that are not from the same distribution as the training distribution.
Distributed Kalman Filter with Embedded Consensus Filters

The problem of distributed Kalman filtering (DKF) for sensor networks is one of the most fundamental distributed estimation problems for scalable sensor fusion. This paper addresses the DKF problem by reducing it to two separate dynamic consensus problems in terms of weighted measurements and inverse-covariance matrices. These to data fusion problems are solved is a distributed way using low-pass and band-pass consensus filters. Consensus filters are distributed algorithms that allow calculation of average-consensus of time-varying signals. The stability properties of consensus filters is discussed in a companion CDC ’05 paper [24]. We show that a central Kalman filter for sensor networks can be decomposed into n micro-Kalman filters with inputs that are provided by two types of consensus filters. This network of micro-Kalman filters collectively are capable to provide an estimate of the state of the process (under observation) that is identical to the estimate obtained by a central Kalman filter given that all nodes agree on two central sums. Later, we demonstrate that our consensus filters can approximate these sums and that gives an approximate distributed Kalman filtering algorithm. A detailed account of the computational and communication architecture of the algorithm is provided. Simulation results are presented for a sensor network with 200 nodes and more than 1000 links.
An update on the mechanisms of the psychostimulant effects of caffeine.

There has been a long debate about the predominant involvement of the different adenosine receptor subtypes and the preferential role of pre- versus post-synaptic mechanisms in the psychostimulant effects of the adenosine receptor antagonist caffeine. Both striatal A(1) and A(2A) receptors are involved in the motor-activating and probably reinforcing effects of caffeine, although they play a different role under conditions of acute or chronic caffeine administration. The present review emphasizes the key integrative role of adenosine and adenosine receptor heteromers in the computation of information at the level of the striatal spine module (SSM). This local module is mostly represented by the dendritic spine of the medium spiny neuron with its glutamatergic and dopaminergic synapses and astroglial processes that wrap the glutamatergic synapse. In the SSM, adenosine acts both pre- and post-synaptically through multiple mechanisms, which depend on heteromerization of A(1) and A(2A) receptors among themselves and with D(1) and D(2) receptors, respectively. A critical aspect of the mechanisms of the psychostimulant effects of caffeine is its ability to release the pre- and post-synaptic brakes that adenosine imposes on dopaminergic neurotransmission by acting on different adenosine receptor heteromers localized in different elements of the SSM.
DL 4 MD : A Deep Learning Framework for Intelligent Malware Detection

In the Internet-age, malware poses a serious and evolving threat to security, making the detection of malware of utmost concern. Many research efforts have been conducted on intelligent malware detection by applying data mining and machine learning techniques. Though great results have been obtained with these methods, most of them are built on shallow learning architectures, which are still somewhat unsatisfying for malware detection problems. In this paper, based on the Windows Application Programming Interface (API) calls extracted from the Portable Executable (PE) files, we study how a deep learning architecture using the stacked AutoEncoders (SAEs) model can be designed for intelligent malware detection. The SAEs model performs as a greedy layerwise training operation for unsupervised feature learning, followed by supervised parameter fine-tuning (e.g., weights and offset vectors). To the best of our knowledge, this is the first work that deep learning using the SAEs model based on Windows API calls is investigated in malware detection for real industrial application. A comprehensive experimental study on a real and large sample collection from Comodo Cloud Security Center is performed to compare various malware detection approaches. Promising experimental results demonstrate that our proposed method can further improve the overall performance in malware detection compared with traditional shallow learning methods.
Behavioral and physiological consequences of sleep restriction.

Adequate sleep is essential for general healthy functioning. This paper reviews recent research on the effects of chronic sleep restriction on neurobehavioral and physiological functioning and discusses implications for health and lifestyle. Restricting sleep below an individual's optimal time in bed (TIB) can cause a range of neurobehavioral deficits, including lapses of attention, slowed working memory, reduced cognitive throughput, depressed mood, and perseveration of thought. Neurobehavioral deficits accumulate across days of partial sleep loss to levels equivalent to those found after 1 to 3 nights of total sleep loss. Recent experiments reveal that following days of chronic restriction of sleep duration below 7 hours per night, significant daytime cognitive dysfunction accumulates to levels comparable to that found after severe acute total sleep deprivation. Additionally, individual variability in neurobehavioral responses to sleep restriction appears to be stable, suggesting a trait-like (possibly genetic) differential vulnerability or compensatory changes in the neurobiological systems involved in cognition. A causal role for reduced sleep duration in adverse health outcomes remains unclear, but laboratory studies of healthy adults subjected to sleep restriction have found adverse effects on endocrine functions, metabolic and inflammatory responses, suggesting that sleep restriction produces physiological consequences that may be unhealthy.
Imitation Learning for Code Generation via Recurrent State Space Embeddings

In their 1988 paper, Rich and Waters define the Programmer’s Apprentice as an “intelligent computer program that functions like a human support team,” providing guidance in requirements, design, and implementation of programs [21]. The Programmer’s Apprentice is differentiated from a simple code auto-completion agent in its ability to understand software design considerations and engage in helpful dialogue with the software engineer [23]. As we considered the problem of the programmer’s apprentice, we came to agree that interfaces are among the most important structural elements of code. In fact, in considering our own coding habits, we realized the most helpful assistant would be one that can comprehend design through the structure of an interface and assist in the completion of the code through a divide-and-conquer approach. In this work, we focus solely on the goal of implementation, proposing a novel framework for code generation that, given a sketch of helper function interfaces, suggests both use of primitive language operations and structuring of programs through decomposition.
Recurrent Reinforcement Learning: A Hybrid Approach

Successful applications of reinforcement learning in realworld problems often require dealing with partially observable states. It is in general very challenging to construct and infer hidden states as they often depend on the agent’s entire interaction history and may require substantial domain knowledge. In this work, we investigate a deep-learning approach to learning the representation of states in partially observable tasks, with minimal prior knowledge of the domain. In particular, we study reinforcement learning with deep neural networks, including RNN and LSTM, which are equipped with the desired property of being able to capture long-term dependency on history, and thus providing an effective way of learning the representation of hidden states. We further develop a hybrid approach that combines the strength of both supervised learning (for representing hidden states) and reinforcement learning (for optimizing control) with joint training. Extensive experiments based on a KDD Cup 1998 direct mailing campaign problem demonstrate the effectiveness and advantages of the proposed approach, which performs the best across the board.
Disarming jealousy in couples relationships: a multidimensional approach.

Jealousy is a powerful emotional force in couples' relationships. In just seconds it can turn love into rage and tenderness into acts of control, intimidation, and even suicide or murder. Yet it has been surprisingly neglected in the couples therapy field. In this paper we define jealousy broadly as a hub of contradictory feelings, thoughts, beliefs, actions, and reactions, and consider how it can range from a normative predicament to extreme obsessive manifestations. We ground jealousy in couples' basic relational tasks and utilize the construct of the vulnerability cycle to describe processes of derailment. We offer guidelines on how to contain the couple's escalation, disarm their ineffective strategies and power struggles, identify underlying vulnerabilities and yearnings, and distinguish meanings that belong to the present from those that belong to the past, or to other contexts. The goal is to facilitate relational and personal changes that can yield a better fit between the partners' expectations.
Chapter 1 Fish Cytokines and Immune Response

The immune system can be defined as a complex system that protects the organism against organisms or substances that might cause infection or disease. One of the most fascinating characteristics of the immune system is its capability to recognize and respond to pathogens with significant specificity. Innate and adaptive immune responses are able to recognize for‐ eign structures and trigger different molecular and cellular mechanisms for antigen elimina‐ tion. The immune response is critical to all individuals; therefore numerous changes have taken place during evolution to generate variability and specialization, although the im‐ mune system has conserved some important features over millions of years of evolution that are common for all species. The emergence of new taxonomic categories coincided with the diversification of the immune response. Most notably, the emergence of vertebrates coincid‐ ed with the development of a novel type of immune response. Apparently, vertebrates in‐ herited innate immunity from their invertebrate ancestors .
The Automatic Creation of Literature Abstracts

Excerpts of technical papers and magazine articles that serve the purposes of conventional abstracts have been created entirely by automatic means. In the exploratory research described, the complete text of an article in machine-readable form i s scanned by a n IBM 704 data-processing machine and analyzed in accordance with a standard program. Statistical information derived from word frequency and distribution is used by the machine to compute a relative measure of significance, first for individual words and then for sentences. Sentences scoring highest in significance are extracted and printed out to become the " auto-abstract. "
The impact of industrial wearable system on industry 4.0

Although industrial automation and robots develop rapidly in the era of “Industry 4.0”, the increasing integration of manufacturing processes and the strengthening of the autonomous capabilities of manufacturing systems make investigating the role of humans a primary research objective. This study is among the first to examine the impact of industrial wearable system in industry 4.0. Industrial Wearable System (IWS) is defined as human empowering technology that fit the operators' cognitive and physical needs, while improving human physical-, sensing- and cognitive-capabilities using new generation of industrial infromatics. Three research objectives are as examined, including (1) to illustrate the specific manufacturing and logistics application scenarios enabled by IWS; (2) to construct human-centric IWS technical framework for industry 4.0; and (3) to verify the presented framework and technology via a real-life case study.
Dating couples' attachment styles and patterns of cortisol reactivity and recovery in response to a relationship conflict.

This study investigated theoretically predicted links between attachment style and a physiological indicator of stress, salivary cortisol levels, in 124 heterosexual dating couples. Cortisol was assessed at 7 points before and after an experimental conflict negotiation task, creating a trajectory of stress reactivity and recovery for each participant. Growth modeling of cortisol data tested hypotheses that (a) insecurely attached individuals show patterns of greater physiological stress reactions to interpersonal conflict than do securely attached individuals and (b) people with insecurely attached partners show patterns of greater stress in reaction to relationship conflict than those with securely attached partners. Hypothesis 1 was supported, but men and women differed in the type of insecure attachment that predicted stress trajectories. Hypothesis 2 was supported for men, but not for women. The discussion emphasizes the role of gender role norms and partner characteristics in understanding connections between adult attachment and patterns of cortisol responses to interpersonal stress.