nils-herrmann/clean_scirepeval huggingface dataset:
VIRAL ADVERTISING IN SOCIAL MEDIA : PARTICIPATION IN FACEBOOK GROUPS AND RESPONSES AMONG COLLEGE-AGED USERS

This study examines the potential link of Facebook group participation with viral advertising responses. The results suggest that college-aged Facebook group members engage in higher levels of self-disclosure and maintain more favorable attitudes toward social media and advertising in general than do nongroup members. However, Facebook group participation does not exert an influence on users' viral advertising pass-on behaviors. The results also identify variations in predictors of passon behaviors between group members and nonmembers. These findings have theoretical and managerial implications for viral advertising on Facebook.
Adoption of Big Data Solutions: A study on its security determinants using Sec-TOE Framework

Big Data Solutions (BDS) refers to innovative solutions designed to perform searching, mining and analysis of high volume of data. While BDS is being actively adopted by pioneering and leading organizations due to its prospective benefits, many organizations are still divided on the need to adopt it. Security issues related to big data’s characteristics are among the hindering factors cited by non-adopters. Thus, it creates opportunities to study on the security related issues pertinent to BDS adoption. In this preliminary study, Technology-Organizational-Environmental (TOE) framework was adopted and adapted to fit the security factors being studied. Data were collected from 25 respondents through an anonymous online questionnaire and descriptive analysis was performed. The results reveal that an organization’s intention to adopt BDS can be positively influenced by perceived compatibility, top management support, information security culture and organizational learning culture. Whilst, the non-adopters are negatively influenced by perceived complexity and risks in outsourcing. One factor was found to have inconclusive outcome to both adopters and non-adopters (security and privacy regulatory concern), suggesting that it may not have any significant effects in organizational intention to adopt BDS.
A generic and customizable framework for the design of ETL scenarios

Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. In this paper, we delve into the logical design of ETL scenarios and provide a generic and customizable framework in order to support the DW designer in his task. First, we present a metamodel particularly customized for the definition of ETL activities. We follow a workflow-like approach, where the output of a certain activity can either be stored persistently or passed to a subsequent activity. Also, we employ a declarative database programming language, LDL, to define the semantics of each activity. The metamodel is generic enough to capture any possible ETL activity. Nevertheless, in the pursuit of higher reusability and flexibility, we specialize the set of our generic metamodel constructs with a palette of frequently-used ETL activities, which we call templates. Moreover, in order to achieve a uniform extensibility mechanism for this library of built-ins, we have to deal with specific language issues. Therefore, we also discuss the mechanics of template instantiation to concrete activities. The design concepts that we introduce have been implemented in a tool, ARKTOS II, which is also presented.
Steep switching tunnel FET: A promise to extend the energy efficient roadmap for post-CMOS digital and analog/RF applications

Steep switching Tunnel FETs (TFET) can extend the supply voltage scaling with improved energy efficiency for both digital and analog/RF application. In this paper, recent approaches on III-V Tunnel FET device design, prototype device demonstration, modeling techniques and performance evaluations for digital and analog/RF application are discussed and compared to CMOS technology. The impact of steep switching, uni-directional conduction and negative differential resistance characteristics are explored from circuit design perspective. Circuit-level implementation such as III-V TFET based Adder and SRAM design shows significant improvement on energy efficiency and power reduction below 0.3V for digital application. The analog/RF metric evaluation is presented including gm/Ids metric, temperature sensitivity, parasitic impact and noise performance. TFETs exhibit promising performance for high frequency, high sensitivity and ultra-low power RF rectifier application.
A 34% PAE, 18.6dBm 42–45GHz stacked power amplifier in 45nm SOI CMOS

A two-stack 42-45GHz power amplifier is implemented in 45nm SOI CMOS. Transistor stacking allows increased drain biasing to increase output power. Additionally, shunt inter-stage matching is used and improves PAE by more than 6%. This amplifier exhibits 18.6dBm saturated output power, with peak power gain of 9.5dB. It occupies 0.3mm2 including pads while achieving a peak PAE of 34%. The PAE remains above 30% from 42 to 45GHz.
MAFIA: A Performance Study of Mining Maximal Frequent Itemsets

We present a performance study of the MAFIA algorithm for mining maximal frequent itemsets from a transactional database. In a thorough experimental analysis, we isolate the effects of individual components of MAFIA, including search space pruning techniques and adaptive compression. We also compare our performance with previous work by running tests on very different types of datasets. Our experiments show that MAFIA performs best when mining long itemsets and outperforms other algorithms on dense data by a factor of three to thirty.
SAMM: A Spontaneous Micro-Facial Movement Dataset

Micro-facial expressions are spontaneous, involuntary movements of the face when a person experiences an emotion but attempts to hide their facial expression, most likely in a high-stakes environment. Recently, research in this field has grown in popularity, however publicly available datasets of micro-expressions have limitations due to the difficulty of naturally inducing spontaneous micro-expressions. Other issues include lighting, low resolution and low participant diversity. We present a newly developed spontaneous micro-facial movement dataset with diverse participants and coded using the Facial Action Coding System. The experimental protocol addresses the limitations of previous datasets, including eliciting emotional responses from stimuli tailored to each participant. Dataset evaluation was completed by running preliminary experiments to classify micro-movements from non-movements. Results were obtained using a selection of spatio-temporal descriptors and machine learning. We further evaluate the dataset on emerging methods of feature difference analysis and propose an Adaptive Baseline Threshold that uses individualised neutral expression to improve the performance of micro-movement detection. In contrast to machine learning approaches, we outperform the state of the art with a recall of 0.91. The outcomes show the dataset can become a new standard for micro-movement data, with future work expanding on data representation and analysis.
A high protein diet (3.4 g/kg/d) combined with a heavy resistance training program improves body composition in healthy trained men and women – a follow-up investigation

The consumption of a high protein diet (>4 g/kg/d) in trained men and women who did not alter their exercise program has been previously shown to have no significant effect on body composition. Thus, the purpose of this investigation was to determine if a high protein diet in conjunction with a periodized heavy resistance training program would affect indices of body composition, performance and health.



Forty-eight healthy resistance-trained men and women completed this study (mean ± SD; Normal Protein group [NP n = 17, four female and 13 male]: 24.8 ± 6.9 yr; 174.0 ± 9.5 cm height; 74.7 ± 9.6 kg body weight; 2.4 ± 1.7 yr of training; High Protein group [HP n = 31, seven female and 24 male]: 22.9 ± 3.1 yr; 172.3 ± 7.7 cm; 74.3 ± 12.4 kg; 4.9 ± 4.1 yr of training). Moreover, all subjects participated in a split-routine, periodized heavy resistance-training program. Training and daily diet logs were kept by each subject. Subjects in the NP and HP groups were instructed to consume their baseline (~2 g/kg/d) and >3 g/kg/d of dietary protein, respectively.



Subjects in the NP and HP groups consumed 2.3 and 3.4 g/kg/day of dietary protein during the treatment period. The NP group consumed significantly (p < 0.05) more protein during the treatment period compared to their baseline intake. The HP group consumed more (p < 0.05) total energy and protein during the treatment period compared to their baseline intake. Furthermore, the HP group consumed significantly more (p < 0.05) total calories and protein compared to the NP group. There were significant time by group (p ≤ 0.05) changes in body weight (change: +1.3 ± 1.3 kg NP, -0.1 ± 2.5 HP), fat mass (change: -0.3 ± 2.2 kg NP, -1.7 ± 2.3 HP), and % body fat (change: -0.7 ± 2.8 NP, -2.4 ± 2.9 HP). The NP group gained significantly more body weight than the HP group; however, the HP group experienced a greater decrease in fat mass and % body fat. There was a significant time effect for FFM; however, there was a non-significant time by group effect for FFM (change: +1.5 ± 1.8 NP, +1.5 ± 2.2 HP). Furthermore, a significant time effect (p ≤ 0.05) was seen in both groups vis a vis improvements in maximal strength (i.e., 1-RM squat and bench) vertical jump and pull-ups; however, there were no significant time by group effects (p ≥ 0.05) for all exercise performance measures. Additionally, there were no changes in any of the blood parameters (i.e., basic metabolic panel).



Consuming a high protein diet (3.4 g/kg/d) in conjunction with a heavy resistance-training program may confer benefits with regards to body composition. Furthermore, there is no evidence that consuming a high protein diet has any deleterious effects.
Structuring Interactions for Large-Scale Synchronous Peer Learning

This research investigates how to introduce synchronous interactive peer learning into an online setting appropriate both for crowdworkers (learning new tasks) and students in massive online courses (learning course material). We present an interaction framework in which groups of learners are formed on demand and then proceed through a sequence of activities that include synchronous group discussion about learner-generated responses. Via controlled experiments with crowdworkers, we show that discussing challenging problems leads to better outcomes than working individually, and incentivizing people to help one another yields still better results. We then show that providing a mini-lesson in which workers consider the principles underlying the tested concept and justify their answers leads to further improvements. Combining the mini-lesson with the discussion of the multiple-choice question leads to significant improvements on that question. We also find positive subjective responses to the peer interactions, suggesting that discussions can improve morale in remote work or learning settings.
F2FS: A New File System for Flash Storage

F2FS is a Linux file system designed to perform well on modern flash storage devices. The file system builds on append-only logging and its key design decisions were made with the characteristics of flash storage in mind. This paper describes the main design ideas, data structures, algorithms and the resulting performance of F2FS. Experimental results highlight the desirable performance of F2FS; on a state-of-the-art mobile system, it outperforms EXT4 under synthetic workloads by up to 3.1× (iozone) and 2× (SQLite). It reduces elapsed time of several realistic workloads by up to 40%. On a server system, F2FS is shown to perform better than EXT4 by up to 2.5× (SATA SSD) and 1.8× (PCIe SSD).
IMBALANCED DATASET CLASSIFICATION AND SOLUTIONS : A REVIEW

-Imbalanced data set problem occurs in classification, where the number of instances of one class is much lower than the instances of the other classes. The main challenge in imbalance problem is that the small classes are often more useful, but standard classifiers tend to be weighed down by the huge classes and ignore the tiny ones. In machine learning the imbalanced datasets has become a critical problem and also usually found in many applications such as detection of fraudulent calls, bio-medical, engineering, remote-sensing, computer society and manufacturing industries. In order to overcome the problems several approaches have been proposed. In this paper a study on Imbalanced dataset problem and the solution is given.
Examining the Alternative Media Ecosystem Through the Production of Alternative Narratives of Mass Shooting Events on Twitter

This research explores the alternative media ecosystem through a Twitter lens. Over a ten-month period, we collected tweets related to alternative narratives—e.g. conspiracy theories—of mass shooting events. We utilized tweeted URLs to generate a domain network, connecting domains shared by the same user, then conducted qualitative analysis to understand the nature of different domains and how they connect to each other. Our findings demonstrate how alternative news sites propagate and shape alternative narratives, while mainstream media deny them. We explain how political leanings of alternative news sites do not align well with a U.S. left-right spectrum, but instead feature an antiglobalist (vs. globalist) orientation where U.S. Alt-Right sites look similar to U.S. Alt-Left sites. Our findings describe a subsection of the emerging alternative media ecosystem and provide insight in how websites that promote conspiracy theories and pseudo-science may function to conduct underlying political agendas.
Blockchain Based Decentralized Management of Demand Response Programs in Smart Energy Grids

In this paper, we investigate the use of decentralized blockchain mechanisms for delivering transparent, secure, reliable, and timely energy flexibility, under the form of adaptation of energy demand profiles of Distributed Energy Prosumers, to all the stakeholders involved in the flexibility markets (Distribution System Operators primarily, retailers, aggregators, etc.). In our approach, a blockchain based distributed ledger stores in a tamper proof manner the energy prosumption information collected from Internet of Things smart metering devices, while self-enforcing smart contracts programmatically define the expected energy flexibility at the level of each prosumer, the associated rewards or penalties, and the rules for balancing the energy demand with the energy production at grid level. Consensus based validation will be used for demand response programs validation and to activate the appropriate financial settlement for the flexibility providers. The approach was validated using a prototype implemented in an Ethereum platform using energy consumption and production traces of several buildings from literature data sets. The results show that our blockchain based distributed demand side management can be used for matching energy demand and production at smart grid level, the demand response signal being followed with high accuracy, while the amount of energy flexibility needed for convergence is reduced.
Hierarchical target type identification for entity-oriented queries

A significant portion of information needs in web search target entities. These may come in different forms or flavours, ranging from short keyword queries to more verbose requests, expressed in natural language. We address the task of automatically annotating queries with target types from an ontology. The identified types can subsequently be used, e.g., for creating semantically more informed query and retrieval models, filtering results, or directing the requests to specific verticals. Our study makes the following contributions. First, we formalise the task of hierarchical target type identification, argue that it is best viewed as a ranking problem, and propose multiple evaluation metrics. Second, we develop a purpose-built test collection by hand-annotating over 300 queries, from various recent entity search benchmarking campaigns, with target types from the DBpedia ontology. Finally, we introduce and examine two baseline models, inspired by federated search techniques. We show that these methods perform surprisingly well when target types are limited to a flat list of top level categories; finding the right level of granularity in the hierarchy, however, is particularly challenging and requires further investigation.
Association of an educational program in mindful communication with burnout, empathy, and attitudes among primary care physicians.

Primary care physicians report high levels of distress, which is linked to burnout, attrition, and poorer quality of care. Programs to reduce burnout before it results in impairment are rare; data on these programs are scarce.



To determine whether an intensive educational program in mindfulness, communication, and self-awareness is associated with improvement in primary care physicians' well-being, psychological distress, burnout, and capacity for relating to patients.



Before-and-after study of 70 primary care physicians in Rochester, New York, in a continuing medical education (CME) course in 2007-2008. The course included mindfulness meditation, self-awareness exercises, narratives about meaningful clinical experiences, appreciative interviews, didactic material, and discussion. An 8-week intensive phase (2.5 h/wk, 7-hour retreat) was followed by a 10-month maintenance phase (2.5 h/mo).



Mindfulness (2 subscales), burnout (3 subscales), empathy (3 subscales), psychosocial orientation, personality (5 factors), and mood (6 subscales) measured at baseline and at 2, 12, and 15 months.



Over the course of the program and follow-up, participants demonstrated improvements in mindfulness (raw score, 45.2 to 54.1; raw score change [Delta], 8.9; 95% confidence interval [CI], 7.0 to 10.8); burnout (emotional exhaustion, 26.8 to 20.0; Delta = -6.8; 95% CI, -4.8 to -8.8; depersonalization, 8.4 to 5.9; Delta = -2.5; 95% CI, -1.4 to -3.6; and personal accomplishment, 40.2 to 42.6; Delta = 2.4; 95% CI, 1.2 to 3.6); empathy (116.6 to 121.2; Delta = 4.6; 95% CI, 2.2 to 7.0); physician belief scale (76.7 to 72.6; Delta = -4.1; 95% CI, -1.8 to -6.4); total mood disturbance (33.2 to 16.1; Delta = -17.1; 95% CI, -11 to -23.2), and personality (conscientiousness, 6.5 to 6.8; Delta = 0.3; 95% CI, 0.1 to 5 and emotional stability, 6.1 to 6.6; Delta = 0.5; 95% CI, 0.3 to 0.7). Improvements in mindfulness were correlated with improvements in total mood disturbance (r = -0.39, P < .001), perspective taking subscale of physician empathy (r = 0.31, P < .001), burnout (emotional exhaustion and personal accomplishment subscales, r = -0.32 and 0.33, respectively; P < .001), and personality factors (conscientiousness and emotional stability, r = 0.29 and 0.25, respectively; P < .001).



Participation in a mindful communication program was associated with short-term and sustained improvements in well-being and attitudes associated with patient-centered care. Because before-and-after designs limit inferences about intervention effects, these findings warrant randomized trials involving a variety of practicing physicians.
BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm

We propose an approach to analyze data from the P300 speller paradigm using the machine-learning technique support vector machines. In a conservative classification scheme, we found the correct solution after five repetitions. While the classification within the competition is designed for offline analysis, our approach is also well-suited for a real-world online solution: It is fast, requires only 10 electrode positions and demands only a small amount of preprocessing.
Effect of electrolyte pH on the rate of the anodic and cathodic reactions in an air-cathode microbial fuel cell.

The measurement of electricity generation from an air-cathode microbial fuel cell (MFC) with a mixed bacteria culture at different pH showed that this MFC could tolerate an initial (feed solution) pH as high as 10. The optimal initial pH was between 8 and 10 with higher current generation compared to lower or higher pH. The bacterial metabolism exhibited a buffer effect and changed the electrolyte pH. The impedance spectra of the anode and cathode of the MFC at the open-circuit potential (OCP) revealed that the anodic microbial process preferred a neutral pH and microbial activities decreased at higher or lower pH; while the cathodic reaction was improved with increasing pH.
SemEval 2018 Task 2: Multilingual Emoji Prediction

This paper describes the results of the first shared task on Multilingual Emoji Prediction, organized as part of SemEval 2018. Given the text of a tweet, the task consists of predicting the most likely emoji to be used along such tweet. Two subtasks were proposed, one for English and one for Spanish, and participants were allowed to submit a system run to one or both subtasks. In total, 49 teams participated in the English subtask and 22 teams submitted a system run to the Spanish subtask. Evaluation was carried out emoji-wise, and the final ranking was based on macro F-Score. Data and further information about this task can be found at https://competitions.
Recognising Human Emotions from Body Movement and Gesture Dynamics

We present an approach for the recognition of acted emotional states based on the analysis of body movement and gesture expressivity. According to research showing that distinct emotions are often associated with different qualities of body movement, we use nonpropositional movement qualities (e.g. amplitude, speed and fluidity of movement) to infer emotions, rather than trying to recognise different gesture shapes expressing specific emotions. We propose a method for the analysis of emotional behaviour based on both direct classification of time series and a model that provides indicators describing the dynamics of expressive motion cues. Finally we show and interpret the recognition rates for both proposals using different classification algorithms.
Concept Drift

Traditional approaches to data mining are based on an assumption that the process that generated or is generating a data stream is static. Although this assumption holds for many applications, it does not hold for many others. Consider systems that build models for identifying important e-mail. Through interaction with and feedback from a user, such a system might determine that particular e-mail addresses and certain words of the subject are useful for predicting the importance of email. However, when the user or the persons sending email start other projects or take on additional responsibilities, what constitutes important e-mail will change. That is, the concept of important e-mail will change or drift. Such a system must be able to adapt its model or concept description in response to this change. Coping with or tracking concept drift is important for other applications, such as market-basket analysis, intrusion detection, and intelligent user interfaces, to name a few.