nils-herrmann/clean_scirepeval huggingface dataset:
Hierarchical cluster analysis of labour market regulations and population health: a taxonomy of low- and middle-income countries

An important contribution of the social determinants of health perspective has been to inquire about non-medical determinants of population health. Among these, labour market regulations are of vital significance. In this study, we investigate the labour market regulations among low- and middle-income countries (LMICs) and propose a labour market taxonomy to further understand population health in a global context.



Using Gross National Product per capita, we classify 113 countries into either low-income (n = 71) or middle-income (n = 42) strata. Principal component analysis of three standardized indicators of labour market inequality and poverty is used to construct 2 factor scores. Factor score reliability is evaluated with Cronbach's alpha. Using these scores, we conduct a hierarchical cluster analysis to produce a labour market taxonomy, conduct zero-order correlations, and create box plots to test their associations with adult mortality, healthy life expectancy, infant mortality, maternal mortality, neonatal mortality, under-5 mortality, and years of life lost to communicable and non-communicable diseases. Labour market and health data are retrieved from the International Labour Organization's Key Indicators of Labour Markets and World Health Organization's Statistical Information System.



Six labour market clusters emerged: Residual (n = 16), Emerging (n = 16), Informal (n = 10), Post-Communist (n = 18), Less Successful Informal (n = 22), and Insecure (n = 31).  (i) labour market poverty and population health is correlated in both LMICs; (ii) association between labour market inequality and health indicators is significant only in low-income countries; (iii) Emerging (e.g., East Asian and Eastern European countries) and Insecure (e.g., sub-Saharan African nations) clusters are the most advantaged and disadvantaged, respectively, with the remaining clusters experiencing levels of population health consistent with their labour market characteristics.



The labour market regulations of LMICs appear to be important social determinant of population health. This study demonstrates the heuristic value of understanding the labour markets of LMICs and their health effects using exploratory taxonomy approaches.
Remote control of a domestic equipment from an Android application based on Raspberry pi card

In this paper, we propose a Remote control of a domestic equipment from an Android application based on Raspberry pi card. Home automation has been recalled in the first stage. In the second stage an application has been developed based on the android system. The different diagrams have been presented. Different codes have been developed to allow the communication between the remote user, the web server, the raspberry pi card and the home components. Also an interface card has been developed to assure the communication between the home components and raspberry pi card. The application has been installed on a Smartphone, a web server and a raspberry pi card to order the shutter of windows. The experimental results are successful.
The global prevalence of dementia: A systematic review and metaanalysis

The evidence base on the prevalence of dementia is expanding rapidly, particularly in countries with low and middle incomes. A reappraisal of global prevalence and numbers is due, given the significant implications for social and public policy and planning.



In this study we provide a systematic review of the global literature on the prevalence of dementia (1980-2009) and metaanalysis to estimate the prevalence and numbers of those affected, aged ≥60 years in 21 Global Burden of Disease regions.



Age-standardized prevalence for those aged ≥60 years varied in a narrow band, 5%-7% in most world regions, with a higher prevalence in Latin America (8.5%), and a distinctively lower prevalence in the four sub-Saharan African regions (2%-4%). It was estimated that 35.6 million people lived with dementia worldwide in 2010, with numbers expected to almost double every 20 years, to 65.7 million in 2030 and 115.4 million in 2050. In 2010, 58% of all people with dementia lived in countries with low or middle incomes, with this proportion anticipated to rise to 63% in 2030 and 71% in 2050.



The detailed estimates in this study constitute the best current basis for policymaking, planning, and allocation of health and welfare resources in dementia care. The age-specific prevalence of dementia varies little between world regions, and may converge further. Future projections of numbers of people with dementia may be modified substantially by preventive interventions (lowering incidence), improvements in treatment and care (prolonging survival), and disease-modifying interventions (preventing or slowing progression). All countries need to commission nationally representative surveys that are repeated regularly to monitor trends.
Building Portable Options: Skill Transfer in Reinforcement Learning

The options framework provides a method for reinforcement learning agents to build new high-level skills. However, since options are usually learned in the same state space as the problem the agent is currently solving, they cannot be ported to other similar tasks that have different state spaces. We introduce the notion of learning options in agent-space, the portion of the agent’s sensation that is present and retains the same semantics across successive problem instances, rather than in problem-space. Agent-space options can be reused in later tasks that share the same agent-space but are sufficiently distinct to require different problem-spaces. We present experimental results that demonstrate the use of agent-space options in building reusable skills.
An Impossibility Theorem for Clustering

Although the study of clustering is centered around an intuitively compelling goal, it has been very difficult to develop a unified framework for reasoning about it at a technical level, and profoundly diverse approaches to clustering abound in the research community. Here we suggest a formal perspective on the difficulty in finding such a unification, in the form of an impossibility theorem: for a set of three simple properties, we show that there is no clustering function satisfying all three. Relaxations of these properties expose some of the interesting (and unavoidable) trade-offs at work in well-studied clustering techniques such as single-linkage, sum-of-pairs, k-means, and k-median.
Local Low-Rank Matrix Approximation

Matrix approximation is a common tool in recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model where we assume instead that the matrix is locally of low-rank, leading to a representation of the observed matrix as a weighted sum of low-rank matrices. We analyze the accuracy of the proposed local lowrank modeling. Our experiments show improvements in prediction accuracy over classical approaches for recommendation tasks.
A convolutional neural network cascade for face detection

In real-world face detection, large visual variations, such as those due to pose, expression, and lighting, demand an advanced discriminative model to accurately differentiate faces from the backgrounds. Consequently, effective models for the problem tend to be computationally prohibitive. To address these two conflicting challenges, we propose a cascade architecture built on convolutional neural networks (CNNs) with very powerful discriminative capability, while maintaining high performance. The proposed CNN cascade operates at multiple resolutions, quickly rejects the background regions in the fast low resolution stages, and carefully evaluates a small number of challenging candidates in the last high resolution stage. To improve localization effectiveness, and reduce the number of candidates at later stages, we introduce a CNN-based calibration stage after each of the detection stages in the cascade. The output of each calibration stage is used to adjust the detection window position for input to the subsequent stage. The proposed method runs at 14 FPS on a single CPU core for VGA-resolution images and 100 FPS using a GPU, and achieves state-of-the-art detection performance on two public face detection benchmarks.
Distributed Top-K Monitoring

The querying and analysis of data streams has been a topic of much recent interest, motivated by applications from the fields of networking, web usage analysis, sensor instrumentation, telecommunications, and others. Many of these applications involve monitoring answers to continuous queries over data streams produced at physically distributed locations, and most previous approaches require streams to be transmitted to a single location for centralized processing. Unfortunately, the continual transmission of a large number of rapid data streams to a central location can be impractical or expensive. We study a useful class of queries that continuously report the k largest values obtained from distributed data streams ("top-k monitoring queries"), which are of particular interest because they can be used to reduce the overhead incurred while running other types of monitoring queries. We show that transmitting entire data streams is unnecessary to support these queries and present an alternative approach that reduces communication significantly. In our approach, arithmetic constraints are maintained at remote stream sources to ensure that the most recently provided top-k answer remains valid to within a user-specified error tolerance. Distributed communication is only necessary on occasion, when constraints are violated, and we show empirically through extensive simulation on real-world data that our approach reduces overall communication cost by an order of magnitude compared with alternatives that o er the same error guarantees.
An analysis of using reflectors for distributed denial-of-service attacks

Attackers can render distributed denial-of-service attacks more difficult to defend against by bouncing their flooding traffic off of reflectors; that is, by spoofing requests from the victim to a large set of Internet servers that will in turn send their combined replies to the victim. The resulting dilution of locality in the flooding stream complicates the victim's abilities both to isolate the attack traffic in order to block it, and to use traceback techniques for locating the source of streams of packets with spoofed source addresses, such as ITRACE [Be00a], probabilistic packet marking [SWKA00], [SP01], and SPIE [S+01]. We discuss a number of possible defenses against reflector attacks, finding that most prove impractical, and then assess the degree to which different forms of reflector traffic will have characteristic signatures that the victim can use to identify and filter out the attack traffic. Our analysis indicates that three types of reflectors pose particularly significant threats: DNS and Gnutella servers, and TCP-based servers (particularly Web servers) running on TCP implementations that suffer from predictable initial sequence numbers. We argue in conclusion in support of "reverse ITRACE" [Ba00] and for the utility of packet traceback techniques that work even for low volume flows, such as SPIE.
The LiteOS Operating System: Towards Unix-Like Abstractions for Wireless Sensor Networks

This paper presents LiteOS, a multi-threaded operating system that provides Unix-like abstractions for wireless sensor networks. Aiming to be an easy-to-use platform, LiteOS offers a number of novel features, including: (1) a hierarchical file system and a wireless shell interface for user interaction using UNIX-like commands; (2) kernel support for dynamic loading and native execution of multithreaded applications; and (3) online debugging, dynamic memory, and file system assisted communication stacks. LiteOS also supports software updates through a separation between the kernel and user applications, which are bridged through a suite of system calls. Besides the features that have been implemented, we also describe our perspective on LiteOS as an enabling platform. We evaluate the platform experimentally by measuring the performance of common tasks, and demonstrate its programmability through twenty-one example applications.
Real Estate Ranking via Mixed Land-use Latent Models

Mixed land use refers to the effort of putting residential, commercial and recreational uses in close proximity to one another. This can contribute economic benefits, support viable public transit, and enhance the perceived security of an area. It is naturally promising to investigate how to rank real estate from the viewpoint of diverse mixed land use, which can be reflected by the portfolio of community functions in the observed area. To that end, in this paper, we develop a geographical function ranking method, named FuncDivRank, by incorporating the functional diversity of communities into real estate appraisal. Specifically, we first design a geographic function learning model to jointly capture the correlations among estate neighborhoods, urban functions, temporal effects, and user mobility patterns. In this way we can learn latent community functions and the corresponding portfolios of estates from human mobility data and Point of Interest (POI) data. Then, we learn the estate ranking indicator by simultaneously maximizing ranking consistency and functional diversity, in a unified probabilistic optimization framework. Finally, we conduct a comprehensive evaluation with real-world data. The experimental results demonstrate the enhanced performance of the proposed method for real estate appraisal.
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria

This paper investigates how the splitting cri teria and pruning methods of decision tree learning algorithms are in uenced by misclas si cation costs or changes to the class distri bution Splitting criteria that are relatively insensitive to costs class distributions are found to perform as well as or better than in terms of expected misclassi cation cost splitting criteria that are cost sensitive Con sequently there are two opposite ways of deal ing with imbalance One is to combine a cost insensitive splitting criterion with a cost in sensitive pruning method to produce a deci sion tree algorithm little a ected by cost or prior class distribution The other is to grow a cost independent tree which is then pruned in a cost sensitive manner
A Data Mining and CIDF Based Approach for Detecting Novel and Distributed Intrusions

As the recent distributed Denial-of-Service (DDOS) attacks on several major Internet sites have shown us, no open computer network is immune from intrusions. Furthermore, intrusion detection systems (IDSs) need to be updated timely whenever a novel intrusion surfaces; and geographically distributed IDSs need to cooperate to detect distributed and coordinated intrusions. In this paper, we describe an experimental system, based on the Common Intrusion Detection Framework (CIDF), where multiple IDSs can exchange attack information to detect distributed intrusions. The system also includes an ID model builder, where a data mining engine can receive audit data of a novel attack from an IDS, compute a new detection model, and then distribute it to other IDSs. We describe our experiences in implementing such system and the preliminary results of deploying the system in an experimental network.
Tenzing A SQL Implementation On The MapReduce Framework

Tenzing is a query engine built on top of MapReduce  for ad hoc analysis of Google data. Tenzing supports a mostly complete SQL implementation (with several extensions) combined with several key characteristics such as heterogeneity, high performance, scalability, reliability, metadata awareness, low latency, support for columnar storage and structured data, and easy extensibility. Tenzing is currently used internally at Google by 1000+ employees and serves 10000+ queries per day over 1.5 petabytes of compressed data. In this paper, we describe the architecture and implementation of Tenzing, and present benchmarks of typical analytical queries.
Incorporating Selectional Preferences in Multi-hop Relation Extraction

Relation extraction is one of the core challenges in automated knowledge base construction. One line of approach for relation extraction is to perform multi-hop reasoning on the paths connecting an entity pair to infer new relations. While these methods have been successfully applied for knowledge base completion, they do not utilize the entity or the entity type information to make predictions. In this work, we incorporate selectional preferences, i.e., relations enforce constraints on the allowed entity types for the candidate entities, to multi-hop relation extraction by including entity type information. We achieve a 17.67% (relative) improvement in MAP score in a relation extraction task when compared to a method that does not use entity type information.
Stochastic Alternating Direction Method of Multipliers

The Alternating Direction Method of Multipliers (ADMM) has received lots of attention recently due to the tremendous demand from large-scale and data-distributed machine learning applications. In this paper, we present a stochastic setting for optimization problems with non-smooth composite objective functions. To solve this problem, we propose a stochastic ADMM algorithm. Our algorithm applies to a more general class of convex and nonsmooth objective functions, beyond the smooth and separable least squares loss used in lasso. We also demonstrate the rates of convergence for our algorithm under various structural assumptions of the stochastic function: O(1/ √ t) for convex functions and O(log t/t) for strongly convex functions. Compared to previous literature, we establish the convergence rate of ADMM for convex problems in terms of both the objective value and the feasibility violation. A novel application named GraphGuided SVM is proposed to demonstrate the usefulness of our algorithm.
Automatic facial expression recognition using features of salient facial patches

Extraction of discriminative features from salient facial patches plays a vital role in effective facial expression recognition. The accurate detection of facial landmarks improves the localization of the salient patches on face images. This paper proposes a novel framework for expression recognition by using appearance features of selected facial patches. A few prominent facial patches, depending on the position of facial landmarks, are extracted which are active during emotion elicitation. These active patches are further processed to obtain the salient patches which contain discriminative features for classification of each pair of expressions, thereby selecting different facial patches as salient for different pair of expression classes. One-against-one classification method is adopted using these features. In addition, an automated learning-free facial landmark detection technique has been proposed, which achieves similar performances as that of other state-of-art landmark detection methods, yet requires significantly less execution time. The proposed method is found to perform well consistently in different resolutions, hence, providing a solution for expression recognition in low resolution images. Experiments on CK+ and JAFFE facial expression databases show the effectiveness of the proposed system.
The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services

The purposes of this study are to construct an instrument to evaluate service quality of mobile value-added services and have a further discussion of the relationships among service quality, perceived value, customer satisfaction, and post-purchase intention. Structural equation modeling and multiple regression analysis were used to analyze the data collected from college and graduate students of fifteen major universities in Taiwan. The main findings are as follows: (1) service quality positively influences both perceived value and customer satisfaction; (2) perceived value positively influences on both customer satisfaction and post-purchase intention; (3) customer satisfaction positively influences post-purchase intention; (4) service quality has an indirect positive influence on post-purchase intention through customer satisfaction or perceived value; (5) among the dimensions of service quality, “customer service and system reliability” is most influential on perceived value and customer satisfaction, and the influence of “content quality” ranks second; (6) the proposed model is proven with the effectiveness in explaining the relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile added-value services.
Learning Multiple Tasks with Deep Relationship Networks

Deep networks trained on large-scale data can learn transferable features to promote learning multiple tasks. As deep features eventually transition from general to specific along deep networks, a fundamental problem is how to exploit the relationship across different tasks and improve the feature transferability in the task-specific layers. In this paper, we propose Deep Relationship Networks (DRN) that discover the task relationship based on novel tensor normal priors over the parameter tensors of multiple task-specific layers in deep convolutional networks. By jointly learning transferable features and task relationships, DRN is able to alleviate the dilemma of negative-transfer in the feature layers and under-transfer in the classifier layer. Extensive experiments show that DRN yields state-of-the-art results on standard multi-task learning benchmarks.
Ego-motion estimation and moving object tracking using multi-layer LIDAR

This paper presents an approach for the robust recognition of a complex and dynamic driving environment, such as an urban area, using on-vehicle multi-layer LIDAR. The multi-layer LIDAR alleviates the consequences of occlusion by vertical scanning; it can detect objects with different heights simultaneously, and therefore the influence of occlusion can be curbed. The road environment recognition algorithm proposed in this paper consists of three procedures: ego-motion estimation, construction and updating of a 3-dimensional local grid map, and the detection and tracking of moving objects. The integration of these procedures enables us to estimate ego-motion accurately, along with the positions and states of moving objects, the free area where vehicles and pedestrians can move freely, and the ‘unknown’ area, which have never previously been observed in a road environment.
High-Frequency-Link Soft-Switching PWM DC–DC Converter for EV On-Board Battery Chargers

This paper proposes a high-frequency-link soft-switching pulse-width modulated dc-dc converter for electric vehicle (EV) on-board battery chargers. Zero-voltage switching of power switches is achieved from light load to full load. Reverse-recovery losses can be reduced in the secondary side output diodes without using any additional circuit components. Zero-current switching of output diodes is achieved by using the series-resonant circuit in the secondary side. The circulating current in the primary side full-bridge circuit can be changed by the operation modes of the output diode current. As a result, a high efficiency can be achieved for EV on-board battery chargers. The performance of the proposed converter is evaluated throughout experimental results for a 2.0-kW prototype circuit.