nils-herrmann/clean_scirepeval huggingface dataset:
Muscle activation patterns while lifting stable and unstable loads on stable and unstable surfaces.

In an attempt to mimic everyday activities that are performed in 3-dimensional environments, exercise programs have been designed to integrate training of the trunk muscles with training of the extremities. Many believe that the most effective way to recruit the core stabilizing muscles is to execute traditional exercise movements on unstable surfaces. However, physical activity is rarely performed with a stable load on an unstable surface; usually, the surface is stable, and the external resistance is not. The purpose of this study was to evaluate muscle activity of the prime movers and core stabilizers while lifting stable and unstable loads on stable and unstable surfaces during the seated overhead shoulder press exercise. Thirty resistance-trained subjects performed the shoulder press exercise for 3 sets of 3 repetitions under 2 load (barbell and dumbbell) and 2 surface (exercise bench and Swiss ball) conditions at a 10 repetition maximum relative intensity. Surface electromyography (EMG) measured muscle activity for 8 muscles (anterior deltoid, middle deltoid, trapezius, triceps brachii, rectus abdominis, external obliques, and upper and lower erector spinae). The average root mean square of the EMG signal was calculated for each condition. The results showed that as the instability of the exercise condition increased, the external load decreased. Triceps activation increased with external resistance, where the barbell/bench condition had the greatest EMG activation and the dumbbell/Swiss ball condition had the least. The upper erector spinae had greater muscle activation when performing the barbell presses on the Swiss ball vs. the bench. The findings provide little support for training with a lighter load using unstable loads or unstable surfaces.
Cs229 Problem Set #4 Cs 229, Autumn 2014 Problem Set #4: Unsupervised Learning & Rl

Notes: (1) These questions require thought, but do not require long answers. Please be as concise as possible. (2) If you have a question about this homework, we encourage you to post your question on our Piazza forum, at https://piazza.com/stanford/fall2014/cs229. (3) If you missed the first lecture or are unfamiliar with the collaboration or honor code policy, please read the policy on Handout #1 (available from the course website) before starting work. (4) For problems that require programming, please include in your submission a printout of your code (with comments) and any figures that you are asked to plot. (5) If you are an on-campus (non-SCPD) student, please print, fill out, and include a copy of the cover sheet (enclosed as the final page of this document), and include the cover sheet as the first page of your submission. as a single PDF file under 20MB in size. If you have trouble submitting online, you can also email your submission to cs229-qa@cs.stanford.edu. However, we strongly recommend using the website submission method as it will provide confirmation of submission, and also allow us to track and return your graded homework to you more easily. If you are scanning your document by cellphone, please check the Piazza forum for recommended cellphone scanning apps and best practices.
Integrated waste management--looking beyond the solid waste horizon.

Waste as a management issue has been evident for over four millennia. Disposal of waste to the biosphere has given way to thinking about, and trying to implement, an integrated waste management approach. In 1996 the United Nations Environmental Programme (UNEP) defined 'integrated waste management' as 'a framework of reference for designing and implementing new waste management systems and for analysing and optimising existing systems'. In this paper the concept of integrated waste management as defined by UNEP is considered, along with the parameters that constitute integrated waste management. The examples used are put into four categories: (1) integration within a single medium (solid, aqueous or atmospheric wastes) by considering alternative waste management options, (2) multi-media integration (solid, aqueous, atmospheric and energy wastes) by considering waste management options that can be applied to more than one medium, (3) tools (regulatory, economic, voluntary and informational) and (4) agents (governmental bodies (local and national), businesses and the community). This evaluation allows guidelines for enhancing success: (1) as experience increases, it is possible to deal with a greater complexity; and (2) integrated waste management requires a holistic approach, which encompasses a life cycle understanding of products and services. This in turn requires different specialisms to be involved in the instigation and analysis of an integrated waste management system. Taken together these advance the path to sustainability.
Advanced Persistent Threat Detection using Malware Infection

APT (Advanced Persistent Threat) is a genuine risk to the Internet. With the help of APT malware, attackers can remotely control infected machine and steal the personal information. DNS is well known for malware to find command and control (C&C) servers. The proposed novel system placed at the network departure guide that points toward effectively and efficiently detect APT malware infections based on malicious DNS and traffic analysis. To detect suspicious APT malware C&C domains the system utilizes malicious DNS analysis method, and afterward analyse the traffic of the comparing suspicious IP utilizing anomaly-based and signature based detection innovation. There are separated features in view of big data to describe properties of malware-related DNS. This manufactured a reputation engine to compute a score for an IP address by utilizing these elements vector together.
Equations for the estimation of strong ground motions from shallow crustal earthquakes using data from Europe and the Middle East : Vertical peak ground acceleration and spectral acceleration

This article presents equations for the estimation of vertical strong ground motions caused by shallow crustal earthquakes with magnitudes Mw ≥ 5 and distance to the surface projection of the fault less than100 km. These equations were derived by weighted regression analysis, used to remove observed magnitude-dependent variance, on a set of 595 strong-motion records recorded in Europe and the Middle East. Coefficients are included to model the effect of local site effects and faulting mechanism on the observed ground motions. The equations include coefficients to model the observed magnitude-dependent decay rate. The main findings of this study are that: short-period ground motions from small and moderate magnitude earthquakes decay faster than the commonly assumed 1/r, the average effect of differing faulting mechanisms is similar to that observed for horizontal motions and is not large and corresponds to factors between0.7 (normal and odd) and 1.4 (thrust) with respect to strike-slip motions and that the average long-period amplification caused by soft soil deposits is about 2.1 over those on rock sites.
Segmentation-free Vehicle License Plate Recognition using ConvNet-RNN

While vehicle license plate recognition (VLPR) is usually done with a sliding window approach, it can have limited performance on datasets with characters that are of variable width. This can be solved by hand-crafting algorithms to prescale the characters. While this approach can work fairly well, the recognizer is only aware of the pixels within each detector window, and fails to account for other contextual information that might be present in other parts of the image. A sliding window approach also requires training data in the form of presegmented characters, which can be more difficult to obtain. In this paper, we propose a unified ConvNet-RNN model to recognize real-world captured license plate photographs. By using a Convolutional Neural Network (ConvNet) to perform feature extraction and using a Recurrent Neural Network (RNN) for sequencing, we address the problem of sliding window approaches being unable to access the context of the entire image by feeding the entire image as input to the ConvNet. This has the added benefit of being able to perform end-to-end training of the entire model on labelled, full license plate images. Experimental results comparing the ConvNet-RNN architecture to a sliding window-based approach shows that the ConvNet-RNN architecture performs significantly better. Vehicle license plate recognition, end-to-end recognition, ConvNet-RNN, segmentation-free recognition
Aspect Level Sentiment Classification with Attention-over-Attention Neural Networks

Aspect-level sentiment classification aims to identify the sentiment expressed towards some aspects given context sentences. In this paper, we introduce an attention-over-attention (AOA) neural network for aspect level sentiment classification. Our approach models aspects and sentences in a joint way and explicitly captures the interaction between aspects and context sentences. With the AOA module, our model jointly learns the representations for aspects and sentences, and automatically focuses on the important parts in sentences. Our experiments on laptop and restaurant datasets demonstrate our approach outperforms previous LSTM-based architectures.
Evaluating Student Satisfaction with Blended Learning in a Gender-Segregated Environment

Blended learning combines multiple delivery media that are designed to complement each other and promote learning and application-learned behavior (Smith & Dillon, 1999). This article reports on a study conducted in the College of Information Technology to evaluate levels of student' satisfaction with blended learning. The particular blend of learning modalities used at the college combines an equal balance of traditional face-to-face and videoconference learning, complemented with the use of a learning management system (Moodle).
Leaner and greener analysis of cannabinoids

There is an explosion in the number of labs analyzing cannabinoids in marijuana (Cannabis sativa L., Cannabaceae) but existing methods are inefficient, require expert analysts, and use large volumes of potentially environmentally damaging solvents. The objective of this work was to develop and validate an accurate method for analyzing cannabinoids in cannabis raw materials and finished products that is more efficient and uses fewer toxic solvents. An HPLC-DAD method was developed for eight cannabinoids in cannabis flowers and oils using a statistically guided optimization plan based on the principles of green chemistry. A single-laboratory validation determined the linearity, selectivity, accuracy, repeatability, intermediate precision, limit of detection, and limit of quantitation of the method. Amounts of individual cannabinoids above the limit of quantitation in the flowers ranged from 0.02 to 14.9% w/w, with repeatability ranging from 0.78 to 10.08% relative standard deviation. The intermediate precision determined using HorRat ratios ranged from 0.3 to 2.0. The LOQs for individual cannabinoids in flowers ranged from 0.02 to 0.17% w/w. This is a significant improvement over previous methods and is suitable for a wide range of applications including regulatory compliance, clinical studies, direct patient medical services, and commercial suppliers.
GPU Kernels for Block-Sparse Weights

We’re releasing highly optimized GPU kernels for an underexplored class of neural network architectures: networks with block-sparse weights. The kernels allow for efficient evaluation and differentiation of linear layers, including convolutional layers, with flexibly configurable block-sparsity patterns in the weight matrix. We find that depending on the sparsity, these kernels can run orders of magnitude faster than the best available alternatives such as cuBLAS. Using the kernels we improve upon the state-of-the-art in text sentiment analysis and generative modeling of text and images. By releasing our kernels in the open we aim to spur further advancement in model and algorithm design.
The screens culture: impact on ADHD

Children's use of electronic media, including Internet and video gaming, has increased dramatically to an average in the general population of roughly 3 h per day. Some children cannot control their Internet use leading to increasing research on "internet addiction." The objective of this article is to review the research on ADHD as a risk factor for Internet addiction and gaming, its complications, and what research and methodological questions remain to be addressed. The literature search was done in PubMed and Psychinfo, as well as by hand. Previous research has demonstrated rates of Internet addiction as high as 25% in the population and that it is addiction more than time of use that is best correlated with psychopathology. Various studies confirm that psychiatric disorders, and ADHD in particular, are associated with overuse, with severity of ADHD specifically correlated with the amount of use. ADHD children may be vulnerable since these games operate in brief segments that are not attention demanding. In addition, they offer immediate rewards with a strong incentive to increase the reward by trying the next level. The time spent on these games may also exacerbate ADHD symptoms, if not directly then through the loss of time spent on more developmentally challenging tasks. While this is a major issue for many parents, there is no empirical research on effective treatment. Internet and off-line gaming overuse and addiction are serious concerns for ADHD youth. Research is limited by the lack of measures for youth or parents, studies of children at risk, and studies of impact and treatment.
The entrepreneur ' s business model : toward a unified perspective

Highly emphasized in entrepreneurial practice, business models have received limited attention from researchers. No consensus exists regarding the definition, nature, structure, and evolution of business models. Still, the business model holds promise as a unifying unit of analysis that can facilitate theory development in entrepreneurship. This article synthesizes the literature and draws conclusions regarding a number of these core issues. Theoretical underpinnings of a firm's business model are explored. A sixcomponent framework is proposed for characterizing a business model, regardless of venture type. These components are applied at three different levels. The framework is illustrated using a successful mainstream company. Suggestions are made regarding the manner in which business models might be expected to emerge and evolve over time. a c Purchase Export
SMS Spam Detection using Machine Learning Approach

Over recent years, as the popularity of mobile phone devices has increased, Short Message Service (SMS) has grown into a multi-billion dollars industry. At the same time, reduction in the cost of messaging services has resulted in growth in unsolicited commercial advertisements (spams) being sent to mobile phones. In parts of Asia, up to 30% of text messages were spam in 2012. Lack of real databases for SMS spams, short length of messages and limited features, and their informal language are the factors that may cause the established email filtering algorithms to underperform in their classification. In this project, a database of real SMS Spams from UCI Machine Learning repository is used, and after preprocessing and feature extraction, different machine learning techniques are applied to the database. Finally, the results are compared and the best algorithm for spam filtering for text messaging is introduced. Final simulation results using 10-fold cross validation shows the best classifier in this work reduces the overall error rate of best model in original paper citing this dataset by more than half.
Underwater optical communications systems. Part 2: basic design considerations

Acoustic systems may provide suitable underwater communications because sound propagates well in water. However, the maximum data transmission rates of these systems in shallow littoral waters are ~10 kilobits per second (kbps) which may be achieved only at ranges of less than 100 m. Although underwater (u/w) wireless optical communications systems can have even shorter ranges due to greater attenuation of light propagating through water, they may provide higher bandwidth (up to several hundred kbps) communications as well as covertness. To exploit these potential advantages, we consider the basic design issues for u/w optical communications systems in this paper. In addition to the basic physics of u/w optical communications with environmental noise, we consider system performance with some state-of-the-art commercial off-the-shelf (COTS) components, which have promise for placing u/w optical communications systems in a small package with low power consumption and weight. We discuss light sources which show promise for u/w optical transmitters such as laser diodes (LDs) and light emitting diodes (LEDs). Laser diodes with their output frequency shifted into the 500- to 650-nm range can emit more energy per pulse than LEDs but are more expensive. Currently, LEDs emit substantial amounts of light and are typically very inexpensive. Also, COTS photodiodes can be used as detectors which can respond to pulses several nanoseconds wide. Transmitter broadcast angles and detector fields of view (FOVs) with pointing considerations are discussed. If the transmitter broadcast angle and the detector FOV are both narrow, the signal-to-noise ratio (SNR) of the received pulse is higher but the pointing accuracy of transmitter and receiver is critical. If, however, the transmitter broadcast angle and/or the detector FOV is wide, pointing is less critical but SNR is lower and some covertness may be lost. The propagation of the transmitted light in various clear oceanic and turbid coastal water types is considered with range estimates for some COTS light sources and detectors. We also consider the effects of environmental noise such as background solar radiation, which typically limits performance of these systems
Predicting Movie Success and Academy Awards through Sentiment and Social Network Analysis

This paper introduces a new Web mining approach that combines social network analysis and automatic sentiment analysis. We show how weighting the forum posts of the contributors according to their network position allow us to predict trends and real world events in the movie business. To test our approach we conducted two experiments analyzing online forum discussions on the Internet movie database (IMDb) by examining the correlation of the social network structure with external metrics such as box office revenue and Oscar Awards. We find that discussion patterns on IMDb predict Academy Awards nominations and box office success. Two months before the Oscars were given we were able to correctly predict nine Oscar nominations. We also found that forum contributions correlated with box office success of 20 top grossing movies of 2006.
Adaptive radiation: contrasting theory with data.

Biologists have long been fascinated by the exceptionally high diversity displayed by some evolutionary groups. Adaptive radiation in such clades is not only spectacular, but is also an extremely complex process influenced by a variety of ecological, genetic, and developmental factors and strongly dependent on historical contingencies. Using modeling approaches, we identify 10 general patterns concerning the temporal, spatial, and genetic/morphological properties of adaptive radiation. Some of these are strongly supported by empirical work, whereas for others, empirical support is more tentative. In almost all cases, more data are needed. Future progress in our understanding of adaptive radiation will be most successful if theoretical and empirical approaches are integrated, as has happened in other areas of evolutionary biology.
The Logical Execution Time Paradigm

Since its introduction in 2000 in the time-triggered programming language Giotto, the Logical Execution Time (LET) paradigm has evolved from a highly controversial idea to a well-understood principle of real-time programming. This chapter provides an easy-to-read overview of LET programming languages and runtime systems as well as some LET-inspired models of computation. The presentation is intuitive, by example, citing the relevant literature including more formal treatment of the material for reference.
How to Systematically Classify Computer Security Intrusions

This paper presents a class@cation of intrusions with respect to technique as well as to result. The taxonomy is intended to be a step on the road to an established taxonomy of intrusions for use in incident reporting, statistics, warning bulletins, intrusion detection systems etc. Unlike previous schemes, it takes the viewpoint of the system owner and should therefore be suitable to a wider community than that of system developers and vendors only. It is based on data from a tzalistic intrusion experiment, a fact that supports the practical applicability of the scheme. The paper also discusses general aspects of classification, and introduces a concept called dimension. After having made a broad survey of previous work in thejield, we decided to base our classification of intrusion techniques on a scheme proposed by Neumann and Parker in I989 and to further refine relevant parts of their scheme. Our classification of intrusion results is derived from the traditional three aspects of computer security: confidentiality, availability and integrity.
Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning

The abundant spatial and contextual information provided by the advanced remote sensing technology has facilitated subsequent automatic interpretation of the optical remote sensing images (RSIs). In this paper, a novel and effective geospatial object detection framework is proposed by combining the weakly supervised learning (WSL) and high-level feature learning. First, deep Boltzmann machine is adopted to infer the spatial and structural information encoded in the low-level and middle-level features to effectively describe objects in optical RSIs. Then, a novel WSL approach is presented to object detection where the training sets require only binary labels indicating whether an image contains the target object or not. Based on the learnt high-level features, it jointly integrates saliency, intraclass compactness, and interclass separability in a Bayesian framework to initialize a set of training examples from weakly labeled images and start iterative learning of the object detector. A novel evaluation criterion is also developed to detect model drift and cease the iterative learning. Comprehensive experiments on three optical RSI data sets have demonstrated the efficacy of the proposed approach in benchmarking with several state-of-the-art supervised-learning-based object detection approaches.
Security Evaluation of the Cyber Networks Under Advanced Persistent Threats

Advanced persistent threats (APTs) pose a grave threat to cyberspace, because they deactivate all the conventional cyber defense mechanisms. This paper addresses the issue of evaluating the security of the cyber networks under APTs. For this purpose, a dynamic model capturing the APT-based cyber-attack-defense processes is proposed. Theoretical analysis shows that this model admits a globally stable equilibrium. On this basis, a new security metric known as the equilibrium security is suggested. The impact of several factors on the equilibrium security is revealed through theoretical analysis or computer simulation. These findings contribute to the development of feasible security solutions against APTs.