nils-herrmann/clean_scirepeval huggingface dataset:
Comparing strategies to integrate health information systems following a data warehouse approach in four countries

This paper addresses one of the major obstacles of reaching the Millennium Development Goals: inefficient and unreliable information systems. Leading international organizations have called for integrated data warehouses as one of the solutions, but this remains hard to achieve. This paper presents four country cases of standardizing and integrating health data which are all following what is here termed a data warehouse approach; data from across different health programs are organized in one database framework – or data warehouse. In all countries, fragmentation of health information in different partly overlapping sub-systems run by different vertical health programs represented a major problem for the efficient use of health information. While South Africa developed a new integrated system in addition to the existing fragmented subsystems, Zanzibar, Sierra Leone and Botswana, all aimed to encompass all or most of the data from the existing systems. The three latter countries all followed slightly different approaches; more or less incremental in the approach to standardizing health data, and more or less strict in whether to include “all” data, and whether to solve all inconsistencies between the various data sets included early on. The four cases demonstrate that integration is as much, and maybe more, about aligning organisationalpolitical actors as it is about technical solutions. The technical solutions are however important in aligning these actors and in enabling integration. We argue that “attractors”, technical solutions or standards that achieve a certain level of success and enable the building of momentum, are important in aligning the various political actors. In turn, these attractors need to evolve within the changing context of a growing health information system in order to achieve the scale needed to address the Millennium Development Goals with full force.
Age-related changes in grey and white matter structure throughout adulthood

Normal ageing is associated with gradual brain atrophy. Determining spatial and temporal patterns of change can help shed light on underlying mechanisms. Neuroimaging provides various measures of brain structure that can be used to assess such age-related change but studies to date have typically considered single imaging measures. Although there is consensus on the notion that brain structure deteriorates with age, evidence on the precise time course and spatial distribution of changes is mixed. We assessed grey matter (GM) and white matter (WM) structure in a group of 66 adults aged between 23 and 81. Multimodal imaging measures included voxel-based morphometry (VBM)-style analysis of GM and WM volume and diffusion tensor imaging (DTI) metrics of WM microstructure. We found widespread reductions in GM volume from middle age onwards but earlier reductions in GM were detected in frontal cortex. Widespread age-related deterioration in WM microstructure was detected from young adulthood onwards. WM decline was detected earlier and more sensitively using DTI-based measures of microstructure than using markers of WM volume derived from conventional T1-weighted imaging.
Keyword Spotting: An Audio Mining Technique in Speech Processing – A Survey

Audio mining is a branch of data mining that is used to search and analyze the contents of audio signal automatically. Keyword spotting (KWS) is an important audio mining technique which searches audio signals for finding the occurrences of given keyword within the input spoken utterance. KWS provides a satisfactory audio mining solution for various tasks like spoken document indexing and retrieval. The research in audio mining has received increasing attention due to the increase in amount of audio content in the Internet, telephone call conversation and other sources. KWS is classified according to the type of input speech content and the method used for spotting. A number of approaches have been used in keyword spotting like DTW, HMM, Neural Network, Vector quantization and other approaches. KWS has been utilized in a broad variety of applications. A majority of such applications relate to audio indexing and phone call routing. In this paper, various audio mining methods and keyword spotting techniques are discussed.
The Concept of Flow

What constitutes a good life? Few questions are of more fundamental importance to a positive psychology. Flow research has yielded one answer, providing an understanding of experiences during which individuals are fully involved in the present moment. Viewed through the experiential lens of flow, a good life is one that is characterized by complete absorption in what one does. In this chapter, we describe the flow model of optimal experience and optimal development, explain how flow and related constructs have been measured, discuss recent work in this area, and identify some promising directions for future research.
A Risk Management Approach to the "Insider Threat"

Recent surveys indicate that the financial impact and operating losses due to insider intrusions are increasing. But these studies often disagree on what constitutes an “insider;” indeed, many define it only implicitly. In theory, appropriate selection of, and enforcement of, properly specified security policies should prevent legitimate users from abusing their access to computer systems, information, and other resources. However, even if policies could be expressed precisely, the natural mapping between the natural language expression of a security policy, and the expression of that policy in a form that can be implemented on a computer system or network, creates gaps in enforcement. This paper defines “insider” precisely, in terms of these gaps, and explores an access-based model for analyzing threats that include those usually termed “insider threats.” This model enables an organization Matt Bishop Matt Bishop, Dept. of Computer Science, University of California at Davis e-mail: bishop@cs.
Imaging of conjoined twins

The incidence of conjoined twins is estimated to be around 1 in 250,000 live births. There is a distinct female predominance. In this paper the imaging of conjoined twins both antenatally and postnatally is reviewed, in particular taking into consideration recent advances with multidetector CT. Accurate counselling of parents regarding the likely outcome of the pregnancy and the likelihood of successful separation is dependent on good prenatal imaging with ultrasound and MRI. Planning of postnatal surgical separation is aided by accurate preoperative imaging which, depending on the conjoined area, will encompass many imaging modalities, but often relies heavily on CT scanning.
The Impact Of Natural Language Processing-Based Textual Analysis Of Social Media Interactions On Decision Making

Organizations typically use sentiment analysis-based systems, or even resort to simple manual analysis, to try to derive useful meaning from the public digital “chatter” of their customers. Motivated by the need for a more accurate way to qualitatively mine valuable productand brandoriented consumer-generated text, this paper experimentally tests the ability of an NLP-based analytics approach to extracting knowledge from highly unstructured text. Results indicate that for detecting problems from social media data, natural language processing outperforms sentiment analysis. Surprisingly, the experiment indicates that sentiment analysis is not only no better than manual analysis of social media data toward the goal of supporting organizational decision-making, but may even prove disadvantageous to such efforts.
IoT security attacks using reverse engineering methods on WSN applications

With the rapid technological advancements of sensors, Wireless Sensor Networks (WSNs) have become a popular technology for the Internet of Things (IoT). We investigated the security of WSNs in an environmental monitoring application with the goal to demonstrate the overall security. We implemented a Secure Temperature Monitoring System (STMS), which served as our WSN application. Our results revealed a security flaw found in the bootstrap loader (BSL) password used to protect MSP430 micro-controller units (MCUs). We demonstrated how the BSL password could be brute forced in a matter of days. Furthermore, we illustrate how an attacker can reverse engineer WSN applications to obtain critical security information such as encryption keys. We contribute a solution to patch the weak BSL password security flaw and improve the security of MSP430 MCU chips. The Secure-BSL patch we contribute allows the randomization of the BSL password. Our solution increases the brute force time to decades. The impractical brute force time enhances the security of the MSP430 and prevents future reverse engineering tactics. Our research serves as proof that the security of WSNs and the overall IoT technology is broken if we cannot protect these everyday objects at the physical layer.
Ensemble Transfer Learning Algorithm

Transfer learning and ensemble learning are the new trends for solving the problem that training data and test data have different distributions. In this paper, we design an ensemble transfer learning framework to improve the classification accuracy when the training data are insufficient. First, a weighted-resampling method for transfer learning is proposed, which is named TrResampling. In each iteration, the data with heavy weights in the source domain are resampled, and the TrAdaBoost algorithm is used to adjust the weights of the source data and target data. Second, three classic machine learning algorithms, namely, naive Bayes, decision tree, and SVM, are used as the base learners of TrResampling, where the base learner with the best performance is chosen for transfer learning. To illustrate the performance of TrResampling, the TrAdaBoost and decision tree are used for evaluation and comparison on 15 UCI data sets, TrAdaBoost, ARTL, and SVM are used for evaluation and comparison on five text data sets. According to the experimental results, our proposed TrResampling is superior to the state-of-the-art learning methods on UCI data sets and text data sets. In addition, TrResampling, bagging-based transfer learning algorithm, and MultiBoosting-based transfer learning algorithm (TrMultiBoosting) are assembled in the framework, and we compare the three ensemble transfer learning algorithms with TrAdaBoost to illustrate the framework’s effective transfer ability.
A neurocognitive approach to understanding the neurobiology of addiction

Recent concepts of addiction to drugs (e.g. cocaine) and non-drugs (e.g. gambling) have proposed that these behaviors are the product of an imbalance between three separate, but interacting, neural systems: an impulsive, largely amygdala-striatum dependent, neural system that promotes automatic, habitual and salient behaviors; a reflective, mainly prefrontal cortex dependent, neural system for decision-making, forecasting the future consequences of a behavior, and inhibitory control; and the insula that integrates interoception states into conscious feelings and into decision-making processes that are involved in uncertain risk and reward. These systems account for poor decision-making (i.e. prioritizing short-term consequences of a decisional option) leading to more elevated addiction risk and relapse. This article provides neural evidence for this three-systems neural model of addiction.
An incremental constraint-based framework for task and motion planning

We present a new constraint-based framework for task and motion planning (TMP). Our approach is extensible, probabilistically-complete, and offers improved performance and generality compared to a similar, state-of-the-art planner. The key idea is to leverage incremental constraint solving to efficiently incorporate geometric information at the task level. Using motion feasibility information to guide task planning improves scalability of the overall planner. Our key abstractions address the requirements of manipulation and object rearrangement. We validate our approach on a physical manipulator and evaluate scalability on scenarios with many objects and long plans, showing order-ofmagnitude gains compared to the benchmark planner and improved scalability from additional geometric guidance. Finally, in addition to describing a new method for TMP and its implementation on a physical robot, we also put forward requirements and abstractions for the development of similar planners in the future.
On-Demand Deployment of Multiple Aerial Base Stations for Traffic Offloading and Network Recovery

Unmanned aerial vehicles (UAVs) are being utilized for a wide spectrum of applications in wireless networks leading to attractive business opportunities. In the case of abrupt disruption to existing cellular network operation or infrastructure, e.g., due to an unexpected surge in user demand or a natural disaster, UAVs can be deployed to provide instant recovery via temporary wireless coverage in designated areas. A major challenge is to determine efficiently how many UAVs are needed and where to position them in a relatively large 3D search space. To this end, we formulate the problem of 3D deployment of a fleet of UAVs as a mixed integer linear program, and present a greedy approach that mimics the optimal behavior assuming a grid composed of a finite set of possible UAV locations. In addition, we propose and evaluate a novel low complexity algorithm for multiple UAV deployment in a continuous 3D space, based on an unsupervised learning technique that relies on the notion of electrostatics with repulsion and attraction forces. We present performance results for the proposed algorithm as a function of various system parameters and demonstrate its effectiveness compared to the close-to-optimal greedy approach and its superiority compared to recent related work from the literature.
Weakly supervised user intent detection for multi-domain dialogues

Users interact with mobile apps with certain intents such as finding a restaurant. Some intents and their corresponding activities are complex and may involve multiple apps; for example, a restaurant app, a messenger app and a calendar app may be needed to plan a dinner with friends. However, activities may be quite personal and third-party developers would not be building apps to specifically handle complex intents (e.g., a DinnerPlanner). Instead we want our intelligent agent to actively learn to understand these intents and provide assistance when needed. This paper proposes a framework to enable the agent to learn an inventory of intents from a small set of task-oriented user utterances. The experiments show that on previously unseen user activities, the agent is able to reliably recognize user intents using graph-based semi-supervised learning methods. The dataset, models, and the system outputs are available to research community.
On Detecting Clustered Anomalies Using SCiForest

Detecting local clustered anomalies is an intricate problem for many existing anomaly detection methods. Distance-based and density-based methods are inherently restricted by their basic assumptions—anomalies are either far from normal points or being sparse. Clustered anomalies are able to avoid detection since they defy these assumptions by being dense and, in many cases, in close proximity to normal instances. In this paper, without using any density or distance measure, we propose a new method called SCiForest to detect clustered anomalies. SCiForest separates clustered anomalies from normal points effectively even when clustered anomalies are very close to normal points. It maintains the ability of existing methods to detect scattered anomalies, and it has superior time and space complexities against existing distance-based and density-based methods.
Threats to Feminist Identity and Reactions to Gender Discrimination

The aim of this research was to examine conditions that modify feminists' support for women as targets of gender discrimination. In an experimental study we tested a hypothesis that threatened feminist identity will lead to greater differentiation between feminists and conservative women as victims of discrimination and, in turn, a decrease in support for non-feminist victims. The study was conducted among 96 young Polish female professionals and graduate students from Gender Studies programs in Warsaw who self-identified as feminists (Mage  = 22.23). Participants were presented with a case of workplace gender discrimination. Threat to feminist identity and worldview of the discrimination victim (feminist vs. conservative) were varied between research conditions. Results indicate that identity threat caused feminists to show conditional reactions to discrimination. Under identity threat, feminists perceived the situation as less discriminatory when the target held conservative views on gender relations than when the target was presented as feminist. This effect was not observed under conditions of no threat. Moreover, feminists showed an increase in compassion for the victim when she was portrayed as a feminist compared to when she was portrayed as conservative. Implications for the feminist movement are discussed.
Hybrid Multi-Mode Narrow-Frame Antenna for WWAN/LTE Metal-Rimmed Smartphone Applications

A hybrid multi-mode narrow-frame antenna for WWAN/LTE metal-rimmed smartphone applications is proposed in this paper. The ground clearance is only 5 mm × 45 mm, which is promising for narrow-frame smartphones. The metal rim with a small gap is connected to the system ground by three grounded patches. This proposed antenna can excite three coupled-loop modes and one slot mode. By incorporating these four modes, the proposed antenna can provide coverage for GSM850/900, DCS/PCS/UMTS2100, and LTE2300/2500 operations. Detailed design considerations of the proposed antenna are described, and both experimental and simulated results are also presented.
Working memory

It is suggested that working memory comprises a system for the temporary storage and manipulation of information, forming an important link between perception and controlled action. Evidence is presented for a three-component model, comprising an attentional control system, the central executive, and two subsidiary slave systems. One of these the, the visuo-spatial sketch pad holds and manipulates spatial information, while the other, the phonological loop performs a similar function for auditory and speech-based information. Evidence is presented for the view that the phonological loop has evolved as a mechanism to facilitate the acquisition of language.
Seeing the Big Picture: Deep Embedding with Contextual Evidences

In the Bag-of-Words (BoW) model based image retrieval task, the precision of visual matching plays a critical role in improv ing retrieval performance. Conventionally, local cues of a keypo int are employed. However, such strategy does not consider the cont xtual evidences of a keypoint, a problem which would lead to the pre valence of false matches. To address this problem, this paper d efines “true match” as a pair of keypoints which are similar on three levels,i.e., local, regional, and global. Then, a principled probabilis tic framework is established, which is capable of implicitly in tegrating discriminative cues from all these feature levels. Specifically, the Convolutional Neural Network (CNN) is employed to extract features from regional and global patches , leading to the so-called “Deep Embedding” framework. CNN has bee n shown to produce excellent performance on a dozen computer v ision tasks such as image classification and detection, but fe w works have been done on BoW based image retrieval. In this paper, fir stly we show that proper pre-processing techniques are necessar y for effective usage of CNN feature. Then, in the attempt to fit it i nto our model, a novel indexing structure called “Deep Indexing ” is introduced, which dramatically reduces memory usage. Extensive experiments on three benchmark datasets demonst rate that, the proposed Deep Embedding method greatly promotes t he retrieval accuracy when CNN feature is integrated. We show t hat our method is efficient in terms of both memory and time cost, a nd compares favorably with the state-of-the-art methods.
$V_{T}$ Compensation Circuit for AM OLED Displays Composed of Two TFTs and One Capacitor

In this paper, we propose the threshold-voltage compensation pixel circuit that is composed of two thin-film transistors (TFTs) and one capacitor (2T1C). It not only compensates the deviation of the threshold voltage of the driver TFT but also actualizes the large aperture ratio for organic light-emitting diode (OLED) devices as well as the traditional 2T1C circuit. We show the result of SPICE simulation for the pixel circuit; it indicates that the circuit can allocate the relatively large aperture ratio for OLED devices
Eye Gaze Tracking Using an Active Stereo Head

In the eye gaze tracking problem, the goal is to determine where on a monitor screen a computer user is looking – the gaze point. Existing systems generally have one of two limitations: either the head must remain fixed in front of a stationary camera, or, to allow for head motion, the user must wear an obtrusive device. We introduce a 3D eye tracking system where head motion is allowed without the need for markers or worn devices. We use a pair of stereo systems: a wide angle stereo system detects the face and steers an active narrow FOV stereo system to track the eye at high resolution. For high resolution tracking, the eye is modeled in 3D, including the corneal ball, pupil and fovea. In this paper, we discuss the calibration of the stereo systems, the eye model, eye detection and tracking, and we close with an evaluation of the accuracy of the estimated gaze point on the monitor.
Efficient frame-sequential label propagation for video object segmentation

In this work, we present an approach for segmenting objects in videos taken in complex scenes. It propagates initial object label through the entire video by a frame-sequential manner where the initial label is usually given by the user. The proposed method has several contributions which make the propagation much more robust and accurate than other methods. First, a novel supervised motion estimation algorithm is employed between each pair of neighboring frames, by which a predicted shape model can be warped in order to segment the similar color around object boundary. Second, unlike previous methods with fixed modeling range, we design a novel range-adaptive appearance model to handle the tough problem of occlusion. Last, the paper gives a reasonable framework based on GraphCut algorithm for obtaining the final label of the object by combining the clues from both appearance and motion. In the experiments, the proposed approach is evaluated qualitatively and quantitatively with some recent methods to show it achieves state-of-art results on multiple videos from benchmark data sets.