nils-herrmann/clean_scirepeval huggingface dataset:
An overview of cardiovascular risk factor burden in sub-Saharan African countries: a socio-cultural perspective

Sub-Saharan African (SSA) countries are currently experiencing one of the most rapid epidemiological transitions characterized by increasing urbanization and changing lifestyle factors. This has resulted in an increase in the incidence of non-communicable diseases, especially cardiovascular disease (CVD). This double burden of communicable and chronic non-communicable diseases has long-term public health impact as it undermines healthcare systems.



The purpose of this paper is to explore the socio-cultural context of CVD risk prevention and treatment in sub-Saharan Africa. We discuss risk factors specific to the SSA context, including poverty, urbanization, developing healthcare systems, traditional healing, lifestyle and socio-cultural factors.



We conducted a search on African Journals On-Line, Medline, PubMed, and PsycINFO databases using combinations of the key country/geographic terms, disease and risk factor specific terms such as "diabetes and Congo" and "hypertension and Nigeria". Research articles on clinical trials were excluded from this overview. Contrarily, articles that reported prevalence and incidence data on CVD risk and/or articles that report on CVD risk-related beliefs and behaviors were included. Both qualitative and quantitative articles were included.



The epidemic of CVD in SSA is driven by multiple factors working collectively. Lifestyle factors such as diet, exercise and smoking contribute to the increasing rates of CVD in SSA. Some lifestyle factors are considered gendered in that some are salient for women and others for men. For instance, obesity is a predominant risk factor for women compared to men, but smoking still remains mostly a risk factor for men. Additionally, structural and system level issues such as lack of infrastructure for healthcare, urbanization, poverty and lack of government programs also drive this epidemic and hampers proper prevention, surveillance and treatment efforts.



Using an African-centered cultural framework, the PEN3 model, we explore future directions and efforts to address the epidemic of CVD risk in SSA.
CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure

Clustering of individuals into populations on the basis of multilocus genotypes is informative in a variety of settings. In population-genetic clustering algorithms, such as BAPS, STRUCTURE and TESS, individual multilocus genotypes are partitioned over a set of clusters, often using unsupervised approaches that involve stochastic simulation. As a result, replicate cluster analyses of the same data may produce several distinct solutions for estimated cluster membership coefficients, even though the same initial conditions were used. Major differences among clustering solutions have two main sources: (1) 'label switching' of clusters across replicates, caused by the arbitrary way in which clusters in an unsupervised analysis are labeled, and (2) 'genuine multimodality,' truly distinct solutions across replicates.



To facilitate the interpretation of population-genetic clustering results, we describe three algorithms for aligning multiple replicate analyses of the same data set. We have implemented these algorithms in the computer program CLUMPP (CLUster Matching and Permutation Program). We illustrate the use of CLUMPP by aligning the cluster membership coefficients from 100 replicate cluster analyses of 600 chickens from 20 different breeds.


AVAILABILITY
CLUMPP is freely available at http://rosenberglab.bioinformatics.med.umich.edu/clumpp.html.
Generative Adversarial Autoencoder Networks

We introduce an effective model to overcome the problem of mode collapse when training Generative Adversarial Networks (GAN). Firstly, we propose a new generator objective that finds it better to tackle mode collapse. And, we apply an independent Autoencoders (AE) to constrain the generator and consider its reconstructed samples as “real” samples to slow down the convergence of discriminator that enables to reduce the gradient vanishing problem and stabilize the model. Secondly, from mappings between latent and data spaces provided by AE, we further regularize AE by the relative distance between the latent and data samples to explicitly prevent the generator falling into mode collapse setting. This idea comes when we find a new way to visualize the mode collapse on MNIST dataset. To the best of our knowledge, our method is the first to propose and apply successfully the relative distance of latent and data samples for stabilizing GAN. Thirdly, our proposed model, namely Generative Adversarial Autoencoder Networks (GAAN), is stable and has suffered from neither gradient vanishing nor mode collapse issues, as empirically demonstrated on synthetic, MNIST, MNIST-1K, CelebA and CIFAR-10 datasets. Experimental results show that our method can approximate well multi-modal distribution and achieve better results than state-of-the-art methods on these benchmark datasets. Our model implementation is published here.
Abusive Language Detection in Online User Content

Detection of abusive language in user generated online content has become an issue of increasing importance in recent years. Most current commercial methods make use of blacklists and regular expressions, however these measures fall short when contending with more subtle, less ham-fisted examples of hate speech. In this work, we develop a machine learning based method to detect hate speech on online user comments from two domains which outperforms a state-ofthe-art deep learning approach. We also develop a corpus of user comments annotated for abusive language, the first of its kind. Finally, we use our detection tool to analyze abusive language over time and in different settings to further enhance our knowledge of this behavior.
Everybody Dance Now

This paper presents a simple method for “do as I do" motion transfer: given a source video of a person dancing we can transfer that performance to a novel (amateur) target after only a few minutes of the target subject performing standard moves. We pose this problem as a per-frame image-to-image translation with spatio-temporal smoothing. Using pose detections as an intermediate representation between source and target, we learn a mapping from pose images to a target subject’s appearance. We adapt this setup for temporally coherent video generation including realistic face synthesis. Our video demo can be found at https://youtu.be/PCBTZh41Ris.
Online Learning for Effort Reduction in Interactive Neural Machine Translation

Neural machine translation systems require large amounts of training data and resources. Even with this, the quality of the translations may be insufficient for some users or domains. In such cases, the output of the system must be revised by a human agent. This can be done in a post-editing stage or following an interactive machine translation protocol. We explore the incremental update of neural machine translation systems during the post-editing or interactive translation processes. Such modifications aim to incorporate the new knowledge, from the edited sentences, into the translation system. Updates to the model are performed on-the-fly, as sentences are corrected, via online learning techniques. In addition, we implement a novel interactive, adaptive system, able to react to single-character interactions. This system greatly reduces the human effort required for obtaining high-quality translations. In order to stress our proposals, we conduct exhaustive experiments varying the amount and type of data available for training. Results show that online learning effectively achieves the objective of reducing the human effort required during the post-editing or the interactive machine translation stages. Moreover, these adaptive systems also perform well in scenarios with scarce resources. We show that a neural machine translation system can be rapidly adapted to a specific domain, exclusively by means of online learning techniques.
Biometrics in banking security: a case study

To identify and discuss the issues and success factors surrounding biometrics, especially in the context of user authentication and controls in the banking sector, using a case study.  The literature survey and analysis of the security models of the present information systems and biometric technologies in the banking sector provide the theoretical and practical background for this work. The impact of adopting biometric solutions in banks was analysed by considering the various issues and challenges from technological, managerial, social and ethical angles. These explorations led to identifying the success factors that serve as possible guidelines for a viable implementation of a biometric enabled authentication system in banking organisations, in particular for a major bank in New Zealand.  As the level of security breaches and transaction frauds increase day by day, the need for highly secure identification and personal verification information systems is becoming extremely important especially in the banking and finance sector. Biometric technology appeals to many banking organisations as a near perfect solution to such security threats. Though biometric technology has gained traction in areas like healthcare and criminology, its application in banking security is still in its infancy. Due to the close association of biometrics to human, physical and behavioural aspects, such technologies pose a multitude of social, ethical and managerial challenges. The key success factors proposed through the case study served as a guideline for a biometric enabled security project called Bio Sec, which is envisaged in a large banking organisation in New Zealand. This pilot study reveals that more than coping with the technology issues of gelling biometrics into the existing information systems, formulating a viable security plan that addresses user privacy fears, human tolerance levels, organisational change and legal issues is of prime importance.  Though biometric systems are successfully adopted in areas such as immigration control and criminology, there is a paucity of their implementation and research pertaining to banking environments. Not all banks venture into biometric solutions to enhance their security systems due to their socio technological issues. This paper fulfils the need for a guideline to identify the various issues and success factors for a viable biometric implementation in a bank’s access control system. This work is only a starting point for academics to conduct more research in the application of biometrics in the various facets of banking businesses.
Factors Affecting the Adoption of ICT in the Hospitality Industry in Imo State

The impact of ICT (Information Communication Technology) hotel and hospitality industries has been widely recognized as one of the major changes in the last decade: new ways of communicating with guests, using ICT to improve services delivery to guest etc. The study tried to investigate the ICT Infrastructural Diffusion in hotels in Owerri, Imo State. In order to know the extent of spread, the study examine the current ICT infrastructures being used, the rate at which its being used and the factors affecting its adoption. The data collected was analyzed using SPSS Software and Regression model was estimated. The findings revealed that the rate at which hotels adopt and use ICT infrastructure is low and the most significant factor affecting the adoption and use of ICT is scope of activities the hotel is engaged in. It is therefore recommended that Government should increase the economic activities in the state so as to increase the adoption of ICT infrastructures.
Mathematical modeling and simulation of selected components on-board autonomous power supply system (ASE), in accordance with the concept of a more electric aircraft (MEA)

The paper presents mathematical models of the key components onboard autonomous power supply system (ASE) plane "conventional" and more electric aircraft MEA "More Electric Aircraft". The subject, and also the aim of this work is the analysis and simulation of selected components of the power system of modern aircraft (synchronous generator, integrated starter/generator, voltage regulator, as device control and regulation of the generator and power electronic rectifiers multi-pulse). Simulation tests of key components confirmed the results obtained by the analytical and based on their mathematical models. The final part of the paper presents the main conclusions of the analysis and simulation based on the mathematical models of the selected components of the ASE system in accordance with the concept of a more electric aircraft.
ATIPS: Automatic Travel Itinerary Planning System for Domestic Areas

Leisure travel has become a topic of great interest to Taiwanese residents in recent years. Most residents expect to be able to relax on a vacation during the holidays; however, the complicated procedure of travel itinerary planning is often discouraging and leads them to abandon the idea of traveling. In this paper, we design an automatic travel itinerary planning system for the domestic area (ATIPS) using an algorithm to automatically plan a domestic travel itinerary based on user intentions that allows users to minimize the process of trip planning. Simply by entering the travel time, the departure point, and the destination location, the system can automatically generate a travel itinerary. According to the results of the experiments, 70% of users were satisfied with the result of our system, and 82% of users were satisfied with the automatic user preference learning mechanism of ATIPS. Our algorithm also provides a framework for substituting modules or weights and offers a new method for travel planning.
Impact and Sustainability of E-Government Services in Developing Countries: Lessons Learned from Tamil Nadu, India

We find that the presence of village Internet facilities, offering government to citizen services, is positively associated with the rate at which the villagers obtain some of these services. In a study of a rural Internet project in India, we identify a positive correlation for two such Internet services: obtaining birth certificates for children and applications for old age pensions. Both these government services are of considerable social and economic value to the citizens. Villagers report that the Internet based services saved them time, money, and effort compared with obtaining the services directly from the government office. We also find that these services can reduce corruption in the delivery of these services. After over one year of successful operation, however, the e-government program was not able to maintain the necessary level of local political and administrative support to remain institutionally viable. As government officers shifted from the region, or grew to find the program a threat, the e-government services faltered. We argue that this failure was due to a variety of Critical Failure Factors. We end with a simple sustainability failure model. In summary, we propose that the e-government program failed to be politically and institutionally sustainable due to people, management, cultural, and structural factors.
On smartphone camera based fingerphoto authentication

Authenticating fingerphoto images captured using a smartphone camera, provide a good alternate solution in place of traditional pin or pattern based approaches. There are multiple challenges associated with fingerphoto authentication such as background variations, environmental illumination, estimating finger position, and camera resolution. In this research, we propose a novel ScatNet feature based fingerphoto matching approach. Effective fingerphoto segmentation and enhancement are performed to aid the matching process and to attenuate the effect of capture variations. Further, we propose and create a publicly available smartphone fingerphoto database having three different subsets addressing the challenges of environmental illumination and background, along with their corresponding live scan fingerprints. Experimental results show improved performance across multiple challenges present in the database.
The fifth dialog state tracking challenge

Dialog state tracking - the process of updating the dialog state after each interaction with the user - is a key component of most dialog systems. Following a similar scheme to the fourth dialog state tracking challenge, this edition again focused on human-human dialogs, but introduced the task of cross-lingual adaptation of trackers. The challenge received a total of 32 entries from 9 research groups. In addition, several pilot track evaluations were also proposed receiving a total of 16 entries from 4 groups. In both cases, the results show that most of the groups were able to outperform the provided baselines for each task.
Rover Visual Obstacle Avoidance

The Stanford AI Lab cart is a remotely controlled TV equipped mobile robot. A computer program has driven the cart through cluttered spaces, gaining its knowledge of the world entirely from images broadcast by the onboard TV system. The cart uses several kinds of stereo to locate objects around it in 3D and to deduce its own motion. It plans an obstacle avoiding path to a desired destination on the basis of a model built with this information. The plan changes as the cart perceives new obstacles on its journey. The system is reliable for short runs, but slow. The cart moves one meter every ten to fifteen minutes, in lurches. After rolling a meter it stops, takes some pictures and thinks about them for a long time. Then it plans a new path, executes a little of it. and pauses again. It has successfully driven the cart through several 20 meter courses (each taking about five hours) complex enough to necessitate three or four avoiding swerves. Some weaknesses and possible improvements were suggested by these and other, less successful, runs.
THE EFFECT OF ADDITIVE AND DRY MATTER CONTENT ON SILAGE PROTEIN DEGRADABILITY AND BIOGENIC AMINE CONTENT

Legumes are widely used ensiling material as they are rich in protein. Ensiling of legumes arises several problems due to their low content of sugars, high buffering capacity and high moisture content. Attention should be paid to silage protein degradability. Objective of the study was to explain the effect of additives and wilting on the fermentation quality and nutritive value of red clover-timothy silage, protein degradability and content of amines included. Test silages from fresh material, either unwilted or wilted for 24 hours, were conserved into 3-litre jars and opened for analysis in 90 days. Biological (L. plantarum +L. fermentum) and chemical (AIV 2000) additives were used for treatment. Silage protein degradability was studied by using in sacco method with ruminally fistulated cows. As the buffering capacity of red clover-timothy mixture (50:50) is low (27.5), increasing up to 39.6 at wilting, treatment with additive is necessary to improve fermentation. The use of biological or chemical additives decreased silage dry matter losses by 1.9 to 3.7 times, significantly improving the quality of fermentation – content of butyric acid was 47 g/kg DM for test silage and 1–2 g/kg DM or 0 for silage without additive. In vitro organic matter digestibility for the silage with chemical additive increased by 4%, compared to that for the test silage (P<0.0001). Ruminal degradability of silage nitrogen was approximately 90%. Protein solubility and ruminal degradability were lower for silage with chemical additive. Ruminal degradability of silage protein after 8 h was 77.2% for the test silage, 76% for the silage with biological additive and 68% for the silage with chemical additive. All biogenic amines under investigation were present in low dry matter (140g/kg) silages, prepared without an additive. The content of histamine was the highest (5.24 g/kg DM), followed by putrescine (0.86 g/kg DM). Wilting and treatment with additive significantly decreased the formation of biogenic amines in silages.
Host-based Web Anomaly Intrusion Detection System , an Artificial Immune System Approach

Line labelling has been used to determine whether a two-dimensional (2D) line drawing object is a possible or impossible representation of a three-dimensional (3D) solid object. However, the results are not sufficiently robust because the existing line labelling methods do not have any validation method to verify their own result. In this research paper, the concept of graph colouring is applied to a validation technique for a labelled 2D line drawing. As a result, a graph colouring algorithm for validating labelled 2D line drawings is presented. A high-level programming language, MATLAB R2009a, and two primitive 2D line drawing classes, prism and pyramid are used to show how the algorithms can be implemented. The proposed algorithm also shows that the minimum number of colours needed to colour the labelled 2D line drawing object is equal to 3 for prisms and 1 n − for pyramids, where n is the number of vertices (junctions) in the pyramid objects.
Cost-Based Intuitionist Probabilities on Spaces of Graphs, Hypergraphs and Theorems

A novel partial order is defined on the space of digraphs or hypergraphs, based on assessing the cost of producing a graph via a sequence of elementary transformations. Leveraging work by Knuth and Skilling on the foundations of inference, and the structure of Heyting algebras on graph space, this partial order is used to construct an intuitionistic probability measure that applies to either digraphs or hypergraphs. As logical inference steps can be represented as transformations on hypergraphs representing logical statements, this also yields an intuitionistic probability measure on spaces of theorems. The central result is also extended to yield intuitionistic probabilities based on more general weighted rule systems defined over bicartesian closed categories.
Improved Word Representation Learning with Sememes

Sememes are minimum semantic units of word meanings, and the meaning of each word sense is typically composed by several sememes. Since sememes are not explicit for each word, people manually annotate word sememes and form linguistic common-sense knowledge bases. In this paper, we present that, word sememe information can improve word representation learning (WRL), which maps words into a low-dimensional semantic space and serves as a fundamental step for many NLP tasks. The key idea is to utilize word sememes to capture exact meanings of a word within specific contexts accurately. More specifically, we follow the framework of Skip-gram and present three sememe-encoded models to learn representations of sememes, senses and words, where we apply the attention scheme to detect word senses in various contexts. We conduct experiments on two tasks including word similarity and word analogy, and our models significantly outperform baselines. The results indicate that WRL can benefit from sememes via the attention scheme, and also confirm our models being capable of correctly modeling sememe information.
Decision making with the analytic hierarchy process

Decisions involve many intangibles that need to be traded off. To do that, they have to be measured along side tangibles whose measurements must also be evaluated as to, how well, they serve the objectives of the decision maker. The Analytic Hierarchy Process (AHP) is a theory of measurement through pairwise comparisons and relies on the judgements of experts to derive priority scales. It is these scales that measure intangibles in relative terms. The comparisons are made using a scale of absolute judgements that represents, how much more, one element dominates another with respect to a given attribute. The judgements may be inconsistent, and how to measure inconsistency and improve the judgements, when possible to obtain better consistency is a concern of the AHP. The derived priority scales are synthesised by multiplying them by the priority of their parent nodes and adding for all such nodes. An illustration is included.