nils-herrmann/clean_scirepeval huggingface dataset:
Contextualising complementary feeding in a broader framework for stunting prevention.

An estimated 165 million children are stunted due to the combined effects of poor nutrition, repeated infection and inadequate psychosocial stimulation. The complementary feeding period, generally corresponding to age 6-24 months, represents an important period of sensitivity to stunting with lifelong, possibly irrevocable consequences. Interventions to improve complementary feeding practices or the nutritional quality of complementary foods must take into consideration the contextual as well as proximal determinants of stunting. This review presents a conceptual framework that highlights the role of complementary feeding within the layers of contextual and causal factors that lead to stunted growth and development and the resulting short- and long-term consequences. Contextual factors are organized into the following groups: political economy; health and health care systems; education; society and culture; agriculture and food systems; and water, sanitation and environment. We argue that these community and societal conditions underlie infant and young child feeding practices, which are a central pillar to healthy growth and development, and can serve to either impede or enable progress. Effectiveness studies with a strong process evaluation component are needed to identify transdisciplinary solutions. Programme and policy interventions aimed at preventing stunting should be informed by careful assessment of these factors at all levels.
Walk, Look, Remember: The Influence of the Gallery’s Spatial Layout on Human Memory for an Art Exhibition

The spatial organisation of museums and its influence on the visitor experience has been the subject of numerous studies. Previous research, despite reporting some actual behavioural correlates, rarely had the possibility to investigate the cognitive processes of the art viewers. In the museum context, where spatial layout is one of the most powerful curatorial tools available, attention and memory can be measured as a means of establishing whether or not the gallery fulfils its function as a space for contemplating art. In this exploratory experiment, 32 participants split into two groups explored an experimental, non-public exhibition and completed two unanticipated memory tests afterwards. The results show that some spatial characteristics of an exhibition can inhibit the recall of pictures and shift the focus to perceptual salience of the artworks.
CrowdPlanner: A crowd-based route recommendation system

As travel is taking more significant part in our life, route recommendation service becomes a big business and attracts many major players in IT industry. Given a pair of user-specified origin and destination, a route recommendation service aims to provide users with the routes of best travelling experience according to criteria, such as travelling distance, travelling time, traffic condition, etc. However, previous research shows that even the routes recommended by the big-thumb service providers can deviate significantly from the routes travelled by experienced drivers. It means travellers' preferences on route selection are influenced by many latent and dynamic factors that are hard to model exactly with pre-defined formulas. In this work we approach this challenging problem with a very different perspective- leveraging crowds' knowledge to improve the recommendation quality. In this light, CrowdPlanner - a novel crowd-based route recommendation system has been developed, which requests human workers to evaluate candidate routes recommended by different sources and methods, and determine the best route based on their feedbacks. In this paper, we particularly focus on two important issues that affect system performance significantly: (1) how to efficiently generate tasks which are simple to answer but possess sufficient information to derive user-preferred routes; and (2) how to quickly identify a set of appropriate domain experts to answer the questions timely and accurately. Specifically, the task generation component in our system generates a series of informative and concise questions with optimized ordering for a given candidate route set so that workers feel comfortable and easy to answer. In addition, the worker selection component utilizes a set of selection criteria and an efficient algorithm to find the most eligible workers to answer the questions with high accuracy. A prototype system has been deployed to many voluntary mobile clients and extensive tests on real-scenario queries have shown the superiority of CrowdPlanner in comparison with the results given by map services and popular route mining algorithms.
Artificial Intelligence Safety and Security

Call for Book Chapters  The history of robotics and artificial intelligence in many ways is also the history of humanity’s attempts to control such technologies. From the Golem of Prague to the military robots of modernity, the debate continues as to what degree of independence such entities should have and how to make sure that they do not turn on us, its inventors. Numerous recent advancements in all aspects of research, development and deployment of intelligent systems are well publicized but safety and security issues related to AI are rarely addressed. This book is proposed to mitigate this fundamental problem. It will be comprised of chapters from leading AI Safety researchers addressing different aspects of the AI control problem as it relates to the development of safe and secure artificial intelligence. The book would be the first textbook to address challenges of constructing safe and secure advanced machine intelligence.
Time series classification from scratch with deep neural networks: A strong baseline

We propose a simple but strong baseline for time series classification from scratch with deep neural networks. Our proposed baseline models are pure end-to-end without any heavy preprocessing on the raw data or feature crafting. The proposed Fully Convolutional Network (FCN) achieves premium performance to other state-of-the-art approaches and our exploration of the very deep neural networks with the ResNet structure is also competitive. The global average pooling in our convolutional model enables the exploitation of the Class Activation Map (CAM) to find out the contributing region in the raw data for the specific labels. Our models provides a simple choice for the real world application and a good starting point for the future research. An overall analysis is provided to discuss the generalization capability of our models, learned features, network structures and the classification semantics.
Aff2Vec: Affect-Enriched Distributional Word Representations

Human communication includes information, opinions and reactions. Reactions are often captured by the affective-messages in written as well as verbal communications. While there has been work in affect modeling and to some extent affective content generation, the area of affective word distributions is not well studied. Synsets and lexica capture semantic relationships across words. These models, however, lack in encoding affective or emotional word interpretations. Our proposed model, Aff2Vec, provides a method for enriched word embeddings that are representative of affective interpretations of words. Aff2Vec outperforms the state-of-the-art in intrinsic word-similarity tasks. Further, the use of Aff2Vec representations outperforms baseline embeddings in downstream natural language understanding tasks including sentiment analysis, personality detection, and frustration prediction.
Adaptive Optimization of Very Large Join Queries

The use of business intelligence tools and other means to generate queries has led to great variety in the size of join queries. While most queries are reasonably small, join queries with up to a hundred relations are not that exotic anymore, and the distribution of query sizes has an incredible long tail. The largest real-world query that we are aware of accesses more than 4,000 relations. This large spread makes query optimization very challenging. Join ordering is known to be NP-hard, which means that we cannot hope to solve such large problems exactly. On the other hand most queries are much smaller, and there is no reason to sacrifice optimality there. This paper introduces an adaptive optimization framework that is able to solve most common join queries exactly, while simultaneously scaling to queries with thousands of joins. A key component there is a novel search space linearization technique that leads to near-optimal execution plans for large classes of queries. In addition, we describe implementation techniques that are necessary to scale join ordering algorithms to these extremely large queries. Extensive experiments with over 10 different approaches show that the new adaptive approach proposed here performs excellent over a huge spectrum of query sizes, and produces optimal or near-optimal solutions for most common queries.
An Introduction to Machine Learning

Class Description Machine learning is an exciting and fast-moving field of computer science with many recent consumer applications (e.g., Microsoft Kinect, Google Translate, Iphone's Siri, digital camera face detection, Netflix recommendations, Google news) and applications within the sciences and medicine (e.g., predicting protein-protein interactions, species modeling, detecting tumors, personalized medicine). This course introduces undergraduate computer science students to the field of machine learning. Students learn about the theoretical foundations of machine learning and how to apply machine learning to solve new problems. Assuming no prior knowledge in machine learning, the course focuses on two major paradigms in machine learning which are supervised and unsupervised learning. In supervised learning, we learn various methods for classification and regression. Dimensionality reduction and clustering are discussed in the case of unsupervised learning The course will consist of lectures and lab sessions.
Scaled Current Tracking Control for Doubly Fed Induction Generator to Ride-Through Serious Grid Faults

For doubly fed induction generator (DFIG)-based wind turbine, the main constraint to ride-through serious grid faults is the limited converter rating. In order to realize controllable low voltage ride through (LVRT) under the typical converter rating, transient control reference usually need to be modified to adapt to the constraint of converter's maximum output voltage. Generally, the generation of such reference relies on observation of stator flux and even sequence separation. This is susceptible to observation errors during the fault transient; moreover, it increases the complexity of control system. For this issue, this paper proposes a scaled current tracking control for rotor-side converter (RSC) to enhance its LVRT capacity without flux observation. In this method, rotor current is controlled to track stator current in a certain scale. Under proper tracking coefficient, both the required rotor current and rotor voltage can be constrained within the permissible ranges of RSC, thus it can maintain DFIG under control to suppress overcurrent and overvoltage. Moreover, during fault transient, electromagnetic torque oscillations can be greatly suppressed. Based on it, certain additional positive-sequence item is injected into rotor current reference to supply dynamic reactive support. Simulation and experimental results demonstrate the feasibility of the proposed method.
2 Who Is an Insider ?

This paper reports on a workshop in June 2007 on the topic of the insider threat. Attendees represented academia and research institutions, consulting firms, industry—especially the financial services sector, and government. Most participants were from the United States. Conventional wisdom asserts that insiders account for roughly a third of the computer security loss. Unfortunately, there is currently no way to validate or refute that assertion, because data on the insider threat problem is meager at best. Part of the reason so little data exists on the insider threat problem is that the concepts of insider and insider threat are not consistently defined. Consequently, it is hard to compare even the few pieces of insider threat data that do exist. Monitoring is a means of addressing the insider threat, although it is more successful to verify a case of suspected insider attack than it is to identify insider attacks. Monitoring has (negative) implications for personal privacy. However, companies generally have wide leeway to monitor the activity of their employees. Psychological profiling of potential insider attackers is appealing but may be hard to accomplish. More productive may be using psychological tools to promote positive behavior on the part of employees.
Addressing the Racial Disparity in Birth Outcomes : Implications for Maternal Racial Identity on Birth weight

As a widely used marker of health, birthweight has been a persistent racialized disparity with the low birthweight rate of Blacks in Alabama nearly doubling the national average. The purpose of this study was to examine the role of racial identity and acculturation on birthweight in a sample of Black women living in Alabama. Black women (n=72) in West Alabama were surveyed about the birthweight of their first born child. Correlation and multiple linear regression analyses were conducted. Racial identity was the only significant predictor of birthweight. Mothers with a strong racial identity reported having low birthweight babies less often than those who scored lower on racial identity. Further exploration of racial identity revealed self-image as the essential element that predicted birthweight. Birthweight increased 4.2 ounces for each additional degree of self-image. Results also indicated that birthweight decreased as mothers’ age increased, within the widely accepted optimal maternal age range 21 to 35. Results add to the existing body of literature in support of the positive effect racial identity has on health. Findings on age are congruent with the weathering hypothesis, which states that the health of Black women may begin to deteriorate in early adulthood possibly due to the strain of racism.
Learning what to look in chest X-rays with a recurrent visual attention model

X-rays are commonly performed imaging tests that use small amounts of radiation to produce pictures of the organs, tissues, and bones of the body. X-rays of the chest are used to detect abnormalities or diseases of the airways, blood vessels, bones, heart, and lungs. In this work we present a stochastic attention-based model that is capable of learning what regions within a chest X-ray scan should be visually explored in order to conclude that the scan contains a specific radiological abnormality. The proposed model is a recurrent neural network (RNN) that learns to sequentially sample the entire X-ray and focus only on informative areas that are likely to contain the relevant information. We report on experiments carried out with more than 100, 000 X-rays containing enlarged hearts or medical devices. The model has been trained using reinforcement learning methods to learn task-specific policies.
Design and characterization of a differential microstrip patch antenna array at 122 GHz

This paper presents a differential microstrip patch antenna array that operates at 122 GHz and is suitable for radar applications. The differential line was chosen in the proposed antenna due to the differential radar transceiver. The microstrip patch antenna array was designed to achieve a higher gain and bandwidth compared to a single element antenna. An 8-element series-fed array was designed, fabricated and measured. The 8-element array provides an impedance bandwidth of 14 GHz and a gain of around 13.74dBi at 122 GHz. These measurement results are also in good agreement with the simulation results.
Causal Graph Based Decomposition of Factored MDPs

We present Variable Influence Structure Analysis, or VISA, an algorithm that performs hierarchical decomposition of factored Markov decision processes. VISA uses a dynamic Bayesian network model of actions, and constructs a causal graph that captures relationships between state variables. In tasks with sparse causal graphs VISA exploits structure by introducing activities that cause the values of state variables to change. The result is a hierarchy of activities that together represent a solution to the original task. VISA performs state abstraction for each activity by ignoring irrelevant state variables and lower-level activities. In addition, we describe an algorithm for constructing compact models of the activities introduced. State abstraction and compact activity models enable VISA to apply efficient algorithms to solve the stand-alone subtask associated with each activity. Experimental results show that the decomposition introduced by VISA can significantly accelerate construction of an optimal, or near-optimal, policy.
Chapter 2 Surface Sensitization Techniques and Recognition Receptors Immobilization on Biosensors and Microarrays

The quality of a biosensing system relies on the interfacial properties where bioactive species are immobilized. The design of the surface includes both the immobilization of the bioreceptor itself and the overall chemical preparation of the transducer surface. Hence, the sensitivity and specificity of such devices are directly related to the accessibility and activity of the immobilized molecules. The inertness of the surface that limits the nonspecific adsorption sets the background noise of the sensor. The specifications of the biosensor (signal-to-noise ratio) depend largely on the surface chemistry and preparation process of the biointerface. Lastly, a robust interface improves the stability and the reliability of biosensors. This chapter reports in detail the main surface coupling strategies spanning from random immobilization of native biospecies to uniform and oriented immobilization of site-specific modified biomolecules. The immobilization of receptors on various shapes of solid support is then introduced. Detection systems sensitive to surface phenomena require immobilization as very thin layers (two-dimensional biofunctionalization), whereas other detection systems accept thicker layers (threedimensional biofunctionalization) such as porous materials of high specific area that lead to large increase of signal detection. This didactical overview introduces each step of the biofunctionalization with respect to the diversity of biological molecules, their accessibility and resistance to nonspecific adsorption at interfaces.
On SCADA control system command and response injection and intrusion detection

SCADA systems are widely used in critical infrastructure sectors, including electricity generation and distribution, oil and gas production and distribution, and water treatment and distribution. SCADA process control systems are typically isolated from the internet via firewalls. However, they may still be subject to illicit cyber penetrations and may be subject to cyber threats from disgruntled insiders. We have developed a set of command injection, data injection, and denial of service attacks which leverage the lack of authentication in many common control system communication protocols including MODBUS, DNP3, and EtherNET/IP. We used these exploits to aid in development of a neural network based intrusion detection system which monitors control system physical behavior to detect artifacts of command and response injection attacks. Finally, we present intrusion detection accuracy results for our neural network based IDS which includes input features derived from physical properties of the control system.
Study of Gamification Effectiveness in Online e- Learning Systems

Online distance e-learning systems allow introducing innovative methods in pedagogy, along with studying their effectiveness. Assessing the system effectiveness is based on analyzing the log files to track the studying time, the number of connections, and earned game bonus points. This study is based on an example of the online application for practical foreign language speaking skills training between random users, which select the role of a teacher or a student on their own. The main features of the developed system include pre-defined synchronized teaching and learning materials displayed for both participants, along with user motivation by means of gamification. The actual percentage of successful connects between specifically unmotivated and unfamiliar with each other users was measured. The obtained result can be used for gauging the developed system success and the proposed teaching methodology in general. elearning; gamification; marketing; monetization; viral marketing; virality
ACTION-DEPENDENT FACTORIZED BASELINES

Policy gradient methods have enjoyed great success in deep reinforcement learning but suffer from high variance of gradient estimates. The high variance problem is particularly exasperated in problems with long horizons or high-dimensional action spaces. To mitigate this issue, we derive a bias-free action-dependent baseline for variance reduction which fully exploits the structural form of the stochastic policy itself and does not make any additional assumptions about the MDP. We demonstrate and quantify the benefit of the action-dependent baseline through both theoretical analysis as well as numerical results, including an analysis of the suboptimality of the optimal state-dependent baseline. The result is a computationally efficient policy gradient algorithm, which scales to high-dimensional control problems, as demonstrated by a synthetic 2000-dimensional target matching task. Our experimental results indicate that action-dependent baselines allow for faster learning on standard reinforcement learning benchmarks and highdimensional hand manipulation and synthetic tasks. Finally, we show that the general idea of including additional information in baselines for improved variance reduction can be extended to partially observed and multi-agent tasks.
Internet of Things Forensics: Challenges and approaches

The scope of this paper is two-fold: firstly it proposes the application of a 1-2-3 Zones approach to Internet of Things (IoT)-related Digital Forensics (DF) investigations. Secondly, it introduces a Next-Best-Thing Triage (NBT) Model for use in conjunction with the 1-2-3 Zones approach where necessary and vice versa. These two `approaches' are essential for the DF process from an IoT perspective: the atypical nature of IoT sources of evidence (i.e. Objects of Forensic Interest - OOFI), the pervasiveness of the IoT environment and its other unique attributes - and the combination of these attributes - dictate the necessity for a systematic DF approach to incidents. The two approaches proposed are designed to serve as a beacon to incident responders, increasing the efficiency and effectiveness of their IoT-related investigations by maximizing the use of the available time and ensuring relevant evidence identification and acquisition. The approaches can also be applied in conjunction with existing, recognised DF models, methodologies and frameworks.