nils-herrmann/clean_scirepeval huggingface dataset:
A comparative study of different machine learning methods on microarray gene expression data

Several classification and feature selection methods have been studied for the identification of differentially expressed genes in microarray data. Classification methods such as SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods have been used in recent studies. The accuracy of these methods has been calculated with validation methods such as v-fold validation. However there is lack of comparison between these methods to find a better framework for classification, clustering and analysis of microarray gene expression results. In this study, we compared the efficiency of the classification methods including; SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods. The v-fold cross validation was used to calculate the accuracy of the classifiers. Some of the common clustering methods including K-means, DBC, and EM clustering were applied to the datasets and the efficiency of these methods have been analysed. Further the efficiency of the feature selection methods including support vector machine recursive feature elimination (SVM-RFE), Chi Squared, and CSF were compared. In each case these methods were applied to eight different binary (two class) microarray datasets. We evaluated the class prediction efficiency of each gene list in training and test cross-validation using supervised classifiers. We presented a study in which we compared some of the common used classification, clustering, and feature selection methods. We applied these methods to eight publicly available datasets, and compared how these methods performed in class prediction of test datasets. We reported that the choice of feature selection methods, the number of genes in the gene list, the number of cases (samples) substantially influence classification success. Based on features chosen by these methods, error rates and accuracy of several classification algorithms were obtained. Results revealed the importance of feature selection in accurately classifying new samples and how an integrated feature selection and classification algorithm is performing and is capable of identifying significant genes.
Explicit Modelling of the Implicit Short Term User Preferences for Music Recommendation

Recommender systems are a key component of music sharing platforms, which suggest musical recordings a user might like. People often have implicit preferences while listening to music, though these preferences might not always be the same while they listen to music at different times. For example, a user might be interested in listening to songs of only a particular artist at some time, and the same user might be interested in the top-rated songs of a genre at another time. In this paper we try to explicitly model the short term preferences of the user with the help of Last.fm tags of the songs the user has listened to. With a session defined as a period of activity surrounded by periods of inactivity, we introduce the concept of a subsession, which is that part of the session wherein the preference of the user does not change much. We assume the user preference might change within a session and a session might have multiple subsessions. We use our modelling of the user preferences to generate recommendations for the next song the user might listen to. Experiments on the user listening histories taken from Last.fm indicate that this approach beats the present methodologies in predicting the next recording a user might listen to.
HotSpot: a dynamic compact thermal model at the processor-architecture level

This paperdescribesa thermal-modelingapproachthatis easyto useandcomputationally efficient for modelingthermaleffectsandthermal-management techniquesat theprocessor architecturelevel. Our approachis basedon modelingthermalbehavior of themicroprocessordie andits packageasa circuit of thermalresistancesandcapacitancesthat correspondto functionalblocksat the architecturelevel. This yields a simplecompactmodel, yet heatdissipationwithin all majorfunctionalblocksandtheheatflow amongblocksand throughthepackageareaccountedfor. Themodelis parameterized, boundary-andinitialconditionsindependent, andis derivedby a structureassemblyapproach.Thearchitecture communityhasdemonstratedgrowing interestin thermalmanagement, but currentlylacks a way to modelon-chiptemperaturesin a tractableway. Our modelcanbeusedfor initial explorationof thedesignspaceat thearchitecturelevel. Themodelcaneasilybeintegrated into popularpower/performancesimulators,canbeusedto determinehow thermalstressis correlatedto thearchitecture, andhow architecture-le vel designdecisionsinfluencethermal behavior andrelatedeffects.
Towards Efficient Learning of Optimal Spatial Bag-of-Words Representations

Spatial Pyramid Matching (SPM) assumes that the spatial Bag-of-Words (BoW) representation is independent of data. However, evidence has shown that the assumption usually leads to a suboptimal representation. In this paper, we propose a novel method called Jensen-Shannon (JS) Tiling to learn the BoW representation from data directly at the BoW level. The proposed JS Tiling is especially appropriate for large-scale datasets as it is orders of magnitude faster than existing methods, but with comparable or even better classification precision. Experimental results on four benchmarks including two TRECVID12 datasets validate that JS Tiling outperforms the SPM and the state-of-the-art methods. The runtime comparison demonstrates that selecting BoW representations by JS Tiling is more than 1,000 times faster than running classifiers. Besides, JS Tiling is an important component contributing to CMU Teams' final submission in TRECVID 2012 Multimedia Event Detection.
Using LSTM and GRU neural network methods for traffic flow prediction

Accurate and real-time traffic flow prediction is important in Intelligent Transportation System (ITS), especially for traffic control. Existing models such as ARMA, ARIMA are mainly linear models and cannot describe the stochastic and nonlinear nature of traffic flow. In recent years, deep-learning-based methods have been applied as novel alternatives for traffic flow prediction. However, which kind of deep neural networks is the most appropriate model for traffic flow prediction remains unsolved. In this paper, we use Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) neural network (NN) methods to predict short-term traffic flow, and experiments demonstrate that Recurrent Neural Network (RNN) based deep learning methods such as LSTM and GRU perform better than auto regressive integrated moving average (ARIMA) model. To the best of our knowledge, this is the first time that GRU is applied to traffic flow prediction.
CUP: Comprehensive User-Space Protection for C/C++

Memory corruption vulnerabilities in C/C++ applications enable attackers to execute code, change data, and leak information. Current memory sanitizers do not provide comprehensive coverage of a program»s data. In particular, existing tools focus primarily on heap allocations with limited support for stack allocations and globals. Orthogonally, existing tools focus on the main executable with limited support for system libraries. Existing tools also suffer from both false positives and false negatives. We present Comprehensive User-Space Protection for C/C++, \sysname, an LLVM sanitizer that provides complete spatial and probabilistic temporal memory safety for C/C++ programs on 64-bit architectures (with a prototype implementation for x86\_64). \sysname uses a hybrid metadata scheme that supports all program data including globals, heap, or stack and maintains Application Binary Interface (ABI) compatibility. Existing approaches have false positives and 8%-25% false negatives on the NIST Juliet test suite. In contrast, \sysname has no false negatives or false positives. \sysname instruments all user-space code, including libc and other system libraries, removing these libraries from the trusted computing base. Supporting all of user space allows \sysname to treat a missed check as a failed check, leading to no false negatives for \sysname. The overhead introduced by \sysname is half that of the state-of-the-art full memory protection on benchmarks where both mechanisms run, and imposes 1.58x overhead when compared to baseline on all benchmarks. Consequently, \sysname is intended as a sanitizer for use by system developers, and to protect truly critical systems.
A model for play-based intervention for children with ADHD.

The importance of play in the social development of children is undisputed. Even though children with attention-deficit hyperactivity disorder (ADHD) experience serious social problems, there is limited research on their play. By integrating literature on ADHD with literature on play, we can postulate how play is influenced by the characteristics of ADHD. These postulations enabled us to propose a theoretical model (proposed model) to depict the interactive process between the characteristics of ADHD and factors that promote play. This paper presents the revised model and principles for intervention based on the results of a study investigating the play of children with ADHD (reported elsewhere).



We tested the proposed model in a study comparing two groups of children (n = 350) between the ages of 5 and 11 years. One group consisted of children diagnosed with ADHD (n = 112) paired with playmates (n = 112) who were typically developing; the control group consisted of typically developing children paired with typically developing playmates (n = 126). The Test of Playfulness was administered, and the model was revised in line with the findings.



The findings suggest difficulties in the social play and lack of interpersonal empathy in the play of children with ADHD. We draw on the revised model to propose preliminary principles for play-based interventions for children with ADHD. The principles emphasise the importance of capturing the motivation of children with ADHD, counteracting the effects of lack of interpersonal empathy, and considerations for including playmates in the intervention process.
Understanding Types of Cyberbullying in an Anonymous Messaging Application

The possibility of anonymity and lack of effective ways to identify inappropriate messages have resulted in a significant amount of online interaction data that attempt to harass, bully, or offend the recipient. In this work, we perform a preliminary linguistic study on messages exchanged using one such popular web/smartphone application—Sarahah, that allows friends to exchange messages anonymously. Since messages exchanged via Sarahah are private, we collect them when the recipient shares it on Twitter. We then perform an analysis of the different kinds of messages exchanged through this application. Our linguistic analysis reveals that a significant number of these messages (∼ 20%) include inappropriate, hurtful, or profane language intended to embarrass, offend, or bully the recipient. Our analysis helps in understanding the different ways in which anonymous message exchange platforms are used and the different types of bullying present in such exchanges.
Polarization-agile antennas

A polarization-agile antenna is a type of antenna the polarization state of which can be changed dynamically, i.e., it can have either linear (vertical or horizontal) or circular polarization (left hand or right hand), depending on the requirements of its specific application. This special characteristic offers great flexibility for antenna systems, because a single antenna could be used to satisfy the different requirements of several systems. This paper gives a comprehensive review of polarization-agile antennas. The basics of polarization in antennas and polarization-agile antennas are explained. Various examples of recent developments are then provided, and the antenna configurations and design principles are illustrated and discussed. Finally, the future development of polarization-agile antennas for wireless systems is discussed
A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles' in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians.
Protecting Respondents' Identities in Microdata Release

ÐToday's globally networked society places great demand on the dissemination and sharing of information. While in the past released information was mostly in tabular and statistical form, many situations call today for the release of specific data (microdata). In order to protect the anonymity of the entities (called respondents) to which information refers, data holders often remove or encrypt explicit identifiers such as names, addresses, and phone numbers. Deidentifying data, however, provides no guarantee of anonymity. Released information often contains other data, such as race, birth date, sex, and ZIP code, that can be linked to publicly available information to reidentify respondents and inferring information that was not intended for disclosure. In this paper we address the problem of releasing microdata while safeguarding the anonymity of the respondents to which the data refer. The approach is based on the definition of k-anonymity. A table provides k-anonymity if attempts to link explicitly identifying information to its content map the information to at least k entities. We illustrate how k-anonymity can be provided without compromising the integrity (or truthfulness) of the information released by using generalization and suppression techniques. We introduce the concept of minimal generalization that captures the property of the release process not to distort the data more than needed to achieve k-anonymity, and present an algorithm for the computation of such a generalization. We also discuss possible preference policies to choose among different minimal
Tobler ' s First Law of Geography , Self Similarity , and Perlin Noise : A Large Scale Analysis of Gradient Distribution in Southern Utah with Application to Procedural Terrain Generation

A statistical analysis finds that in a 160,000 square kilometer region of southern Utah gradients appear to be exponentially distributed at resolutions from 5m up to 1km. A simple modification to the Perlin noise generator changing the gradient distribution in each octave to an exponential distribution results in realistic and interesting procedurally generated terrain. The inverse transform sampling method is used in the amortized noise algorithm to achieve an exponential distribution in each octave, resulting in the generation of infinite non-repeating terrain with the same characteristics.
The Human Brain in Numbers: A Linearly Scaled-up Primate Brain


The human brain has often been viewed as outstanding among mammalian brains: the most cognitively able, the largest-than-expected from body size, endowed with an overdeveloped cerebral cortex that represents over 80% of brain mass, and purportedly containing 100 billion neurons and 10x more glial cells. Such uniqueness was seemingly necessary to justify the superior cognitive abilities of humans over larger-brained mammals such as elephants and whales. However, our recent studies using a novel method to determine the cellular composition of the brain of humans and other primates as well as of rodents and insectivores show that, since different cellular scaling rules apply to the brains within these orders, brain size can no longer be considered a proxy for the number of neurons in the brain. These studies also showed that the human brain is not exceptional in its cellular composition, as it was found to contain as many neuronal and non-neuronal cells as would be expected of a primate brain of its size. Additionally, the so-called overdeveloped human cerebral cortex holds only 19% of all brain neurons, a fraction that is similar to that found in other mammals. In what regards absolute numbers of neurons, however, the human brain does have two advantages compared to other mammalian brains: compared to rodents, and probably to whales and elephants as well, it is built according to the very economical, space-saving scaling rules that apply to other primates; and, among economically built primate brains, it is the largest, hence containing the most neurons. These findings argue in favor of a view of cognitive abilities that is centered on absolute numbers of neurons, rather than on body size or encephalization, and call for a re-examination of several concepts related to the exceptionality of the human brain.
GraphLab: A New Framework For Parallel Machine Learning

Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insufficiently expressive while low-level tools like MPI and Pthreads leave ML experts repeatedly solving the same design challenges. By targeting common patterns in ML, we developed GraphLab, which improves upon abstractions like MapReduce by compactly expressing asynchronous iterative algorithms with sparse computational dependencies while ensuring data consistency and achieving a high degree of parallel performance. We demonstrate the expressiveness of the GraphLab framework by designing and implementing parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and Compressed Sensing. We show that using GraphLab we can achieve excellent parallel performance on large scale real-world problems.
A 0.4 V 63 $\mu$W 76.1 dB SNDR 20 kHz Bandwidth Delta-Sigma Modulator Using a Hybrid Switching Integrator

This paper presents a delta-sigma modulator operating at a supply voltage of 0.4 V. The designed delta-sigma modulator uses a proposed hybrid switching integrator and operates at a low supply voltage without clock boosting or bootstrapped switches. The proposed integrator consists of both switched-resistor and switched-capacitor operations and significantly reduces distortion at a low supply voltage. Variation in the turn-on resistance, which is the main source of distortion, is avoided by placing the switches at the virtual ground node of the amplifier. The proposed low-voltage design scheme can replace commonly-used clock boosting techniques, which rely on internal high-voltage generation circuits. A fabricated modulator achieves a 76.1 dB signal-to-noise-plus-distortion ratio (SNDR) and an 82 dB dynamic range at a 20 kHz bandwidth. The measured total power consumption is 63 μW from a 0.4 V supply voltage. The measured results show robust SNDR performance, even at ±10% supply voltage variations. The measured results also show stable performance over a wide temperature range.
Linear systems analysis of functional magnetic resonance imaging in human V1.

The linear transform model of functional magnetic resonance imaging (fMRI) hypothesizes that fMRI responses are proportional to local average neural activity averaged over a period of time. This work reports results from three empirical tests that support this hypothesis. First, fMRI responses in human primary visual cortex (V1) depend separably on stimulus timing and stimulus contrast. Second, responses to long-duration stimuli can be predicted from responses to shorter duration stimuli. Third, the noise in the fMRI data is independent of stimulus contrast and temporal period. Although these tests can not prove the correctness of the linear transform model, they might have been used to reject the model. Because the linear transform model is consistent with our data, we proceeded to estimate the temporal fMRI impulse-response function and the underlying (presumably neural) contrast-response function of human V1.
COMPARISON OF DIFFERENT METHODS OF ANTIOXIDANT ACTIVITY EVALUATION OF GREEN AND ROAST C . ARABICA AND C . ROBUSTA COFFEE BEANS

aDepartment of Hygiene and Technology of Vegetable Foodstuff, University of Veterinary and Pharmaceutical Sciences Brno, Palackého tř. 1/3, 612 42 Brno Královo Pole. Czech Republic bDepartment of Biochemistry and Biophysics, University of Veterinary and Pharmaceutical Sciences Brno, Palackého tř. 1/3, 612 42 Brno Královo Pole. Czech Republic cDepartment of Food Science and Technology, Kaunas University of Technology, Radvilėnų pl. 19, LT-50254 Kaunas. Lithuania
Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing

Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for mobile phone sensing. We consider two system models: the platform-centric model where the platform provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the platform-centric model, we design an incentive mechanism using a Stackelberg game, where the platform is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the platform is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.
Design Science Research Contributions: Finding a Balance between Artifact and Theory

With the rising interest in Design Science Research (DSR), it is crucial to engage in the ongoing debate on what constitutes an acceptable contribution for publishing DSR the design artifact, the design theory, or both. In this editorial, we provide some constructive guidance across different positioning statements with actionable recommendations for DSR authors and reviewers. We expect this editorial to serve as a foundational step towards clarifying misconceptions about DSR contributions and to pave the way for the acceptance of more DSR papers to top IS journals.