nils-herrmann/clean_scirepeval huggingface dataset:
Sarcasm Detection: Beyond Machine Learning Algorithms

Noise in online networks especially knowledge networks such as Quora, Yahoo! Q&A, reddit can be attributed to jokes, redundancy, insults, sarcasm. As the size of the content on these websites grows in a manner not possible to be monitored manually, there is a need to automatically detect the undesired text to increase the signal (useful content) to noise ratio. Popular machine learning algorithms does a good job at predicting noise in online social networks, such as sarcasm in Twitter, Davidov et. al. (2010) . However, sarcasm detection remains a challenge for learning algorithms because of the complexity involved in learning the patterns. Some statement in topic Physics on Quora might be sarcastic without containing any word features normally found in general sarcastic sentences. For instance: “I know that this defies the law of gravity, but, you see, I never studied law” is a joke without containing any typical word features like oh!, !, yeah! etc., found in sarcastic comments. However to tag it as a sarcasm, reader must know the context i.e. law of gravity and law subject. Even humans will have difficult time classifying whether a piece of text is sarcastic or not without knowing the context. Since humans need context to determine sarcasm, so must be the learning algorithms as experimented by Wallace et. al. (2014) . This generates a need to develop intelligent machine learning algorithms which can take complex context and subject matter knowledge to detect specialized sarcasm. BODY Learning Algorithms just like humans need context to detect sarcasm.
Progressive Web Apps: The Possible Web-native Unifier for Mobile Development

A recent advancement of the mobile web has enabled features previously only found in natively developed apps. Thus, arduous development for several platforms or using cross-platform approaches was required. The novel approach, coined Progressive Web Apps, can be implemented through a set of concepts and technologies on any web site that meets certain requirements. In this paper, we argue for progressive web apps as a possibly unifying technology for web apps and native apps. After an introduction of features, we scrutinize the performance. Two cross-platform mobile apps and one Progressive Web App have been developed for comparison purposes, and provided in an open source repository for results’ validity verification. We aim to spark interest in the academic community, as a lack of academic involvement was identified as part of the literature search.
Knowledge Graph Embedding via Dynamic Mapping Matrix

Knowledge graphs are useful resources for numerous AI applications, but they are far from completeness. Previous work such as TransE, TransH and TransR/CTransR regard a relation as translation from head entity to tail entity and the CTransR achieves state-of-the-art performance. In this paper, we propose a more fine-grained model named TransD, which is an improvement of TransR/CTransR. In TransD, we use two vectors to represent a named symbol object (entity and relation). The first one represents the meaning of a(n) entity (relation), the other one is used to construct mapping matrix dynamically. Compared with TransR/CTransR, TransD not only considers the diversity of relations, but also entities. TransD has less parameters and has no matrix-vector multiplication operations, which makes it can be applied on large scale graphs. In Experiments, we evaluate our model on two typical tasks including triplets classification and link prediction. Evaluation results show that our approach outperforms stateof-the-art methods.
Intelligent forest fire monitoring system

This paper presents iForestFire, an Environmental Monitoring Information System for forest fire protection. The system is composed of several components, each having a particular function. Automatic fire detection is a crucial component of the system. It is based on various complex image processing algorithms. Complexity of the system also emerges from integration, based on multi agent technology, of different environment information. The presented system contributes to the environment protection and is in use in Croatia for several years.
An Agile Approach to Capturing Requirements and Traceability

Agile methodologies are gaining popularity quickly, receiving increasing support from the software development community. Current requirements engineering practices have addressed traceability approaches for well defined phase-driven development models. Echo is a tool-based approach that provides for the implicit recording and management of relationships between conversations about requirements, specifications, and subsequent design decisions. By providing a means to capture requirements in an informal manner and later restructure the information to suit formal requirements specifications, Echo aims to solve the problems of applying traditional requirements engineering practices to agile development methods making available the demonstrated benefits of requirements traceability – a key enabler for large-scale change management.
Neural data mining for credit card fraud detection

Due to a rapid advancement in the electronic commerce technology, use of credit cards has dramatically increased. As credit card becomes the most popular mode of payment, credit card frauds are becoming increasingly rampant in recent years. In this paper, we model the sequence of operations in credit card transaction processing using a confidence-based neural network. Receiver operating characteristic (ROC) analysis technology is also introduced to ensure the accuracy and effectiveness of fraud detection. A neural network is initially trained with synthetic data. If an incoming credit card transaction is not accepted by the trained neural network model (NNM) with sufficiently low confidence, it is considered to be fraudulent. This paper shows how confidence value, neural network algorithm and ROC can be combined successfully to perform credit card fraud detection.
Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm.

In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.
Polyhedral Source-to-Source Compiler

This paper describes a tool which enables source to source compilation. Implemented Polyhedral Source-to-Source Compiler (PSSC) is based on Polly compiler and LLVM infrastructure and it enables automatic recognition of parallel regions of C/C++ code and annotating them with OpenMP / OpenACC pragmas. The analysis of the input code is done by Polly compiler and then the results are mapped to original source code. This approach allows automatic parallelization of old code, it also ensures high level of code portability and it provides the possibility of manual optimization of the output code. Experimental results of the PSSC compiler have shown that the proposed compiler is able to reach the comparable performance as the original Polly compiler does.
The technological pedagogical content knowledge framework

The increasingly ubiquitous availability of digital and networked tools has the potential to fundamentally transform the teaching and learning process. Research on the instructional uses of technology, however, has revealed that teachers often lack the knowledge to successfully integrate technology in their teaching and their attempts tend to be limited in scope, variety, and depth. Thus, technology is used more as “ef fi ciency aids and extension devices” (McCormick & Scrimshaw, 2001 , p. 31) rather than as tools that can “transform the nature of a subject at the most fundamental level” (p. 47). One way in which researchers have tried to better understand how teachers may better use technology in their classrooms has focused on the kinds of knowledge that teachers require Abstract In this chapter, we introduce a framework, called technological pedagogical content knowledge (or TPACK for short), that describes the kinds of knowledge needed by a teacher for effective technology integration. The TPACK framework emphasizes how the connections among teachers’ understanding of content, pedagogy, and technology interact with one another to produce effective teaching. Even as a relatively new framework, the TPACK framework has signi fi cantly in fl uenced theory, research, and practice in teacher education and teacher professional development. In this chapter, we describe the theoretical underpinnings of the framework, and explain the relationship between TPACK and related constructs in the educational technology literature. We outline the various approaches teacher educators have used to develop TPACK in preand in-service teachers, and the theoretical and practical issues that these professional development efforts have illuminated. We then review the widely varying approaches to measuring TPACK, with an emphasis on the interaction between form and function of the assessment, and resulting reliability and validity outcomes for the various approaches. We conclude with a summary of the key theoretical, pedagogical, and methodological issues related to TPACK, and suggest future directions for researchers, practitioners, and teacher educators.
Effectiveness and Efficiency of Open Relation Extraction

A large number of Open Relation Extraction approaches have been proposed recently, covering a wide range of NLP machinery, from “shallow” (e.g., part-of-speech tagging) to “deep” (e.g., semantic role labeling–SRL). A natural question then is what is the tradeoff between NLP depth (and associated computational cost) versus effectiveness. This paper presents a fair and objective experimental comparison of 8 state-of-the-art approaches over 5 different datasets, and sheds some light on the issue. The paper also describes a novel method, EXEMPLAR, which adapts ideas from SRL to less costly NLP machinery, resulting in substantial gains both in efficiency and effectiveness, over binary and n-ary relation extraction tasks.
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence

Deep models that are both effective and explainable are desirable in many settings; prior explainable models have been unimodal, offering either image-based visualization of attention weights or text-based generation of post-hoc justifications. We propose a multimodal approach to explanation, and argue that the two modalities provide complementary explanatory strengths. We collect two new datasets to define and evaluate this task, and propose a novel model which can provide joint textual rationale generation and attention visualization. Our datasets define visual and textual justifications of a classification decision for activity recognition tasks (ACT-X) and for visual question answering tasks (VQA-X). We quantitatively show that training with the textual explanations not only yields better textual justification models, but also better localizes the evidence that supports the decision. We also qualitatively show cases where visual explanation is more insightful than textual explanation, and vice versa, supporting our thesis that multimodal explanation models offer significant benefits over unimodal approaches.
An Information Theoretic Analysis of Random Number Generator based on Cellular Automaton

Realization of Randomness had always been a controversial concept with great importance both from theoretical and practical Perspectives. This realization has been revolutionized in the light of recent studies especially in the realms of Chaos Theory, Algorithmic Information Theory and Emergent behavior in complex systems. We briefly discuss different definitions of Randomness and also different methods for generating it. The connection between all these approaches and the notion of Normality as the necessary condition of being unpredictable would be discussed. Then a complex-system-based Random Number Generator would be introduced. We will analyze its paradoxical features (Conservative Nature and reversibility in spite of having considerable variation) by using information theoretic measures in connection with other measures. The evolution of this Random Generator is equivalent to the evolution of its probabilistic description in terms of probability distribution over blocks of different lengths. By getting the aid of simulations we will show the ability of this system to preserve normality during the process of coarse graining. Random number generators; entropy; correlation information; elementary cellular automata; reversibility
TumbleBit: An Untrusted Bitcoin-Compatible Anonymous Payment Hub

This paper presents TumbleBit, a new unidirectional unlinkable payment hub that is fully compatible with today’s Bitcoin protocol. TumbleBit allows parties to make fast, anonymous, off-blockchain payments through an untrusted intermediary called the Tumbler. TumbleBit’s anonymity properties are similar to classic Chaumian eCash: no one, not even the Tumbler, can link a payment from its payer to its payee. Every payment made via TumbleBit is backed by bitcoins, and comes with a guarantee that Tumbler can neither violate anonymity, nor steal bitcoins, nor “print money” by issuing payments to itself. We prove the security of TumbleBit using the real/ideal world paradigm and the random oracle model. Security follows from the standard RSA assumption and ECDSA unforgeability. We implement TumbleBit, mix payments from 800 users and show that TumbleBit’s offblockchain payments can complete in seconds.
Predicting postpartum changes in emotion and behavior via social media

We consider social media as a promising tool for public health, focusing on the use of Twitter posts to build predictive models about the forthcoming influence of childbirth on the behavior and mood of new mothers. Using Twitter posts, we quantify postpartum changes in 376 mothers along dimensions of social engagement, emotion, social network, and linguistic style. We then construct statistical models from a training set of observations of these measures before and after the reported childbirth, to forecast significant postpartum changes in mothers. The predictive models can classify mothers who will change significantly following childbirth with an accuracy of 71%, using observations about their prenatal behavior, and as accurately as 80-83% when additionally leveraging the initial 2-3 weeks of postnatal data. The study is motivated by the opportunity to use social media to identify mothers at risk of postpartum depression, an underreported health concern among large populations, and to inform the design of low-cost, privacy-sensitive early-warning systems and intervention programs aimed at promoting wellness postpartum.
Spoofing 2D Face Detection: Machines See People Who Aren't There

Machine learning is increasingly used to make sense of the physical world yet may suffer from adversarial manipulation. We examine the Viola-Jones 2D face detection algorithm to study whether images can be created that humans do not notice as faces yet the algorithm detects as faces. We show that it is possible to construct images that Viola-Jones recognizes as containing faces yet no human would consider a face. Moreover, we show that it is possible to construct images that fool facial detection even when they are printed and then photographed.
On the energy (in)efficiency of Hadoop clusters

Distributed processing frameworks, such as Yahoo!'s Hadoop and Google's MapReduce, have been successful at harnessing expansive datacenter resources for large-scale data analysis. However, their effect on datacenter energy efficiency has not been scrutinized. Moreover, the filesystem component of these frameworks effectively precludes scale-down of clusters deploying these frameworks (i.e. operating at reduced capacity). This paper presents our early work on modifying Hadoop to allow scale-down of operational clusters. We find that running Hadoop clusters in fractional configurations can save between 9% and 50% of energy consumption, and that there is a tradeoff between performance energy consumption. We also outline further research into the energy-efficiency of these frameworks.
Trolling the trolls: Online forum users constructions of the nature and properties of trolling

Trolling’ refers to a specific type of malicious online behaviour, intended to disrupt interactions, aggravate interactional partners and lure them into fruitless argumentation. However, as with other categories, both ‘troll’ and ‘trolling’ may have multiple, inconsistent and incompatible meanings, depending upon the context in which the term is used and the aims of the person using the term. Drawing data from 15 online fora and newspaper comment threads, this paper explores how online users mobilise and make use of the term ‘troll’. Data was analysed from a discursive psychological perspective. Four repertoires describing trolls were identified in posters online messages: 1) that trolls are easily identifiable, 2) nostalgia, 3) vigilantism and 4) that trolls are nasty. A final theme follows these repertoires – that of identifying trolls. Analysis also revealed that despite repertoire 01, identifying trolls is not a simple and straight-forward task. Similarly to any other rhetorical category, there are tensions inherent in posters accounts of nature and acceptability of trolling. Neither the category ‘troll’ nor the action of ‘trolling’ has a single, fixed meaning. Either action may be presented as desirable or undesirable, depending upon the aims of the poster at the time of posting.
An Evidence-Based Model of Work Engagement

Employees who are engaged in their work are fully connected with their work roles. They are bursting with energy, dedicated to their work, and immersed in their work activities. This article presents an overview of the concept of work engagement. I discuss the antecedents and consequences of engagement. The review shows that job and personal resources are the main predictors of engagement. These resources gain their salience in the context of high job demands. Engaged workers are more open to new information, more productive, and more willing to go the extra mile. Moreover, engaged workers proactively change their work environment in order to stay engaged. The findings of previous studies are integrated in an overall model that can be used to develop work engagement and advance job performance in today’s workplace.
Tunnel FET technology: A reliability perspective

Tunneling-field-effect-transistor (TFET) has emerged as an alternative for conventional CMOS by enabling the supply voltage (VDD) scaling in ultra-low power, energy efficient computing, due to its sub-60 mV/ decade sub-threshold slope (SS). Given its unique device characteristics such as the asymmetrical source/drain design induced uni-directional conduction, enhanced on-state Miller capacitance effect and steep switching at low voltages, TFET based circuit design requires strong interactions between the device-level and the circuit-level to explore the performance benefits, with certain modifications of the conventional CMOS circuits to achieve the functionality and optimal energy efficiency. Because TFET operates at low supply voltage range (VDD < 0:5 V) to outperform CMOS, reliability issues can have profound impact on the circuit design from the practical application perspective. In this review paper, we present recent development on Tunnel FET device design, and modeling technique for circuit implementation and performance benchmarking. We focus on the reliability issues such as soft-error, electrical noise and process variation, and their impact on TFET based circuit performance compared to sub-threshold CMOS. Analytical models of electrical noise and process variation are also discussed for circuit-level
Topic model based behaviour modeling and clustering analysis for wireless network users

User behaviour analysis based on traffic log in wireless networks can be beneficial to many fields in real life: not only for commercial purposes, but also for improving network service quality and social management. We cluster users into groups marked by the most frequently visited websites to find their preferences. In this paper, we propose a user behaviour model based on Topic Model from document classification problems. We use the logarithmic TF-IDF (term frequency - inverse document frequency) weighing to form a high-dimensional sparse feature matrix. Then we apply LSA (Latent semantic analysis) to deduce the latent topic distribution and generate a low-dimensional dense feature matrix. K-means++, which is a classic clustering algorithm, is then applied to the dense feature matrix and several interpretable user clusters are found. Moreover, by combining the clustering results with additional demographical information, including age, gender, and financial information, we are able to uncover more realistic implications from the clustering results.
Nanotechnology and its Applications in Medicine

Advancement in the field of nanotechnology and its applications to the field of medicines and pharmaceuticals has revolutionized the twentieth century. Nanotechnology  is the study of extremely small structures. The prefix “nano” is a Greek word which means “dwarf ”. The word “nano” means very small or miniature size. Nanotechnology is the treatment of individual atoms, molecules, or compounds into structures to produce materials and devices with special properties. Nanotechnology involve work from top down i.e. reducing the size of large structures to smallest structure e.g. photonics applications in nano electronics and nano engineering, top-down or the bottom up, which involves changing individual atoms and molecules into nanostructures and more closely resembles chemistry biology.