nils-herrmann/clean_scirepeval huggingface dataset:
What Lies Beneath : The Linguistic Traces of Deception in Online Dating Profiles

This article investigates whether deceptions in online dating profiles correlate with changes in the way daters write about themselves in the free-text portion of the profile, and whether these changes are detectable by both computerized linguistic analyses and human judges. Computerized analyses (Study 1) found that deceptions manifested themselves through linguistic cues pertaining to (a) liars’ emotions and cognitions and (b) liars’ strategic efforts to manage their self-presentations. Technological affordances (i.e., asynchronicity and editability) affected the production of cognitive cues more than that of emotional cues. Human judges (Study 2) relied on different and nonpredictive linguistic cues to assess daters’ trustworthiness. The findings inform theories concerned with deception, media, and self-presentation, and also expound on how writing style influences perceived trustworthiness.
Mining Knowledge in Storytelling Systems for Narrative Generation

Storytelling systems are computational systems designed to tell stories. Every story generation system defines its specific knowledge representation for supporting the storytelling process. Thus, there is a shared need amongst all the systems: the knowledge must be expressed unambiguously to avoid inconsistencies. However, when trying to make a comparative assessment between the storytelling systems, there is not a common way for expressing this knowledge. That is when a form of expression that covers the different aspects of the knowledge representations becomes necessary. A suitable solution is the use of a Controlled Natural Language (CNL) which is a good half-way point between natural and formal languages. A CNL can be used as a common medium of expression for this heterogeneous set of systems. This paper proposes the use of Controlled Natural Language for expressing every storytelling system knowledge as a collection of natural language sentences. In this respect, an initial grammar for a CNL is proposed, focusing on certain aspects of this knowledge.
Survival status and predictors of mortality among severely acute malnourished children <5 years of age admitted to stabilization centers in Gedeo Zone: a retrospective cohort study

Despite the existence of standard protocol, many stabilization centers (SCs) continue to experience high mortality of children receiving treatment for severe acute malnutrition. Assessing treatment outcomes and identifying predictors may help to overcome this problem. Therefore, a 30-month retrospective cohort study was conducted among 545 randomly selected medical records of children <5 years of age admitted to SCs in Gedeo Zone. Data was entered by Epi Info version 7 and analyzed by STATA version 11. Cox proportional hazards model was built by forward stepwise procedure and compared by the likelihood ratio test and Harrell's concordance, and fitness was checked by Cox-Snell residual plot. During follow-up, 51 (9.3%) children had died, and 414 (76%) and 26 (4.8%) children had recovered and defaulted (missed follow-up for 2 consecutive days), respectively. The survival rates at the end of the first, second and third weeks were 95.3%, 90% and 85%, respectively, and the overall mean survival time was 79.6 days. Age <24 months (adjusted hazard ratio [AHR] =2.841, 95% confidence interval [CI] =1.101-7.329), altered pulse rate (AHR =3.926, 95% CI =1.579-9.763), altered temperature (AHR =7.173, 95% CI =3.05-16.867), shock (AHR =3.805, 95% CI =1.829-7.919), anemia (AHR =2.618, 95% CI =1.148-5.97), nasogastric tube feeding (AHR =3.181, 95% CI =1.18-8.575), hypoglycemia (AHR =2.74, 95% CI =1.279-5.87) and treatment at hospital stabilization center (AHR =4.772, 95% CI =1.638-13.9) were independent predictors of mortality. The treatment outcomes and incidence of death were in the acceptable ranges of national and international standards. Intervention to further reduce deaths has to focus on young children with comorbidities and altered general conditions.
Biometric cryptosystem based on discretized fingerprint texture descriptors

This paper focuses on a biometric cryptosystem implementation and evaluation based on a number of fingerprint texture descriptors. The texture descriptors, namely, the Gabor filter-based FingerCode, a local binary pattern (LBP), and a local direction pattern (LDP), and their various combinations are considered. These fingerprint texture descriptors are binarized using a biometric discretization method and used in a fuzzy commitment scheme (FCS). We constructed the biometric cryptosystems, which achieve a good performance, by fusing discretized fingerprint texture descriptors and using effective error-correcting codes. We tested the proposed system on a FVC2000 DB2a fingerprint database, and the results demonstrate that the new system significantly improves the performance of the FCS for texture-based
Fast webpage classification using URL features

We demonstrate the usefulness of the uniform resource locator (URL) alone in performing web page classification. This approach is faster than typical web page classification, as the pages do not have to be fetched and analyzed. Our approach segments the URL into meaningful chunks and adds component, sequential and orthographic features to model salient patterns. The resulting features are used in supervised maximum entropy modeling. We analyze our approach's effectiveness on two standardized domains. Our results show that in certain scenarios, URL-based methods approach the performance of current state-of-the-art full-text and link-based methods.
Blockchain Router: A Cross-Chain Communication Protocol

Cross-chain communication is one of the major design considerations in current blockchain systems [4-7] such as Ethereum[8]. Currently, Blockchain operates like information isolated island, they cannot obtain external data or execute transactions on their own.
 Motivated by recent studies [1-3] on blockchain's multiChain framework, we investigate the cross-chain communication. We introduces blockchain router, which empowers blockchains to connect and communicate cross chains. By establishing an economic model, blockchain router enables different blockchains in the network communicate with each other same like Internet network. In the network of blockchain router, some blockchain plays the role of a router which, according to the communication protocol, analyzes and transmits communication requests, dynamically maintaining a topology structure of the blockchain network.
Advanced Persistent Threat: New analysis driven by life cycle phases and their challenges

In a world where highly skilled actors involved in cyber-attacks are constantly increasing and where the associated underground market continues to expand, organizations should adapt their defence strategy and improve consequently their security incident management. In this paper, we give an overview of Advanced Persistent Threats (APT) attacks life cycle as defined by security experts. We introduce our own compiled life cycle model guided by attackers objectives instead of their actions. Challenges and opportunities related to the specific camouflage actions performed at the end of each APT phase of the model are highlighted. We also give an overview of new APT protection technologies and discuss their effectiveness at each one of life cycle phases.
Application of Katharine Kolcaba Comfort Theory in Post Operative Child : Delivering Integrative Comfort Care Intervention by using Theory of Comfort – A Case Study of a 5 year Old Child Admitted in PICU with Laprotomy Experiencing Post Operative Discomfort

To apply Katharine Kolcaba’s nursing theory on post operative child admitted in PICU.  Case study approach was used in applying Katharine Kolcaba theory of comfort on post operative child Application: Katharine Kolcaba comfort theory frame work used to assess patient condition, plan and deliver integrative comfort care interventions The steps followed for data collection planning and implementing effective care were based on the presuppositions of the Katharine Kolcaba theory. The tools used for data collection were Base line information of children. Comfort Behavior check list, Taxonomy structure of comfort or Comfort Grid and Comfort Daisies. . Comfort care interventions were delivered by using nursing process approach. These interventions were addressed across physical, psychological, social, spiritual, and environmental aspects. Result of application of theory were analyzed and demonstrated improvement in child comfort level.
Review of the magnetocaloric effect in manganite materials

A thorough understanding of the magnetocaloric properties of existing magnetic refrigerant materials has been an important issue in magnetic refrigeration technology. This paper reviews a new class of magnetocaloric material, that is, the ferromagnetic perovskite manganites (R1 xMxMnO3, where R 1⁄4 La, Nd, Pr and M 1⁄4 Ca, Sr, Ba, etc.). The nature of these materials with respect to their magnetocaloric properties has been analyzed and discussed systematically. A comparison of the magnetocaloric effect of the manganites with other materials is given. The potential manganites are nominated for a variety of largeand small-scale magnetic refrigeration applications in the temperature range of 100–375K. It is believed that the manganite materials with the superior magnetocaloric properties in addition to cheap materials-processing cost will be the option of future magnetic refrigeration technology. r 2006 Elsevier B.V. All rights reserved.
ConDroid: Targeted Dynamic Analysis of Android Applications

Recent years have seen the development of a multitude of tools for the security analysis of Android applications. A major deficit of current fully automated security analyses, however, is their inability to drive execution to interesting parts, such as where code is dynamically loaded or certain data is decrypted. In fact, security-critical or downright offensive code may not be reached at all by such analyses when dynamically checked conditions are not met by the analysis environment. To tackle this unsolved problem, we propose a tool combining static call path analysis with byte code instrumentation and a heuristic partial symbolic execution, which aims at executing interesting calls paths. It can systematically locate potentially security-critical code sections and instrument applications such that execution of these sections can be observed in a dynamic analysis. Among other use cases, this can be leveraged to force applications into revealing dynamically loaded code, a simple yet effective way to circumvent detection by security analysis software such as the Google Play Store's Bouncer. We illustrate the functionality of our tool by means of a simple logic bomb example and a real-life security vulnerability which is present in hunderd of apps and can still be actively exploited at this time.
Analysis of Credit Card Fraud Detection Techniques

Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. In an era of digitalization, credit card fraud detection is of great importance to financial institutions. In this paper, we analyze credit card fraud detection using different techniques : Bayesian Learning, BLAST-SSAHA Hybridization, Hidden Markov Model, Fuzzy Darwinian detection, Neural Networks, SVM, K-Nearest Neighbour and Naïve Bayes. After analyzing through each technique, our aim is to compare all the techniques based on some parameters. The obtained results from databases of credit card transactions show the power of these techniques in the fight against banking fraud comparing them to others in the same field.
To belong or not to belong, that is the question: terror management and identification with gender and ethnicity.

The terror management prediction that reminders of death motivate in-group identification assumes people view their identifications positively. However, when the in-group is framed negatively, mortality salience should lead to disidentification. Study 1 found that mortality salience increased women's perceived similarity to other women except under gender-based stereotype threat. In Study 2, mortality salience and a negative ethnic prime led Hispanic as well as Anglo participants to derogate paintings attributed to Hispanic (but not Anglo-American) artists. Study 3 added a neutral prime condition and used a more direct measure of psychological distancing. Mortality salience and the negative prime led Hispanic participants to view themselves as especially different from a fellow Hispanic. Implications for understanding in-group derogation and disidentification are briefly discussed.
A 52 $\mu$ W Wake-Up Receiver With $-$ 72 dBm Sensitivity Using an Uncertain-IF Architecture

A dedicated wake-up receiver may be used in wireless sensor nodes to control duty cycle and reduce network latency. However, its power dissipation must be extremely low to minimize the power consumption of the overall link. This paper describes the design of a 2 GHz receiver using a novel ldquouncertain-IFrdquo architecture, which combines MEMS-based high-Q filtering and a free-running CMOS ring oscillator as the RF LO. The receiver prototype, implemented in 90 nm CMOS technology, achieves a sensitivity of -72 dBm at 100 kbps (10-3 bit error rate) while consuming just 52 muW from the 0.5 V supply.
Character-Based LSTM-CRF with Radical-Level Features for Chinese Named Entity Recognition

State-of-the-art systems of Chinese Named Entity Recognition (CNER) require large amounts of hand-crafted features and domainspecific knowledge to achieve high performance. In this paper, we apply a bidirectional LSTM-CRF neural network that utilizes both characterlevel and radical-level representations. We are the first to use characterbased BLSTM-CRF neural architecture for CNER. By contrasting the results of different variants of LSTM blocks, we find the most suitable LSTM block for CNER. We are also the first to investigate Chinese radical-level representations in BLSTM-CRF architecture and get better performance without carefully designed features. We evaluate our system on the third SIGHAN Bakeoff MSRA data set for simplfied CNER task and achieve state-of-the-art performance 90.95% F1.
SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis

SenticNet is a publicly available semantic and affective resource for concept-level sentiment analysis. Rather than using graph-mining and dimensionality-reduction techniques, SenticNet 3 makes use of ‘energy flows’ to connect various parts of extended common and common-sense knowledge representations to one another. SenticNet 3 models nuanced semantics and sentics (that is, the conceptual and affective information associated with multi-word natural language expressions), representing information with a symbolic opacity of an intermediate nature between that of neural networks and typical symbolic systems.
Schemas and memory consolidation.

Memory encoding occurs rapidly, but the consolidation of memory in the neocortex has long been held to be a more gradual process. We now report, however, that systems consolidation can occur extremely quickly if an associative "schema" into which new information is incorporated has previously been created. In experiments using a hippocampal-dependent paired-associate task for rats, the memory of flavor-place associations became persistent over time as a putative neocortical schema gradually developed. New traces, trained for only one trial, then became assimilated and rapidly hippocampal-independent. Schemas also played a causal role in the creation of lasting associative memory representations during one-trial learning. The concept of neocortical schemas may unite psychological accounts of knowledge structures with neurobiological theories of systems memory consolidation.
Current perspectives: the impact of cyberbullying on adolescent health

Cyberbullying has become an international public health concern among adolescents, and as such, it deserves further study. This paper reviews the current literature related to the effects of cyberbullying on adolescent health across multiple studies worldwide and provides directions for future research. A review of the evidence suggests that cyberbullying poses a threat to adolescents' health and well-being. A plethora of correlational studies have demonstrated a cogent relationship between adolescents' involvement in cyberbullying and negative health indices. Adolescents who are targeted via cyberbullying report increased depressive affect, anxiety, loneliness, suicidal behavior, and somatic symptoms. Perpetrators of cyberbullying are more likely to report increased substance use, aggression, and delinquent behaviors. Mediating/moderating processes have been found to influence the relationship between cyberbullying and adolescent health. More longitudinal work is needed to increase our understanding of the effects of cyberbullying on adolescent health over time. Prevention and intervention efforts related to reducing cyberbullying and its associated harms are discussed.
Boosted Generative Models

We propose a novel approach for using unsupervised boosting to create an ensemble of generative models, where models are trained in sequence to correct earlier mistakes. Our metaalgorithmic framework can leverage any existing base learner that permits likelihood evaluation, including recent deep expressive models. Further, our approach allows the ensemble to include discriminative models trained to distinguish real data from model-generated data. We show theoretical conditions under which incorporating a new model in the ensemble will improve the fit and empirically demonstrate the effectiveness of our black-box boosting algorithms on density estimation, classification, and sample generation on benchmark datasets for a wide range of generative models.
Deep learning for sentiment analysis of movie reviews

In this study, we explore various natural language processing (NLP) methods to perform sentiment analysis. We look at two different datasets, one with binary labels, and one with multi-class labels. For the binary classification we applied the bag of words, and skip-gram word2vec models followed by various classifiers, including random forest, SVM, and logistic regression. For the multi-class case, we implemented the recursive neural tensor networks (RNTN). To overcome the high computational cost of training the standard RNTN we introduce the lowrank RNTN, in which the matrices involved in the quadratic term of RNTN are substituted by symmetric low-rank matrices. We show that the low-rank RNTN leads to significant saving in computational cost, while having similar a accuracy as that of RNTN.
Twitter event detection: combining wavelet analysis and topic inference summarization

Today streaming text mining plays an important role within real-time social media mining. Given the amount and cadence of the data generated by those platforms, classical text mining techniques are not suitable to deal with such new mining challenges. Event detection is no exception, available algorithms rely on text mining techniques applied to pre-known datasets processed with no restrictions about computational complexity and required execution time per document analysis. This work presents a lightweight event detection using wavelet signal analysis of hashtag occurrences in the twitter public stream. It also proposes a strategy to describe detected events using a Latent Dirichlet Allocation topic inference model based on Gibbs Sampling. Peak detection using Continuous Wavelet Transformation achieved good results in the identification of abrupt increases on the mentions of specific hashtags. The combination of this method with the extraction of topics from tweets with hashtag mentions proved to be a viable option to summarize detected twitter events in streaming environments.
The sketchy database: learning to retrieve badly drawn bunnies

We present the Sketchy database, the first large-scale collection of sketch-photo pairs. We ask crowd workers to sketch particular photographic objects sampled from 125 categories and acquire 75,471 sketches of 12,500 objects. The Sketchy database gives us fine-grained associations between particular photos and sketches, and we use this to train cross-domain convolutional networks which embed sketches and photographs in a common feature space. We use our database as a benchmark for fine-grained retrieval and show that our learned representation significantly outperforms both hand-crafted features as well as deep features trained for sketch or photo classification. Beyond image retrieval, we believe the Sketchy database opens up new opportunities for sketch and image understanding and synthesis.
Workflow scheduling in cloud computing environment using Cat Swarm Optimization

Cloud computing is new era of network based computing, where resources are distributed over the network and shared among its users. Any user can use these resources through internet on the basis of Pay-As-Per-Use system. A service used by any user can produce a very large amount of data. So in this case, the data transfer cost between two dependent resources will be very high. In addition, a complex application can have a large number of tasks which may cause an increase in total cost of execution of that application, if not scheduled in an optimized way. So to overcome these problems, the authors present a Cat Swarm Optimization (CSO) - based heuristic scheduling algorithm to schedule the tasks of an application onto available resources. The CSO heuristic algorithm considers both data transmission cost between two dependent resources and execution cost of tasks on different resources. The authors experiment with the proposed CSO algorithm using a hypothetical workflow and compare the workflow scheduling results with the existing Particle Swarm Optimization (PSO) algorithm. The experimental results show - (1) CSO gives an optimal task-to-resource (TOR) scheduling scheme that minimizes the total cost, (2) CSO shows an improvement over existing PSO in terms of number of iterations, and (3) CSO ensures fair load distribution on the available resources.
Extending Fitts' Law to Two-Dimensional Tasks

Fitts' law, a one-dimensional model of human movement, is commonly applied to two-dimensional target acquisition tasks on interactive computing systems. For rectangular targets, such as words, it is demonstrated that the model can break down and yield unrealistically low (even negative!) ratings for a task's index of difficulty (ID). The Shannon formulation is shown to partially correct this problem, since ID is always ≥ 0 bits. As well, two alternative interpretations “target width” are introduced that accommodate the two-dimensional nature of tasks. Results of an experiment are presented that show a significant improvement in the model's performance using the suggested changes.