nils-herrmann/clean_scirepeval huggingface dataset:
Students' self-presentation on Facebook: An examination of personality and self-construal factors

The present research seeks to extend existing theory on self-disclosure to the online arena in higher educational institutions and contribute to the knowledge base and understanding about the use of a popular social networking site (SNS), Facebook, by college students. We conducted a non-experimental study to investigate how university students (N = 463) use Facebook, and examined the roles that personality and culture play in disclosure of information in online SNS-based environments. Results showed that individuals do disclose differently online vs. in-person, and that both culture and personality matter. Specifically, it was found that collectivistic individuals low on extraversion and interacting in an online environment disclosed the least honest and the most audience-relevant information, as compared to others. Exploratory analyses also indicate that students use sites such as Facebook primarily to maintain existing personal relationships and selectively used privacy settings to control their self-presentation on SNSs. The findings of this study offer insight into understanding college students’ self-disclosure on SNS, add to the literature on personality and self-disclosure, and shape future directions for research and practice on online self-presentation.
Searching for a baseline: Functional imaging and the resting human brain

Functional brain imaging in humans has revealed task-specific increases in brain activity that are associated with various mental activities. In the same studies, mysterious, task-independent decreases have also frequently been encountered, especially when the tasks of interest have been compared with a passive state, such as simple fixation or eyes closed. These decreases have raised the possibility that there might be a baseline or resting state of brain function involving a specific set of mental operations. We explore this possibility, including the manner in which we might define a baseline and the implications of such a baseline for our understanding of brain function.
Deep Learning Driven Visual Path Prediction From a Single Image

Capabilities of inference and prediction are the significant components of visual systems. Visual path prediction is an important and challenging task among them, with the goal to infer the future path of a visual object in a static scene. This task is complicated as it needs high-level semantic understandings of both the scenes and underlying motion patterns in video sequences. In practice, cluttered situations have also raised higher demands on the effectiveness and robustness of models. Motivated by these observations, we propose a deep learning framework, which simultaneously performs deep feature learning for visual representation in conjunction with spatiotemporal context modeling. After that, a unified path-planning scheme is proposed to make accurate path prediction based on the analytic results returned by the deep context models. The highly effective visual representation and deep context models ensure that our framework makes a deep semantic understanding of the scenes and motion patterns, consequently improving the performance on visual path prediction task. In experiments, we extensively evaluate the model's performance by constructing two large benchmark datasets from the adaptation of video tracking datasets. The qualitative and quantitative experimental results show that our approach outperforms the state-of-the-art approaches and owns a better generalization capability.
Packing Sparse Convolutional Neural Networks for Efficient Systolic Array Implementations: Column Combining Under Joint Optimization

This paper describes a novel approach of packing sparse convolutional neural networks for their efficient systolic array implementations. By combining subsets of columns in the original filter matrix associated with a convolutional layer, we increase the utilization efficiency of the systolic array substantially (e.g., 4x) due to the increased density of nonzeros in the resulting packed filter matrix. In combining columns, for each row, all filter weights but one with the largest magnitude are pruned. We retrain the remaining weights to preserve high accuracy. We demonstrate that in mitigating data privacy concerns the retraining can be accomplished with only fractions of the original dataset (e.g., 10% for CIFAR-10). We study the effectiveness of this joint optimization for both high utilization and classification accuracy with ASIC and FPGA designs based on efficient bit-serial implementations of multiplier-accumulators. We present analysis and empirical evidence on the superior performance of our column combining approach against prior arts under metrics such as energy efficiency (3x) and inference latency (12x).
Generating spiking time series with Generative Adversarial Networks : an application on banking transactions by Luca Simonetto 11413522 September 2018

The task of data generation using Generative Models has recently gained more and more attention from the scientific community, as the number of applications in which these models work surprisingly well is constantly increasing. Some examples are image and video generation, speech synthesis and style transfer, pose guided image generation, cross-domain transfer and super resolution. Contrarily to such tasks generating data coming from the banking domain poses a different challenge, due to its atypical structure when compared with traditional data and its limited availability due to privacy restrictions. In this work, we analyze the feasibility of generating spiking time series patterns appearing in the banking environment using Generative Adversarial Networks. We develop a novel end-to-end framework for training, testing and comparing different generative models using both quantitative and qualitative metrics. Finally, we propose a novel approach that combines Variational Autoencoders with Generative Adversarial Networks in order to learn a loss function for datasets in which good similarity metrics are difficult to define.
Nonlinear Extensions of Reconstruction ICA

In a recent paper  it was observed that unsupervised feature learning with overcomplete features could be achieved using linear autoencoders (named Reconstruction Independent Component Analysis). This algorithm has been shown to outperform other well-known algorithms by penalizing the lack of diversity (or orthogonality) amongst features. In our project, we wish to extend and improve this algorithm to include other non-linearities. In this project we have considered three unsupervised learning algorithms: (a) Sparse Autoencoder (b) Reconstruction ICA (RICA), a linear autoencoder proposed in , and (c) Nonlinear RICA, a proposed extension of RICA for capturing nonlinearities in feature detection. Our research indicates that exploring non-linear extensions of RICA holds good promise; preliminary results with hyperbolic tangent function on the MNIST dataset showed impressive accuracy (comparable with sparse autoencoders), robustness, and required a fraction of the computational effort.
A Fuzzy Logic Approach for Opinion Mining on Large Scale Twitter Data

Recently, some efforts have been made to mine social media for the analysis of public sentiment. By means of a literature review on early works related to social media analytics especially on opinion mining, it was recognized that in the real life social media environment, the structure of the data is commonly not clear and it does not directly generate enough information to fully represent any selected target. However, most of these works were unable to accurately extract clear indications of general public opinion from the ambiguous social media data. They also lacked the capacity to summarize multi-characteristics from the scattered mass of social data and use it to compile useful models, also lacked any efficient mechanism for managing the big data. Motivated by these research problems, this paper proposes a novel matrix-based fuzzy algorithm, called the FMM system, to mine the defined multi-layered Twitter data. Through sets of comparable experiments applied on Twitter data, the proposed FMM system achieved an excellent performance, with both fast processing speeds and high predictive accuracy.
Datum: Managing Data Purchasing and Data Placement in a Geo-Distributed Data Market

This paper studies two design tasks faced by a geo-distributed cloud data market: which data to purchase data purchasing and where to place/replicate the data for delivery data placement. We show that the joint problem of data purchasing and data placement within a cloud data market can be viewed as a facility location problem and is thus NP-hard. However, we give a provably optimal algorithm for the case of a data market made up of a single data center and then generalize the structure from the single data center setting in order to develop a near-optimal, polynomial-time algorithm for a geo-distributed data market. The resulting design, $\mathsf {Datum}$ , decomposes the joint purchasing and placement problem into two subproblems, one for data purchasing and one for data placement, using a transformation of the underlying bandwidth costs. We show, via a case study, that $\mathsf {Datum}$ is near optimal within 1.6% in practical settings.
BRAVO: Balanced Reliability-Aware Voltage Optimization

Defining a processor micro-architecture for a targeted productspace involves multi-dimensional optimization across performance, power and reliability axes. A key decision in sucha definition process is the circuit-and technology-driven parameterof the nominal (voltage, frequency) operating point. This is a challenging task, since optimizing individually orpair-wise amongst these metrics usually results in a designthat falls short of the specification in at least one of the threedimensions. Aided by academic research, industry has nowadopted early-stage definition methodologies that considerboth energy-and performance-related metrics. Reliabilityrelatedenhancements, on the other hand, tend to get factoredin via a separate thread of activity. This task is typically pursuedwithout thorough pre-silicon quantifications of the energyor even the performance cost. In the late-CMOS designera, reliability needs to move from a post-silicon afterthoughtor validation-only effort to a pre-silicon definitionprocess. In this paper, we present BRAVO, a methodologyfor such reliability-aware design space exploration. BRAVOis supported by a multi-core simulation framework that integratesperformance, power and reliability modeling capability. Errors induced by both soft and hard fault incidence arecaptured within the reliability models. We introduce the notionof the Balanced Reliability Metric (BRM), that we useto evaluate overall reliability of the processor across soft andhard error incidences. We demonstrate up to 79% improvementin reliability in terms of this metric, for only a 6% dropin overall energy efficiency over design points that maximizeenergy efficiency. We also demonstrate several real-life usecaseapplications of BRAVO in an industrial setting.
Inertial sensor-based knee flexion/extension angle estimation.

A new method for estimating knee joint flexion/extension angles from segment acceleration and angular velocity data is described. The approach uses a combination of Kalman filters and biomechanical constraints based on anatomical knowledge. In contrast to many recently published methods, the proposed approach does not make use of the earth's magnetic field and hence is insensitive to the complex field distortions commonly found in modern buildings. The method was validated experimentally by calculating knee angle from measurements taken from two IMUs placed on adjacent body segments. In contrast to many previous studies which have validated their approach during relatively slow activities or over short durations, the performance of the algorithm was evaluated during both walking and running over 5 minute periods. Seven healthy subjects were tested at various speeds from 1 to 5 mile/h. Errors were estimated by comparing the results against data obtained simultaneously from a 10 camera motion tracking system (Qualysis). The average measurement error ranged from 0.7 degrees for slow walking (1 mph) to 3.4 degrees for running (5 mph). The joint constraint used in the IMU analysis was derived from the Qualysis data. Limitations of the method, its clinical application and its possible extension are discussed.
Can crowdsourcing really be used in B2B innovation?

The aim of this research is to explore the use of crowdsourcing especially from business-to-business companies' innovation perspective, and to create a more comprehensive picture of the possibilities of crowdsourcing for companies operating in business-to-business markets. Business-to-business context was chosen because it is in many ways a very different environment for crowdsourcing than business-to-consumer context, and we found no academic studies on the topic. A systematic literature review was performed to gain an understanding of the state-of-the-art, and to create a research framework on the concept of crowdsourcing in innovation covering e.g. the type of crowdsourcing used, which crowds were used and in what innovation process phase crowds were utilized. Concerning the current ways of using 'crowds' and crowdsourcing in B2B innovation process, we found evidence of using crowdsourcing in B2B's in all the three innovation process phases: front-end, product development, and commercialization. Furthermore, evidence was found for crowdsourcing to be used in innovation mainly in the manner of crowd creation, crowd wisdom and crowd funding. We found that the role of social media was quite essential in all the found B2B crowdsourcing examples.
Overfitting avoidance as bias

Strategies for increasing predictive accuracy through selective pruning have been widely adopted by researchers in decision tree induction. It is easy to get the impression from research reports that there are statistical reasons for believing that theseoverfitting avoidance strategies do increase accuracy and that, as a research community, we are making progress toward developing powerful, general methods for guarding against overfitting in inducing decision trees. In fact, any overfitting avoidance strategy amounts to a form of bias and, as such, may degrade performance instead of improving it. If pruning methods have often proven successful in empirical tests, this is due, not to the methods, but to the choice of test problems. As examples in this article illustrate, overfitting avoidance strategies are not better or worse, but only more or less appropriate to specific application domains. We are not—and cannot be—making progress toward methods both powerful and general.
Requirements Engineering with Use Cases - a Basis for Software Development

Successful development of software systems depends on the quality of the requirements engineering process. Use cases and scenarios are promising vehicles for eliciting, specifying and validating requirements. This thesis investigates the role of use case modelling in requirements engineering and its relation to system verification and validation. The thesis includes studies of concepts and representations in use case modelling. Semantic issues are discussed and notations based on natural and graphical languages are provided, which allow a hierarchical structure and enable representation at different abstraction levels. Two different strategies for integrating use case modelling with system testing are presented and evaluated, showing how use cases can be a basis for test case generation and reliability assessment. An experiment on requirements validation using inspections with perspective-based reading is also reported, where one of the perspectives applies use case modelling. The results of the experiment indicate that a combination of multiple perspectives may not give higher defect detection rate compared to single perspective reading. Pilot studies of the transition from use case based requirements to high-level design are described, where use cases are successfully applied for documenting how functional requirements are distributed on architectural elements. The investigation of an industrial requirements engineering process improvement programme is also reported, where the introduction of a release-driven prioritisation method contributed to a measurable improvement in delivery precision and product quality. The results presented in the thesis provide further support for how to successfully apply requirements engineering with use cases as an important basis for software development.
DWT and MFCCs based Feature Extraction Methods for Isolated Word Recognition

A new method for feature extraction is presented in this paper for speech recognition using a combination of discrete wavelet transform (DWT) and mel Frequency Cepstral Coefficients (MFCCs). The objective of this method is to enhance the performance of the proposed method by introducing more features from the signal. The performance of the Wavelet-based mel Frequency Cepstral Coefficients method is compared to mel Frequency Cepstral Coefficients based method for features extraction. Wavelet transform is applied to the speech signal where the input speech signal is decomposed into various frequency channels using the properties of wavelet transform. then Mel-Frequency Cepstral Coefficients (MFCCs) of the wavelet channels are calculated. A new set of features can be generated by concatenating both features. The speech signals are sampled directly from the microphone. Neural Networks (NN) are used in the proposed methods for classification. The proposed method is implemented for 15 male speakers uttering 10 isolated words each which are the digits from zero to nine. each digit is repeated 15 times.
Named entity recognition on Indonesian Twitter posts using long short-term memory networks

The task of Named-Entity Recognition (NER) can support the higher-level tasks such as question answering, text summarization, and information retrieval. This work views NER on Indonesian Twitter posts as a sequence labeling problem using supervised machine learning approach. The architecture used is Long Short-Term Memory Networks (LSTMs), with word embedding and POS tag as the model features. As the result, our model can give a performance with an F1 score of 77.08%.
Internet Gratifications and Internet Addiction: On the Uses and Abuses of New Media

Internet addiction has been identified as a pathological behavior, but its symptoms may be found in normal populations, placing it within the scope of conventional theories of media attendance. The present study drew upon fresh conceptualizations of gratifications specific to the Internet to uncover seven gratification factors: Virtual Community, Information Seeking, Aesthetic Experience, Monetary Compensation, Diversion, Personal Status, and Relationship Maintenance. With no parallel in prior research, Virtual Community might be termed a "new" gratification. Virtual Community, Monetary Compensation, Diversion, and Personal Status gratifications accounted for 28% of the variance in Internet Addiction Tendency. The relationship between Internet addiction and gratifications was discussed in terms of the formation of media habits and the distinction between content and process gratifications.
Paper2vec: Combining Graph and Text Information for Scientific Paper Representation

We present Paper2vec, a novel neural network embedding based approach for creating scientific paper representations which make use of both textual and graph-based information. An academic citation network can be viewed as a graph where individual nodes contain rich textual information. With the current trend of open-access to most scientific literature, we presume that this full text of a scientific article contain vital source of information which aids in various recommendation and prediction tasks concerning this domain. To this end, we propose an approach, Paper2vec, which comprises of information from both the modalities and results in a rich representation for scientific papers. Over the recent past representation learning techniques have been studied extensively using neural networks. However, they are modeled independently for text and graph data. Paper2vec leverages recent research in the broader field of unsupervised feature learning from both graphs and text documents. We demonstrate the efficacy of our representations on three real world academic datasets in two tasks node classification and link prediction where Paper2vec is able to outperform state-of-the-art by a considerable margin.
THz Imaging Radar for Standoff Personnel Screening

A summary of the NASA Jet Propulsion Laboratory's 675 GHz imaging radar is presented, with an emphasis on several key design aspects that enable fast, reliable through-clothes imaging of person-borne concealed objects. Using the frequency-modulated continuous-wave (FMCW) radar technique with a nearly 30 GHz bandwidth, sub-centimeter range resolution is achieved. To optimize the radar's range resolution, a reliable software calibration procedure compensates for signal distortion from radar waveform nonlinearities. Low-noise, high dynamic range detection comes from the radar's heterodyne RF architecture, low-noise chirp source, and high-performance 675 GHz transceiver. The radar's optical design permits low-distortion fast beam scanning for single-pixel imaging, and a real-time radar image frame rate of 1 Hz is now possible. Still faster speeds are on the horizon as multi-beam THz transceivers are developed.
Effects of weightlifting vs. kettlebell training on vertical jump, strength, and body composition.

Effects of weightlifting vs. kettlebell training on vertical jump, strength, and body composition.  2012-The present study compared the effects of 6 weeks of weightlifting plus traditional heavy resistance training exercises vs. kettlebell training on strength, power, and anthropometric measures. Thirty healthy men were randomly assigned to 1 of 2 groups: (a) weightlifting (n = 13; mean ± SD: age, 22.92 ± 1.98 years; body mass, 80.57 ± 12.99 kg; height, 174.56 ± 5.80 cm) or (b) kettlebell (n = 17; mean ± SD: age, 22.76 ± 1.86 years; body mass, 78.99 ± 10.68 kg; height, 176.79 ± 5.08 cm) and trained 2 times a week for 6 weeks. A linear periodization model was used for training; at weeks 1-3 volume was 3 × 6 (kettlebell swings or high pull), 4 × 4 (accelerated swings or power clean), and 4 × 6 (goblet squats or back squats), respectively, and the volume increased during weeks 4-6 to 4 × 6, 6 × 4, and 4 × 6, respectively. Participants were assessed for height (in centimeters), body mass (in kilograms), and body composition (skinfolds). Strength was assessed by the back squat 1 repetition maximum (1RM), whereas power was assessed by the vertical jump and power clean 1RM. The results of this study indicated that short-term weightlifting and kettlebell training were effective in increasing strength and power. However, the gain in strength using weightlifting movements was greater than that during kettlebell training. Neither method of training led to significant changes in any of the anthropometric measures. In conclusion, 6 weeks of weightlifting induced significantly greater improvements in strength compared with kettlebell training. No between-group differences existed for the vertical jump or body composition.