Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring, Characterizing, and Detecting Facebook Like Farms

Online social networks offer convenient ways to reach out to large audiences. In particular, Facebook pages are increasingly used by businesses, brands, and organizations to connect with multitudes of users worldwide. As the number of likes of a page has become a de-facto measure of its popularity and profitability, an underground market of services artificially inflating page likes (“like farms”) has emerged alongside Facebook’s official targeted advertising platform. Nonetheless, besides a few media reports, there is little work that systematically analyzes Facebook pages’ promotion methods. Aiming to fill this gap, we present a honeypot-based comparative measurement study of page likes garnered via Facebook advertising and from popular like farms. First, we analyze likes based on demographic, temporal, and social characteristics and find that some farms seem to be operated by bots and do not really try to hide the nature of their operations, while others follow a stealthier approach, mimicking regular users’ behavior. Next, we look at fraud detection algorithms currently deployed by Facebook and show that they do not work well to detect stealthy farms that spread likes over longer timespans and like popular pages to mimic regular users. To overcome their limitations, we investigate the feasibility of timeline-based detection of like farm accounts, focusing on characterizing content generated by Facebook accounts on their timelines as an indicator of genuine versus fake social activity. We analyze a wide range of features extracted from timeline posts, which we group into two main categories: lexical and non-lexical. We find that like farm accounts tend to re-share content more often, use fewer words and poorer vocabulary, and more often generate duplicate comments and likes compared to normal users. Using relevant lexical and non-lexical features, we build a classifier to detect like farms accounts that achieves a precision higher than 99% and a 93% recall.
ActDetector: Detecting Daily Activities Using Smartwatches

Detecting daily activities is helpful for health care and clinical medicine. In this paper, we present ActDetector, a smartwatch based application which detects 8 common daily activities, including sitting, walking, running, going upstairs, going downstairs, eating, driving and sitting in a vehicle. By leveraging the built-in sensors on smartwatch, a multi-level classification system is proposed which considers both detection accuracy and energy efficiency. ActDetector is designed to work unobtrusively, no matter on which wrist the smartwatch is worn. We have implemented ActDetector on Sony Smartwatch 3 and evaluated its performance in real experiments involving 12 users. Experimental results show that ActDetector is energy efficient and can detect the daily activities with high accuracy.
Emotion and automaticity: Impact of positive and negative emotions on novice and experienced performance of a sensorimotor skill

Attention was directed towards negative, neutral, and positive word stimuli to explore the effect of emotions on sensorimotor skill performance. Forty novice and 40 experienced basketballers simultaneously completed a free-throw shooting task and a secondary word semantics task. A manipulation check confirmed that the secondary task influenced participants' feelings. Both groups responded faster to neutral and positive words than negative words. Shooting performance of novices did not differ between experimental conditions, but experienced basketballers were more accurate when processing positive stimuli. It was concluded that directing attention towards positive emotion may have benefited sports performance by diverting attention away from execution of the primary task, promoting automatic skill execution by experienced basketballers.
Lexical choice and language selection in bilingual preschoolers

This study examined single-word code-mixing produced by bilingual preschoolers in order to better understand lexical choice patterns in each language. Analysis included item-level code-mixed responses of 606 five-year-old children. Per parent report, children were separated by language dominance based on language exposure and use. Children were assigned to a no-risk or at-risk for language impairment group based on individual performance from an English–Spanish screening battery. Data analysis compared the prevalence, frequency, and accuracy of code-mixed responses on expressive semantic items across participants’ language dominance and risk status. Language dominance and risk status impacted children’s code-mixing patterns. The correct number of code-mixed responses on the English screener was influenced by risk status, whereas language dominance determined the number of correct code-mixed answers on the Spanish screener. Lexical choice and language selection depend on linguistic knowledge and skill. During this emergent stage of bilingualism, preschoolers demonstrate the use of code-mixing as a compensatory strategy to fill lexical gaps. Consistent with previous studies, findings indicate that code-mixing necessitates linguistic competence in more than one language.
Shape Based Image Segmentation Using Bootstrap Resampling and Hcma

—For any learning algorithm, the problems of robustness toward small fluctuations in the data as well as the generalization of inferred solution to previous unseen instances of dataset from the chosen domain are highly relevant. Image segmentation as a learning problem requires inferring a robust partitioning of image patches with generalization to novel images of the same type. The sensitivity of segmentation solution to image variations is measured by image resampling. A Hierarchial Chamfer matching algorithm implements shape constraints which are included in the inference process to guide ambiguous groupings of color and texture features. Shape and similarity based grouping based information is combined into a semantic likelihood map in the framework of Bayesian statistics.
Data bubbles: quality preserving performance boosting for hierarchical clustering

In this paper, we investigate how to scale hierarchical clustering methods (such as OPTICS) to extremely large databases by utilizing data compression methods (such as BIRCH or random sampling). We propose a three step procedure: 1) compress the data into suitable representative objects; 2) apply the hierarchical clustering algorithm only to these objects; 3) recover the clustering structure for the whole data set, based on the result for the compressed data. The key issue in this approach is to design compressed data items such that not only a hierarchical clustering algorithm can be applied, but also that they contain enough information to infer the clustering structure of the original data set in the third step. This is crucial because the results of hierarchical clustering algorithms, when applied naively to a random sample or to the clustering features (CFs) generated by BIRCH, deteriorate rapidly for higher compression rates. This is due to three key problems, which we identify. To solve these problems, we propose an efficient post-processing step and the concept of a Data Bubble as a special kind of compressed data item. Applying OPTICS to these Data Bubbles allows us to recover a very accurate approximation of the clustering structure of a large data set even for very high compression rates. A comprehensive performance and quality evaluation shows that we only trade very little quality of the clustering result for a great increase in performance.
Learning and Earnings: How College Matters in Earnings Gap between Hispanic Workers and Non-Hispanic White Workers

This article uses Current Population Survey Outgoing Rotation Group files to investigate the role of higher education in explaining the earnings gap between Hispanic workers and non-Hispanic White workers. Based on the decomposition method developed by Cotton, it was found that human capital takes precedence over labor market discrimination in explaining the earnings gap. A policy implication is that equal compensation of Hispanic workers as non-Hispanic White workers is as important as the improvement of educational attainment.
A three class treatment of the FHR classification problem using latent class analysis labeling

Electronic Fetal Monitoring in the form of cardiotocography is routinely used for fetal assessment both during pregnancy and delivery. However its interpretation requires a high level of expertise and even then the assessment is somewhat subjective as it has been proven by the high inter and intra-observer variability. Therefore the scientific community seeks for more objective methods for its interpretation. Along this path, presented work proposes a classification approach, which is based on a latent class analysis method that attempts to produce more objective labeling of the training cases, a step which is vital in a classification problem. The method is combined with a simple logistic regression approach under two different schemes: a standard multi-class classification formulation and an ordinal classification one. The results are promising suggesting that more effort should be put in this proposed approach.
A predictive system shutdown method for energy saving of event-driven computation

We present a system-level power management technique for power saving of event-driven applications. We present a new predictive system shutdown method to exploit sleep mode operations for power saving. We use an exponential-average approach to predict the upcoming idle period. We introduce two mechanisms, prediction-miss correction and pre-wakeup, to improve the hit ratio and to reduce the delay overhead. Experiments on four different event-driven applications show that our proposed method achieves high hit ratios in a wide range of delay overheads, which results in a high degree of power saving with low delay penalties.
Learning of Culturally Disadvantaged Children as a Function of Social and Tangible Reward.

The experiment studied the *relative effectiveness of tangible and social rewards upon the performance of a simple discrimination learning task by culturally disadvantaged children: Ako assessed were the effects of the sex of the subjects (Ss) and the examiner. Ss . ere 144 five-and six-year old youngsters in a Georgia Head Start program. No differential effects of the two types of rewards were found nor was there a significant interaction of sex as a variable. Among the conclusions is the statement that these results question *the-)pplicability of a 9eneral concept of cultural disadvantage as a function of lower socioeconomic status. (NH)
Systems 1 and 2 thinking processes and cognitive reflection testing in medical students

Background Diagnostic decision-making is made through a combination of Systems 1 (intuition or pattern-recognition) and Systems 2 (analytic) thinking. The purpose of this study was to use the Cognitive Reflection Test (CRT) to evaluate and compare the level of Systems 1 and 2 thinking among medical students in pre-clinical and clinical programs. Methods The CRT is a three-question test designed to measure the ability of respondents to activate metacognitive processes and switch to System 2 (analytic) thinking where System 1 (intuitive) thinking would lead them astray. Each CRT question has a correct analytical (System 2) answer and an incorrect intuitive (System 1) answer. A group of medical students in Years 2 & 3 (pre-clinical) and Years 4 (in clinical practice) of a 5-year medical degree were studied. Results Ten percent (13/128) of students had the intuitive answers to the three questions (suggesting they generally relied on System 1 thinking) while almost half (44%) answered all three correctly (indicating full analytical, System 2 thinking). Only 3–13% had incorrect answers (i.e. that were neither the analytical nor the intuitive responses). Non-native English speaking students (n = 11) had a lower mean number of correct answers compared to native English speakers (n = 117: 1.0 s 2.12 respectfully: p < 0.01). As students progressed through questions 1 to 3, the percentage of correct System 2 answers increased and the percentage of intuitive answers decreased in both the pre-clinical and clinical students. Conclusions Up to half of the medical students demonstrated full or partial reliance on System 1 (intuitive) thinking in response to these analytical questions. While their CRT performance has no claims to make as to their future expertise as clinicians, the test may be used in helping students to understand the importance of awareness and regulation of their thinking processes in clinical practice.
Framework for Sentiment Analysis of Twitter Post

In micro-blogging services like Twitter offers a robust outlet for people's thoughts and feelings it's a colossal ever-growing provider of texts ranging from everyday observations to concerned discussions. This paper contributes to the sphere of sentiment analysis, that aims to extract emotions and sentiment from text. A basic goal is to classify text as expressing either positive or negative feeling. Sentiment classifiers are created for social media text like blog posts, product reviews, and even Tweets. With increasing quality of text sources and topics, it is time to re- examine the quality sentiment extraction approaches, and presumptively to redefine and enrich the definition of sentiment.
The Differential Relationships of Shame–Proneness and Guilt–Proneness to Psychological and Somatization Symptoms

Abstract Historically, much attention has been focused on the role of guilt in psychopathology. However, recent theorists have posited that the association between guilt and psychopathology may be better accounted for by shame or by the overlapping features of guilt and shame. The current investigation assessed the relationships of shame–proneness versus guilt–proneness to psychological symptoms, somatization symptoms, attributional style, and concealment (n= 156). The shared variance between shame–proneness and guilt–proneness and the unique component of shame–proneness were related to both psychological and somatization symptoms, whereas the unique component of guilt–proneness was not related to these measures. Further, increased shame–proneness was associated with making depressogenic attributions, whereas guilt–proneness was not. Concealment was found to mediate the relationship between shame–proneness and psychological symptoms. These findings provide further evidence that the association between gui.
Advances in Nucleases Used for Genome Editing

Genome editing is an exciting technology that allows for specific manipulation of complex genomes. While the original tools for genome manipulation had low efficiencies, genome editing tools discovered in the past fifteen years have been widely studied and great efforts have been required to improve their efficiency. This article summarizes how Zinc Finger Nuclease (ZFN), Transcription Activator-Like Effector Nuclease (TALEN) and Clustered Regularly Interspaced Palindromic Repeat (CRISPR)/Cas9 nucleases work, as well as the most meaningful advances achieved in the development of these technologies.
Do Diametric Measurements Provide Sufficient and Reliable Tumor Assessment? An Evaluation of Diametric, Areametric, and Volumetric Variability of Lung Lesion Measurements on Computerized Tomography Scans

Diametric analysis is the standard approach utilized for tumor measurement on medical imaging. However, the availability of newer more sophisticated techniques may prove advantageous. An evaluation of diameter, area, and volume was performed on 64 different lung lesions by three trained users. These calculations were obtained using a free DICOM viewer and standardized measuring procedures. Measurement variability was then studied using relative standard deviation (RSD) and intraclass correlation. Volumetric measurements were shown to be more precise than diametric. With minimal RSD and variance between different users, volumetric analysis was demonstrated as a reliable measurement technique. Additionally, the diameters were used to calculate an estimated area and volume; thereafter the estimated area and volume were compared against the actual measured values. The results in this study showed independence of the estimated and actual values. Estimated area deviated an average of 43.5% from the actual measured, and volume deviated 88.03%. The range of this variance was widely scattered and without trend. These results suggest that diametric measurements cannot be reliably correlated to actual tumor size. Access to appropriate software capable of producing volume measurements has improved drastically and shows great potential in the clinical assessment of tumors. Its applicability merits further consideration.
Traffic lights detection and recognition based on color segmentation and circle hough transform

Automatic detection and recognition of traffic light system is a useful real world application. In this paper, we describe our effort in building such a system. The image of scene obtained from on-vehicle camera is first segmented and converted into three binary images representing images with red, green and yellow color, respectively. Each binary image is then smoothed using Gaussian filter in order to remove noise. The traffic light is detected by finding circular object on the smoothed binary images. We employ Circle Hough transform for circular object detection. The recognition of traffic light is based on the color represented by the binary image with detected circular object. Evaluation of the above method with two real traffic videos demonstrates the effectiveness of the method.
Endoscopic submucosal dissection in gastric lesions: the 100 cases experience from a tertiary reference center in West

Abstract Objective. Endoscopic submucosal dissection (ESD) is an endoscopic treatment method widely used in premalignant and malignant lesions in countries of the Far East. This method, which is difficult technically and has a high complication risk rate, has rarely been performed in the West, because of the fewer number of upper gastrointestinal lesions. In the present study, we aimed to present our results of gastric ESD procedures in respect to the learning curve. Methods. A total of 100 ESD procedures, which were performed in the stomach between April 2012 and September 2014, were recorded prospectively before and after the procedure. Patient data were analyzed retrospectively. ESD procedures were numbered chronologically; the first 30 patients constituted group 1, whereas the rest were classified as the group 2. ESD results were compared between the groups. Results. In a total of 95 patients, 100 gastric ESDs were performed. The overall en-bloc and complete resection rates were 93% and 92%, respectively. In respect of the learning curve, there were significant differences in the sizes of lesions and tissues obtained, procedure duration and dissection rate, snare use and knife preferences between groups (p = 0.002, p < 0.001, p = 0.003, p < 0.001, p = 0.009, and p < 0.001, respectively). No significant difference was detected in the en-bloc and complete resection rates and complications between the groups. Conclusion. According to guideline recommendations and masters for ESD, if ESD training is initiated and continued, successful ESD may be performed in localized lesions in the stomach.
Resampling-Based Ensemble Methods for Online Class Imbalance Learning

Online class imbalance learning is a new learning problem that combines the challenges of both online learning and class imbalance learning. It deals with data streams having very skewed class distributions. This type of problems commonly exists in real-world applications, such as fault diagnosis of real-time control monitoring systems and intrusion detection in computer networks. In our earlier work, we defined class imbalance online, and proposed two learning algorithms OOB and UOB that build an ensemble model overcoming class imbalance in real time through resampling and time-decayed metrics. In this paper, we further improve the resampling strategy inside OOB and UOB, and look into their performance in both static and dynamic data streams. We give the first comprehensive analysis of class imbalance in data streams, in terms of data distributions, imbalance rates and changes in class imbalance status. We find that UOB is better at recognizing minority-class examples in static data streams, and OOB is more robust against dynamic changes in class imbalance status. The data distribution is a major factor affecting their performance. Based on the insight gained, we then propose two new ensemble methods that maintain both OOB and UOB with adaptive weights for final predictions, called WEOB1 and WEOB2. They are shown to possess the strength of OOB and UOB with good accuracy and robustness.
An Algebraic Approach to Ensemble Clustering

In clustering, consensus clustering aims at providing a single partition fitting a consensus from a set of independently generated. Common procedures, which are mainly statistical and graph-based, are recognized for their robustness and ability to scale-up. In this paper, we provide a complementary and original viewpoint over consensus clustering, by means of algebraic definitions which allow to ascertain the nature of available inferences in a systematic approach (e.g. a knowledge base). We found our approach on the lattice of partitions, for which we shall disclose how some operators can be added with the aim to express a formula representing the consensus. We show that adopting an incremental approach may assist to retain significant amount of aggregate data which fits well with the set of input clustering's. Beyond that ability to model formulae, we also note that its potential cannot be easily captured through such a logical system. It is due to the volatile nature of handling partitions which finally impacts on ability to draw some valuable conclusions.
Realization of Boolean Functions Using Binary Pi-sigma Networks

This paper introduces a higher-order neural network called the Binary Pi-sigma Network (BPSN), which is a feedforward network with a single \hidden" layer and product units in the output layer. As training proceeds, the BPSN forms an internal representation of the conjunctive normal form expression corresponding to the Boolean function to be learned. This enables the network to have a regular structure and to exhibit fast learning. We formally prove that the BPSN can realize any Boolean function. Simulation results show that the network converges very fast and in a stable manner.
Effects of Diversity Experiences on Critical Thinking Skills: Who Benefits?

This study analyzed data from the Wabash National Study of Liberal Arts Education to estimate the unique effects of exposure to classroom diversity and involvement in interactional diversity on growth in critical thinking skills during the fi rst year of college. Net of important confounding infl uences, neither classroom nor interactional diversity had signifi cant general effects on critical thinking. However, interactional diversity did positively infl uence critical thinking skills for White students and for students who were the least well prepared academically for college.