nils-herrmann/clean_scirepeval huggingface dataset:
Compiling PCRE to FPGA for accelerating SNORT IDS

Deep Payload Inspection systems like SNORT and BRO utilize regular expression for their rules due to their high expressibility and compactness. The SNORT IDS system uses the PCRE Engine for regular expression matching on the payload. The software based PCRE Engine utilizes an NFA engine based on certain opcodes which are determined by the regular expression operators in a rule. Each rule in the SNORT ruleset is translated by PCRE compiler into an unique regular expression engine. Since the software based PCRE engine can match the payload with a single regular expression at a time, and needs to do so for multiple rules in the ruleset, the throughput of the SNORT IDS system dwindles as each packet is processed through a multitude of regular expressions.
 In this paper we detail our implementation of hardware based regular expression engines for the SNORT IDS by transforming the PCRE opcodes generated by the PCRE compiler from SNORT regular expression rules. Our compiler generates VHDL code corresponding to the opcodes generated for the SNORT regular expression rules. We have tuned our hardware implementation to utilize an NFA based regular expression engine, using greedy quantifiers, in much the same way as the software based PCRE engine. Our system implements a regular expression only once for each new rule in the SNORT ruleset, thus resulting in a fast system that scales well with new updates. We implement two hundred PCRE engines based on a plethora of SNORT IDS rules, and use a Virtex-4 LX200 FPGA, on the SGI RASC RC 100 Blade connected to the SGI ALTIX 4700 supercomputing system as a testbed. We obtain an interface through-put of (12.9 GBits/s) and also a maximum speedup of 353X over software based PCRE execution.
Adaptive increase in sample size when interim results are promising: a practical guide with examples.

This paper discusses the benefits and limitations of adaptive sample size re-estimation for phase 3 confirmatory clinical trials. Comparisons are made with more traditional fixed sample and group sequential designs. It is seen that the real benefit of the adaptive approach arises through the ability to invest sample size resources into the trial in stages. The trial starts with a small up-front sample size commitment. Additional sample size resources are committed to the trial only if promising results are obtained at an interim analysis. This strategy is shown through examples of actual trials, one in neurology and one in cardiology, to be more advantageous than the fixed sample or group sequential approaches in certain settings. A major factor that has generated controversy and inhibited more widespread use of these methods has been their reliance on non-standard tests and p-values for preserving the type-1 error. If, however, the sample size is only increased when interim results are promising, one can dispense with these non-standard methods of inference. Therefore, in the spirit of making adaptive increases in trial size more widely appealing and readily implementable we here define those promising circumstances in which a conventional final inference can be performed while preserving the overall type-1 error. Methodological, regulatory and operational issues are examined.
Binarized Neural Networks

In this work we introduce a binarized deep neural network (BDNN) model. BDNNs are trained using a novel binarized back propagation algorithm (BBP), which uses binary weights and binary neurons during the forward and backward propagation, while retaining precision of the stored weights in which gradients are accumulated. At test phase, BDNNs are fully binarized and can be implemented in hardware with low circuit complexity. The proposed binarized networks can be implemented using binary convolutions and proxy matrix multiplications with only standard binary XNOR and population count (popcount) operations. BBP is expected to reduce energy consumption by at least two orders of magnitude when compared to the hardware implementation of existing training algorithms. We obtained near state-of-the-art results with BDNNs on the permutation-invariant MNIST, CIFAR-10 and SVHN datasets.
Evidence for a collective intelligence factor in the performance of human groups.

Psychologists have repeatedly shown that a single statistical factor--often called "general intelligence"--emerges from the correlations among people's performance on a wide variety of cognitive tasks. But no one has systematically examined whether a similar kind of "collective intelligence" exists for groups of people. In two studies with 699 people, working in groups of two to five, we find converging evidence of a general collective intelligence factor that explains a group's performance on a wide variety of tasks. This "c factor" is not strongly correlated with the average or maximum individual intelligence of group members but is correlated with the average social sensitivity of group members, the equality in distribution of conversational turn-taking, and the proportion of females in the group.
Study of the lithium diffusion properties and high rate performance of TiNb6O17 as an anode in lithium secondary battery

TiNb6O17 and TiNb2O7 were synthesized using a solid-state method. The techniques were used to assess the electrochemical performance and lithium diffusion kinetics of TiNb6O17 related to the unit cell volume with TiNb2O7. The charge-discharge curves and cyclic voltammetry revealed TiNb6O17 to have a similar redox potential to TiNb2O7 as well as a high discharge capacity. The rate performance of TiNb6O17 was measured using a rate capability test. SSCV and EIS showed that TiNb6O17 had higher lithium diffusion coefficients during the charging. From GITT, the lithium diffusion coefficients at the phase transition region showed the largest increase from TiNb2O7 to TiNb6O17.
Fundamental Components of the Gameplay Experience: Analysing Immersion

This co-authored paper is based on research that originated in 2003 when our team started a series of extensive field studies into the character of gameplay experiences. Originally within the Children as the Actors of Game Cultures research project, our aim was to better understand why particularly young people enjoy playing games, while also asking their parents how they perceive gaming as playing partners or as close observers. Gradually our in-depth interviews started to reveal a complex picture of more general relevance, where personal experiences, social contexts and cultural practices all came together to frame gameplay within something we called game cultures. Culture was the keyword, since we were not interested in studying games and play experiences in isolation, but rather as part of the rich meaning-making practices of lived reality.
Brief meditation training can improve perceived stress and negative mood.

To test a brief, non-sectarian program of meditation training for effects on perceived stress and negative emotion, and to determine effects of practice frequency and test the moderating effects of neuroticism (emotional lability) on treatment outcome.



The study used a single-group, open-label, pre-test post-test design conducted in the setting of a university medical center.



Healthy adults (N=200) interested in learning meditation for stress-reduction were enrolled. One hundred thirty-three (76% females) completed at least 1 follow-up visit and were included in data analyses.



Participants learned a simple mantra-based meditation technique in 4, 1-hour small-group meetings, with instructions to practice for 15-20 minutes twice daily. Instruction was based on a psychophysiological model of meditation practice and its expected effects on stress.



Baseline and monthly follow-up measures of Profile of Mood States; Perceived Stress Scale; State-Trait Anxiety Inventory (STAI); and Brief Symptom Inventory (BSI). Practice frequency was indexed by monthly retrospective ratings. Neuroticism was evaluated as a potential moderator of treatment effects.



All 4 outcome measures improved significantly after instruction, with reductions from baseline that ranged from 14% (STAI) to 36% (BSI). More frequent practice was associated with better outcome. Higher baseline neuroticism scores were associated with greater improvement.



Preliminary evidence suggests that even brief instruction in a simple meditation technique can improve negative mood and perceived stress in healthy adults, which could yield long-term health benefits. Frequency of practice does affect outcome. Those most likely to experience negative emotions may benefit the most from the intervention.
Theory and methods for sampling in fractional domains

The fractional Fourier transform (FRFT) and linear canonical transform (LCT) have been shown to be one of powerful tools in signal processing community. While sampling is a fundamental and core topic in the field of signal processing. The research of sampling theory and methods in FRFT and LCT domains received much interests in recent years. In this paper, we review the developments and research trends of sampling theories and methods associate with FRFT and LCT.
Conceptualizing smart city with dimensions of technology, people, and institutions

This conceptual paper discusses how we can consider a particular city as a smart one, drawing on recent practices to make cities smart. A set of the common multidimensional components underlying the smart city concept and the core factors for a successful smart city initiative is identified by exploring current working definitions of smart city and a diversity of various conceptual relatives similar to smart city. The paper offers strategic principles aligning to the three main dimensions (technology, people, and institutions) of smart city: integration of infrastructures and technology-mediated services, social learning for strengthening human infrastructure, and governance for institutional improvement and citizen engagement.
Managing Brands in the Social Media Environment

The dynamic, ubiquitous, and often real-time interaction enabled by social media significantly changes the landscape for brand management. A deep understanding of this change is critical since it may affect a brand’s performance substantially. Literature about social media’s impact on brands is evolving, but lacks a systematic identification of key challenges related to managing brands in this new environment. This paper reviews existing research and introduces a framework of social media’s impact on brand management. It argues that consumers are becoming pivotal authors of brand stories due to new dynamic networks of consumers and brands formed through social media and the easy sharing of brand experiences in such networks. Firms need to pay attention to such consumer-generated brand stories to ensure a brand’s success in the marketplace. The authors identify key research questions related to the phenomenon and the challenges in coordinating consumerand firm-generated brand stories.
Fog and IoT: An Overview of Research Opportunities

Fog is an emergent architecture for computing, storage, control, and networking that distributes these services closer to end users along the cloud-to-things continuum. It covers both mobile and wireline scenarios, traverses across hardware and software, resides on network edge but also over access networks and among end users, and includes both data plane and control plane. As an architecture, it supports a growing variety of applications, including those in the Internet of Things (IoT), fifth-generation (5G) wireless systems, and embedded artificial intelligence (AI). This survey paper summarizes the opportunities and challenges of fog, focusing primarily in the networking context of IoT.
Match analysis in football: a systematic review.

The main focus of this paper was to review the available literature on match analysis in adult male football. The most common research topics were identified, their methodologies described and the evolutionary tendencies of this research area systematised. A systematic review of Institute for Scientific Information (ISI) Web of Knowledge database was performed according to PRISMA (Preferred Reporting Items for Systematic reviews and Meta-analyses) guidelines. The following keywords were used: football and soccer, each one associated with the terms: match analysis, performance analysis, notational analysis, game analysis, tactical analysis and patterns of play. Of 2732 studies initially identified, only 53 were fully reviewed, and their outcome measures abstracted and analysed. Studies that fit all inclusion criteria were organised according to their research design as descriptive, comparative or predictive. Results showed that 10 studies focused predominantly on a description of technical, tactical and physical performance variables. From all comparative studies, the dependent variables more frequently used were "playing position" and "competitive level". Even though the literature stresses the importance of developing predictive models of sports performance, only few studies (n = 8) have focused on modelling football performance. Situational variables like game location, quality of opposing teams, match status and match half have been progressively included as object of research, since they seem to work as effective covariables of football performance. Taking into account the limitations of the reviewed studies, future research should provide comprehensive operational definitions for the studied variables, use standardised categories and description of activities and participants, and consider integrating the situational and interactional contexts into the analysis of football performance.
Differential Privacy

In 1977 Dalenius articulated a desideratum for statistical databases: nothing about an individual should be learnable from the database that cannot be learned without access to the database. We give a general impossibility result showing that a formalization of Dalenius’ goal along the lines of semantic security cannot be achieved. Contrary to intuition, a variant of the result threatens the privacy even of someone not in the database. This state of affairs suggests a new measure, differential privacy, which, intuitively, captures the increased risk to one’s privacy incurred by participating in a database. The techniques developed in a sequence of papers [8, 13, 3], culminating in those described in , can achieve any desired level of privacy under this measure. In many cases, extremely accurate information about the database can be provided while simultaneously ensuring very high levels of privacy.
A Comparative Study Between Naive Bayes and Neural Network (MLP) Classifier for Spam Email Detection

The continue demands of internet and email communication has creating spam emails also known unsolicited bulk mails. These emails enter bypass in our mail box and affect our system. Different filtering techniques are using to detect these emails such as Random Forest, Naive Bayesian, SVM and Neural Network. In this paper, we compare the different performance matrices using Bayesian Classification and Neural Network approaches of data mining that are
Parallel data processing with MapReduce: a survey

A prominent parallel data processing tool MapReduce is gaining significant momentum from both industry and academia as the volume of data to analyze grows rapidly. While MapReduce is used in many areas where massive data analysis is required, there are still debates on its performance, efficiency per node, and simple abstraction. This survey intends to assist the database and open source communities in understanding various technical aspects of the MapReduce framework. In this survey, we characterize the MapReduce framework and discuss its inherent pros and cons. We then introduce its optimization strategies reported in the recent literature. We also discuss the open issues and challenges raised on parallel data analysis with MapReduce.
Learning to Diagnose with LSTM Recurrent Neural Networks

Clinical medical data, especially in the intensive care unit (ICU), consist of multivariate time series of observations. For each patient visit (or episode), sensor data and lab test results are recorded in the patient’s Electronic Health Record (EHR). While potentially containing a wealth of insights, the data is difficult to mine effectively, owing to varying length, irregular sampling and missing data. Recurrent Neural Networks (RNNs), particularly those using Long Short-Term Memory (LSTM) hidden units, are powerful and increasingly popular models for learning from sequence data. They effectively model varying length sequences and capture long range dependencies. We present the first study to empirically evaluate the ability of LSTMs to recognize patterns in multivariate time series of clinical measurements. Specifically, we consider multilabel classification of diagnoses, training a model to classify 128 diagnoses given 13 frequently but irregularly sampled clinical measurements. First, we establish the effectiveness of a simple LSTM network for modeling clinical data. Then we demonstrate a straightforward and effective training strategy in which we replicate targets at each sequence step. Trained only on raw time series, our models outperform several strong baselines, including a multilayer perceptron trained on hand-engineered features.
Fostering Creativity through Education — A Conceptual Framework of Creative Pedagogy

Capacities and qualities of creativity have been identified by researchers and strategies in fostering children’s creative thinking skills were proposed to create supportive environments in an educational setting. There is little consistent rhetoric, however, among these insights and strategies concerning different aspects of fostering creativity. In light of this, a three-element framework of creative pedagogy is proposed to offer a more holistic view of enhancing creativity through teaching, to cover the aspect of creative learning which was overlooked in the past, and to provide a different explanation to some arguments about teaching creativity. This framework is also a starting point for studies which intend to understand the teachers and pupils’ responses to creative pedagogy, and to provide implications for applying creative pedagogy in a classroom and in Asian context as well. In the end, several possible routes are suggested for future research in creative pedagogy.
BWIBots: A platform for bridging the gap between AI and human-robot interaction research

Recent progress in both Artificial Intelligence (AI) and Robotics have enabled the development of general purpose robot platforms that are capable of executing a wide variety of complex, temporally extended service tasks in open environments. This article introduces a novel, custom-designed multi-robot platform for research on AI, robotics, and especially Human-Robot Interaction (HRI) for service robots. Called BWIBots, the robots were designed as a part of the Building-Wide Intelligence (BWI) project at the University of Texas at Austin. The article begins with a description of, and justification for, the hardware and software design decisions underlying the BWIBots, with the aim of informing the design of such platforms in the future. It then proceeds to present an overview of various research contributions that have enabled the BWIBots to better (i) execute action sequences to complete user requests, (ii) efficiently ask questions to resolve user requests, (iii) understand human commands given in natural language, and (iv) understand human intention from afar. The article concludes with a look forward towards future research opportunities and applications enabled by the BWIBot platform.
Crime Analysis Using K-Means Clustering

Analysis of crime is essential for providing safety and security to the civilian population. Using data mining, we can discover critical information which can help local authorities detect crime and areas of importance. The main purpose of this paper is to analyze the crime which entails theft, homicide and various drug offences which also include suspicious activities, noise complaints and burglar alarm by using qualitative and quantitative approach. Using K-means clustering data mining approach on a crime dataset from New South Wales region of Australia, crime rates of each type of crimes and cities with high crime rates have been found.
Medical Semantic Similarity with a Neural Language Model

Advances in neural network language models have demonstrated that these models can effectively learn representations of words meaning. In this paper, we explore a variation of neural language models that can learn on concepts taken from structured ontologies and extracted from free-text, rather than directly from terms in free-text.
 This model is employed for the task of measuring semantic similarity between medical concepts, a task that is central to a number of techniques in medical informatics and information retrieval. The model is built with two medical corpora (journal abstracts and patient records) and empirically validated on two ground-truth datasets of human-judged concept pairs assessed by medical professionals. Empirically, our approach correlates closely with expert human assessors (≈0.9) and outperforms a number of state-of-the-art benchmarks for medical semantic similarity.
 The demonstrated superiority of this model for providing an effective semantic similarity measure is promising in that this may translate into effectiveness gains for techniques in medical information retrieval and medical informatics (e.g., query expansion and literature-based discovery).
CyberPhishing: A Game-Based Platform for Phishing Awareness Testing

Phishing attacks sap billions of dollars annually from unsuspecting individuals while compromising individual privacy. Companies and privacy advocates seek ways to better educate the populace against such attacks. Current approaches examining phishing include test-based techniques that ask subjects to classify content as phishing or not and inthe- wild techniques that directly observe subject behavior through distribution of faked phishing attacks. Both approaches have issues. Test-based techniques produce less reliable data since subjects may adjust their behavior with the expectation of seeing phishing stimuli, while in-the-wild studies can put subjects at risk through lack of consent or exposure of data. This paper examines a third approach that seeks to incorporate game-based learning techniques to combine the realism of in-thewild approaches with the training features of testing approaches. We propose a three phase experiment to test our approach on our CyberPhishing simulation platform, and present the results of phase one.
A Dataset and Architecture for Visual Reasoning with a Working Memory

A vexing problem in artificial intelligence is reasoning about events that occur in complex, changing visual stimuli such as in video analysis or game play. Inspired by a rich tradition of visual reasoning and memory in cognitive psychology and neuroscience, we developed an artificial, configurable visual question and answer dataset (COG) to parallel experiments in humans and animals. COG is much simpler than the general problem of video analysis, yet it addresses many of the problems relating to visual and logical reasoning and memory – problems that remain challenging for modern deep learning architectures. We additionally propose a deep learning architecture that performs competitively on other diagnostic VQA datasets (i.e. CLEVR) as well as easy settings of the COG dataset. However, several settings of COG result in datasets that are progressively more challenging to learn. After training, the network can zero-shot generalize to many new tasks. Preliminary analyses of the network architectures trained on COG demonstrate that the network accomplishes the task in a manner interpretable to humans.