nils-herrmann/clean_scirepeval huggingface dataset:
Analysis of Deep Convolutional Neural Network Architectures

In computer vision many tasks are solved using machine learning. In the past few years, state of the art results in computer vision have been achieved using deep learning. Deeper machine learning architectures are better capable in handling complex recognition tasks, compared to previous more shallow models. Many architectures for computer vision make use of convolutional neural networks which were modeled after the visual cortex. Currently deep convolutional neural networks are the state of the art in computer vision. Through a literature survey an overview is given of the components in deep convolutional neural networks. The role and design decisions for each of the components is presented, and the difficulties involved in training deep neural networks are given. To improve deep learning architectures an analysis is given of the activation values in four different architectures using various activation functions. Current state of the art classifiers use dropout, max-pooling as well as the maxout activation function. New components may further improve the architecture by providing a better solution for the diminishing gradient problem.
A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity

The present study describes the development of a triaxial accelerometer (TA) and a portable data processing unit for the assessment of daily physical activity. The TA is composed of three orthogonally mounted uniaxial piezoresistive accelerometers and can be used to register accelerations covering the amplitude and frequency ranges of human body acceleration. Interinstrument and test-retest experiments showed that the offset and the sensitivity of the TA were equal for each measurement direction and remained constant on two measurement days. Transverse sensitivity was significantly different for each measurement direction, but did not influence accelerometer output (<3% of the sensitivity along the main axis). The data unit enables the on-line processing of accelerometer output to a reliable estimator of physical activity over eight-day periods. Preliminary evaluation of the system in 13 male subjects during standardized activities in the laboratory demonstrated a significant relationship between accelerometer output and energy expenditure due to physical activity, the standard reference for physical activity (r=0.89). Shortcomings of the system are its low sensitivity to sedentary activities and the inability to register static exercise. The validity of the system for the assessment of normal daily physical activity and specific activities outside the laboratory should be studied in free-living subjects.
VT: An Expert Elevator Designer That Uses Knowledge-Based Backtracking

system for handling the design of elevator systems that is currently in use at Westinghouse Elevator Company Although VT tries to postpone each decision in creating a design until all information that constrains the decision is known, for many decisions this postponement is not possible In these cases, VT uses the strategy of constructing a plausible approximation and successively refining it VT uses domain-specific knowledge to guide its backtracking search for successful refinements The VT architecture provides the basis for a knowledge representation that is used by SALT, an automated knowledge-acquisition tool SALT was used to build VT and provides an analysis of VT’s knowledge base to assess its potential for convergence on a solution VT: An Expert Elevator Designer That Uses Knowledge-Based Backtracking Sandra Marcus, Jeffrey Stout, John McDermott
Data privacy through optimal k-anonymization

Data de-identification reconciles the demand for release of data for research purposes and the demand for privacy from individuals. This paper proposes and evaluates an optimization algorithm for the powerful de-identification procedure known as k-anonymization. A k-anonymized dataset has the property that each record is indistinguishable from at least k - 1 others. Even simple restrictions of optimized k-anonymity are NP-hard, leading to significant computational challenges. We present a new approach to exploring the space of possible anonymizations that tames the combinatorics of the problem, and develop data-management strategies to reduce reliance on expensive operations such as sorting. Through experiments on real census data, we show the resulting algorithm can find optimal k-anonymizations under two representative cost measures and a wide range of k. We also show that the algorithm can produce good anonymizations in circumstances where the input data or input parameters preclude finding an optimal solution in reasonable time. Finally, we use the algorithm to explore the effects of different coding approaches and problem variations on anonymization quality and performance. To our knowledge, this is the first result demonstrating optimal k-anonymization of a non-trivial dataset under a general model of the problem.
Compositional Imitation Learning: Explaining and executing one task at a time

We introduce a framework for Compositional Imitation Learning and Execution (CompILE) of hierarchically-structured behavior. CompILE learns reusable, variable-length segments of behavior from demonstration data using a novel unsupervised, fully-differentiable sequence segmentation module. These learned behaviors can then be re-composed and executed to perform new tasks. At training time, CompILE auto-encodes observed behavior into a sequence of latent codes, each corresponding to a variable-length segment in the input sequence. Once trained, our model generalizes to sequences of longer length and from environment instances not seen during training. We evaluate our model in a challenging 2D multi-task environment and show that CompILE can find correct task boundaries and event encodings in an unsupervised manner without requiring annotated demonstration data. Latent codes and associated behavior policies discovered by CompILE can be used by a hierarchical agent, where the high-level policy selects actions in the latent code space, and the low-level, task-specific policies are simply the learned decoders. We found that our agent could learn given only sparse rewards, where agents without task-specific policies struggle.
Coil Design and Shielding Methods for a Magnetic Resonant Wireless Power Transfer System

In this paper, we introduce the basic principles of wireless power transfer using magnetic field resonance and describe techniques for the design of a resonant magnetic coil, the formation of a magnetic field distribution, and electromagnetic field (EMF) noise suppression methods. The experimental results of wireless power transfer systems in consumer electronics applications are discussed in terms of issues related to their efficiency and EMF noise. Furthermore, we present a passive shielding method and a magnetic field cancellation method using a reactive resonant current loop and the utilization of these methods in an online electric vehicle (OLEV) system, in which an OLEV green transportation bus system absorbs wireless power from power cables underneath the road surface with only a minimal battery capacity.
Maternal gestational diabetes, birth weight, and adolescent obesity.

Obesity increases risk of many adverse outcomes, but its early origins are obscure. Gestational diabetes mellitus (GDM) reflects a metabolically altered fetal environment associated with high birth weight, itself associated with later obesity. Previous studies of GDM and offspring obesity, however, have been few and conflicting. The objectives of this study were to examine associations of birth weight and GDM with adolescent body mass index (BMI) and to determine the extent to which the effect of GDM is explained by its influence on birth weight or by maternal adiposity.



We conducted a survey of 7981 girls and 6900 boys, 9 to 14 years of age, who are participants in the Growing Up Today Study, a US nationwide study of diet, activity, and growth. In 1996, participants reported height, weight, diet, activity, and other variables by self-administered mailed questionnaire. We linked these data with information reported by their mothers, participants in the Nurses' Health Study II, including GDM, height, current weight, and child's birth weight. We excluded births <34 weeks' gestation and mothers who had preexisting diabetes. We defined overweight as BMI (kg/m(2)) >95th percentile, and at risk for overweight as 85th to 95th percentile, for age and gender from US national data.



Mean birth weight was 3.4 kg for girls and 3.6 kg for boys. Among the 465 subjects whose mothers had GDM, 17.1% were at risk for overweight and 9.7% were overweight in early adolescence. In the group without maternal diabetes, these estimates were 14.2% and 6.6%, respectively. In multiple logistic regression analysis, controlling for age, gender, and Tanner stage, the odds ratio for adolescent overweight for each 1-kg increment in birth weight was 1.4 (95% confidence interval: 1.2-1.6). Adjustment for physical activity, television watching, energy intake, breastfeeding duration, mother's BMI, and other maternal and family variables reduced the estimate to 1.3 (1.1-1.5). For offspring of mothers with GDM versus no diabetes, the odds ratio for adolescent overweight was 1.4 (1.1-2.0), which was unchanged after controlling for energy balance and socioeconomic factors. Adjustment for birth weight slightly attenuated the estimate (1.3; 0.9-1.9); adjustment for maternal BMI reduced the odds ratio to 1.2 (0.8-1.7).



Higher birth weight predicted increased risk of overweight in adolescence. Having been born to a mother with GDM was also associated with increased adolescent overweight. However, the effect of GDM on offspring obesity seemed only partially explained by its influence on birth weight, and adjustment for mother's own BMI attenuated the GDM associations. Our results only modestly support a causal role of altered maternal-fetal glucose metabolism in the genesis of obesity in the offspring. Alternatively, GDM may program risk for a postnatal insult leading to obesity, or it may merely be a risk marker, not in the causal pathway.
Videogame Addiction and its Treatment

For many, the concept of videogame addiction seems far-fetched, particularly if their concepts and definitions of addiction involve the taking of drugs. This paper overviews the small, but growing area of videogame addiction and then examines the treatment options available for those affected by excessive videogame playing. An overview of the available empirical literature appears to indicate that adverse effects are likely to affect only a relatively small subgroup of players and that frequent players are the most at-risk for developing problems. Worldwide, there are relatively few practitioners that specialise in the treatment of videogame addiction and this may be because there are so few players who are genuinely addicted to playing videogames. However, the Internet may be facilitating excessive online game playing as evidenced by the increasing number of specialist addiction treatment clinics for online videogame addiction. This paper overviews the various approaches that have been used as an intervention in treating videogame addicts, including online support groups, 12-step programmes, behavioural and cognitive-behavioural therapies, and motivational interviewing.
Counterintuitive Behavior of Social Systems

This paper addresses several social concerns: population trends; quality of urban life; policies for urban growth; and the unexpected, ineffective, or detrimental results often generated by government programs. Society becomes frustrated as repeated attacks on deficiencies in social systems lead only to worse symptoms. Legislation is debated and passed with great hope, but many programs prove to be ineffective. Results are often far short of expectations. Because dynamic behavior of social systems is not understood, government programs often cause exactly the reverse of desired results. The field of system dynamics now can explain how such contrary results happen. Fundamental reasons cause people to misjudge behavior of social systems. Orderly processes in creating human judgment and intuition lead people to wrong decisions when faced with complex and highly interacting systems. Until we reach a much better public understanding of social systems, attempts to develop corrective programs for social troubles will continue to be disappointing. This paper cautions against continuing to depend on the same past approaches that have led to present feelings of frustration. New methods developed over the last 30 years will lead to a better understanding of social systems and thereby to more effective policies for guiding the future. 1 Updated March, 1995. This paper was first copyrighted  It is based on testimony for the Subcommittee on Urban Growth of the Committee on Banking and Currency, U.S. House of Representatives, on October 7, 1970. The original text appeared in the January, 1971, issue of the Technology Review published by the Alumni Association of the Massachusetts Institute of Technology. All figures are taken from World Dynamics by Jay W. Forrester, Productivity Press, Portland, Oregon. 2 Germeshausen Professor Emeritus and Senior Lecturer, Massachusetts Institute of Technology, Cambridge, MA, USA
The effect of website features in online relationship marketing: A case of online hotel booking

The primary objective of this research is to develop a theory-based model of utilitarian and hedonic website features, customer commitment, trust, and e-loyalty in an online hotel booking context.  Modeling was deployed to test research hypotheses. Findings highlight the importance of creating loyalty by focusing on both hedonic and utilitarian features. Affective commitment is more influenced by hedonic features whereas calculative commitment is driven by utilitarian ones. Both commitment dimensions sway customers’ trust towards the online vendor and trust is an important antecedent of e-loyalty. Findings confirm that web design features are important for online relationship marketing. Both commitment dimensions were found to be precursors of trust whereas affective commitment is the precursor of e-loyalty.
Preventing Cell Phone Intrusion and Theft using Biometrics

Most cell phones use a password, PIN, or visual pattern to secure the phone. With these types of security methods being used, there is much vulnerability. Another alternative is biometric authentication. Biometric security systems have been researched for many years. Some mobile manufacturers have implemented fingerprint scanners into their phones, such as the old Fujitsu F505i [7] and the current Motorola Atrix. Since theft of cell phones is becoming more common every day, there is a real need for a security system that not only protects the data, but the phone itself. It is proposed through this research that a biometric security system be the alternative to knowledge-based and password-based authentication. Furthermore, a device dongle must be implemented into this infrastructure to establish a reliable security system that deters theft for the majority; biometrics alone is not sufficient. Cell phones need power and must be charged almost daily. A biometric phone charger that acts as a dongle with a solid state relay, will be presented as a viable solution to theft in this research. Additionally, it will be shown through the results of this research that a system dependant only on biometrics is unreliable and unsecure. Essentially, a mobile security system that combines biometrics with dongle technology is believed to be the ideal solution for limiting the black market of stolen cell phones; without the biometric charger/dongle, the stolen cell phone would be rendered useless.
BigSR: an empirical study of real-time expressive RDF stream reasoning on modern Big Data platforms

The trade-off between language expressiveness and system scalability (E&S) is a well-known problem in RDF stream reasoning. Higher expressiveness supports more complex reasoning logic, however, it may also hinder system scalability. Current research mainly focuses on logical frameworks suitable for stream reasoning as well as the implementation and the evaluation of prototype systems. These systems are normally developed in a centralized setting which suffer from inherent limited scalability, while an in-depth study of applying distributed solutions to cover E&S is still missing. In this paper, we aim to explore the feasibility of applying modern distributed computing frameworks to meet E&S all together. To do so, we first propose BigSR, a technical demonstrator that supports a positive fragment of the LARS framework. For the sake of generality and to cover a wide variety of use cases, BigSR relies on the two main execution models adopted by major distributed execution frameworks: Bulk Synchronous Processing (BSP) and Record-at-A-Time (RAT). Accordingly, we implement BigSR on top of Apache Spark Streaming (BSP model) and Apache Flink (RAT model). In order to conclude on the impacts of BSP and RAT on E&S, we analyze the ability of the two models to support distributed stream reasoning and identify several types of use cases characterized by their levels of support. This classification allows for quantifying the E&S trade-off by assessing the scalability of each type of use case w.r.t. its level of expressiveness. Then, we conduct a series of experiments with 15 queries from 4 different datasets. Our experiments show that BigSR over both BSP and RAT generally scales up to high throughput beyond million-triples per second (with or without recursion), and RAT attains sub-millisecond delay for stateless query operators.
Cloud Federation: Effects of Federated Compute Resources on Quality of Service and Cost*

Cloud Federation is one concept to confront challenges that still persist in Cloud Computing, such as vendor lock-in or compliance requirements. The lack of a standardized meaning for the term Cloud Federation has led to multiple conflicting definitions and an unclear prospect of its possible benefits. Taking a client-side perspective on federated compute services, we analyse how choosing a certain federation strategy affects Quality of Service and cost of the resulting service or application. Based on a use case, we experimentally prove our analysis to be correct and describe the different trade-offs that exist within each of the strategies.
Deep Neural Network Approaches to Speaker and Language Recognition

The impressive gains in performance obtained using deep neural networks (DNNs) for automatic speech recognition (ASR) have motivated the application of DNNs to other speech technologies such as speaker recognition (SR) and language recognition (LR). Prior work has shown performance gains for separate SR and LR tasks using DNNs for direct classification or for feature extraction. In this work we present the application of single DNN for both SR and LR using the 2013 Domain Adaptation Challenge speaker recognition (DAC13) and the NIST 2011 language recognition evaluation (LRE11) benchmarks. Using a single DNN trained for ASR on Switchboard data we demonstrate large gains on performance in both benchmarks: a 55% reduction in EER for the DAC13 out-of-domain condition and a 48% reduction in Cavg on the LRE11 30 s test condition. It is also shown that further gains are possible using score or feature fusion leading to the possibility of a single i-vector extractor producing state-of-the-art SR and LR performance.
Motion area based exposure fusion algorithm for ghost removal in high dynamic range video generation

High dynamic range (HDR) image is becoming prevalent for its superiority in recording details of the real-world scene. A conventional exposure fusion method can be utilized to generate a HDR image from a set of differently exposed low dynamic range (LDR) images according to their weight maps. However, this method causes ghosts in the HDR image if there are moving objects or the background changes in the LDR image sequence. Thus, conventional exposure fusion method is only suitable for HDR image generation, not for HDR video generation. A motion area based exposure fusion algorithm is therefore proposed to remove ghosts in HDR video generation. First, the motion areas are detected using block matching based motion estimation method. Then those areas are combined as an additional measurement to change values of original weight maps. The subjective and objective experimental results show ghost-free and highly detailed exposure fused image sequences from HDR videos.
Repeated observation of breast tumor subtypes in independent gene expression data sets.

Characteristic patterns of gene expression measured by DNA microarrays have been used to classify tumors into clinically relevant subgroups. In this study, we have refined the previously defined subtypes of breast tumors that could be distinguished by their distinct patterns of gene expression. A total of 115 malignant breast tumors were analyzed by hierarchical clustering based on patterns of expression of 534 "intrinsic" genes and shown to subdivide into one basal-like, one ERBB2-overexpressing, two luminal-like, and one normal breast tissue-like subgroup. The genes used for classification were selected based on their similar expression levels between pairs of consecutive samples taken from the same tumor separated by 15 weeks of neoadjuvant treatment. Similar cluster analyses of two published, independent data sets representing different patient cohorts from different laboratories, uncovered some of the same breast cancer subtypes. In the one data set that included information on time to development of distant metastasis, subtypes were associated with significant differences in this clinical feature. By including a group of tumors from BRCA1 carriers in the analysis, we found that this genotype predisposes to the basal tumor subtype. Our results strongly support the idea that many of these breast tumor subtypes represent biologically distinct disease entities.
#Healthy Selfies: Exploration of Health Topics on Instagram

Social media provides a complementary source of information for public health surveillance. The dominate data source for this type of monitoring is the microblogging platform Twitter, which is convenient due to the free availability of public data. Less is known about the utility of other social media platforms, despite their popularity.



This work aims to characterize the health topics that are prominently discussed in the image-sharing platform Instagram, as a step toward understanding how this data might be used for public health research.



The study uses a topic modeling approach to discover topics in a dataset of 96,426 Instagram posts containing hashtags related to health. We use a polylingual topic model, initially developed for datasets in different natural languages, to model different modalities of data: hashtags, caption words, and image tags automatically extracted using a computer vision tool.



We identified 47 health-related topics in the data (kappa=.77), covering ten broad categories: acute illness, alternative medicine, chronic illness and pain, diet, exercise, health care & medicine, mental health, musculoskeletal health and dermatology, sleep, and substance use. The most prevalent topics were related to diet (8,293/96,426; 8.6% of posts) and exercise (7,328/96,426; 7.6% of posts).



A large and diverse set of health topics are discussed in Instagram. The extracted image tags were generally too coarse and noisy to be used for identifying posts but were in some cases accurate for identifying images relevant to studying diet and substance use. Instagram shows potential as a source of public health information, though limitations in data collection and metadata availability may limit its use in comparison to platforms like Twitter.