nils-herrmann/clean_scirepeval huggingface dataset:
Causal embeddings for recommendation

Many current applications use recommendations in order to modify the natural user behavior, such as to increase the number of sales or the time spent on a website. This results in a gap between the final recommendation objective and the classical setup where recommendation candidates are evaluated by their coherence with past user behavior, by predicting either the missing entries in the user-item matrix, or the most likely next event. To bridge this gap, we optimize a recommendation policy for the task of increasing the desired outcome versus the organic user behavior. We show this is equivalent to learning to predict recommendation outcomes under a fully random recommendation policy. To this end, we propose a new domain adaptation algorithm that learns from logged data containing outcomes from a biased recommendation policy and predicts recommendation outcomes according to random exposure. We compare our method against state-of-the-art factorization methods, in addition to new approaches of causal recommendation and show significant improvements.
Metrics for Information Security - A literature review

It is important to know how vulnerable systems are for a wide variety of reasons. Information Systems managers have the duty to advise senior management of the level of risks faced by the information systems. Therefore an assessment of the level of risk is necessary. Research work in this area is in its infancy. Further, the efforts are varied and deal with different aspects of the issue. There is no coherent approach. This paper provides a review of the literature and will be presenting a framework for analysis and development of metrics for security at the conference.
KDGAN: Knowledge Distillation with Generative Adversarial Networks

Knowledge distillation (KD) aims to train a lightweight classifier suitable to provide accurate inference with constrained resources in multi-label learning. Instead of directly consuming feature-label pairs, the classifier is trained by a teacher, i.e., a high-capacity model whose training may be resource-hungry. The accuracy of the classifier trained this way is usually suboptimal because it is difficult to learn the true data distribution from the teacher. An alternative method is to adversarially train the classifier against a discriminator in a two-player game akin to generative adversarial networks (GAN), which can ensure the classifier to learn the true data distribution at the equilibrium of this game. However, it may take excessively long time for such a two-player game to reach equilibrium due to high-variance gradient updates. To address these limitations, we propose a three-player game named KDGAN consisting of a classifier, a teacher, and a discriminator. The classifier and the teacher learn from each other via distillation losses and are adversarially trained against the discriminator via adversarial losses. By simultaneously optimizing the distillation and adversarial losses, the classifier will learn the true data distribution at the equilibrium. We approximate the discrete distribution learned by the classifier (or the teacher) with a concrete distribution. From the concrete distribution, we generate continuous samples to obtain low-variance gradient updates, which speed up the training. Extensive experiments using real datasets confirm the superiority of KDGAN in both accuracy and training speed.
Distributed Representations of Words and Phrases and their Compositionality

The recently introduced continuous Skip-gram model is an ef fici nt method for learning high-quality distributed vector representation s that capture a large number of precise syntactic and semantic word relationships. I n this paper we present several extensions that improve both the quality of the vect ors and the training speed. By subsampling of the frequent words we obtain signifi ca t speedup and also learn more regular word representations. We also descr ib a simple alternative to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their ind ifference to word order and their inability to represent idiomatic phrases. For exa mple, the meanings of “Canada” and “Air” cannot be easily combined to obtain “Air C anada”. Motivated by this example, we present a simple method for finding phrase s in t xt, and show that learning good vector representations for millions of p hrases is possible.
Indonesian social media sentiment analysis with sarcasm detection

Sarcasm is considered one of the most difficult problem in sentiment analysis. In our observation on Indonesian social media, for certain topics, people tend to criticize something using sarcasm. Here, we proposed two additional features to detect sarcasm after a common sentiment analysis is conducted. The features are the negativity information and the number of interjection words. We also employed translated SentiWordNet in the sentiment classification. All the classifications were conducted with machine learning algorithms. The experimental results showed that the additional features are quite effective in the sarcasm detection.
A Novel Variational Autoencoder with Applications to Generative Modelling, Classification, and Ordinal Regression

We develop a novel probabilistic generative model based on the variational autoencoder approach. Notable aspects of our architecture are: a novel way of specifying the latent variables prior, and the introduction of an ordinality enforcing unit. We describe how to do supervised, unsupervised and semi-supervised learning, and nominal and ordinal classification, with the model. We analyze generative properties of the approach, and the classification effectiveness under nominal and ordinal classification, using two benchmark datasets. Our results show that our model can achieve comparable results with relevant baselines in both of the classification tasks.
A CHAID Based Performance Prediction Model in Educational Data Mining

The performance in higher secondary school education in India is a turning point in the academic lives of all students. As this academic performance is influenced by many factors, it is essential to develop predictive data mining model for students’ performance so as to identify the slow learners and study the influence of the dominant factors on their academic performance. In the present investigation, a survey cum experimental methodology was adopted to generate a database and it was constructed from a primary and a secondary source. While the primary data was collected from the regular students, the secondary data was gathered from the school and office of the Chief Educational Officer (CEO). A total of 1000 datasets of the year 2006 from five different schools in three different districts of Tamilnadu were collected. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 772 student records, which were used for CHAID prediction model construction. A set of prediction rules were extracted from CHIAD prediction model and the efficiency of the generated CHIAD prediction model was found. The accuracy of the present model was compared with other model and it has been found to be satisfactory.
Quality and productivity outcomes relating to continuous integration in GitHub

Software processes comprise many steps; coding is followed by building, integration testing, system testing, deployment, operations, among others. Software process integration and automation have been areas of key concern in software engineering, ever since the pioneering work of Osterweil; market pressures for Agility, and open, decentralized, software development have provided additional pressures for progress in this area. But do these innovations actually help projects? Given the numerous confounding factors that can influence project performance, it can be a challenge to discern the effects of process integration and automation. Software project ecosystems such as GitHub provide a new opportunity in this regard: one can readily find large numbers of projects in various stages of process integration and automation, and gather data on various influencing factors as well as productivity and quality outcomes. In this paper we use large, historical data on process metrics and outcomes in GitHub projects to discern the effects of one specific innovation in process automation: continuous integration. Our main finding is that continuous integration improves the productivity of project teams, who can integrate more outside contributions, without an observable diminishment in code quality.
Neural Classification of Malicious Scripts: A study with JavaScript and VBScript

Malicious scripts are an important computer infection threat vector. Our analysis reveals that the two most prevalent types of malicious scripts include JavaScript and VBScript. The percentage of detected JavaScript attacks are on the rise. To address these threats, we investigate two deep recurrent models, LaMP (LSTM and Max Pooling) and CPoLS (Convoluted Partitioning of Long Sequences), which process JavaScript and VBScript as byte sequences. Lower layers capture the sequential nature of these byte sequences while higher layers classify the resulting embedding as malicious or benign. Unlike previously proposed solutions, our models are trained in an end-to-end fashion allowing discriminative training even for the sequential processing layers. Evaluating these models on a large corpus of 296,274 JavaScript files indicates that the best performing LaMP model has a 65.9% true positive rate (TPR) at a false positive rate (FPR) of 1.0%. Similarly, the best CPoLS model has a TPR of 45.3% at an FPR of 1.0%. LaMP and CPoLS yield a TPR of 69.3% and 67.9%, respectively, at an FPR of 1.0% on a collection of 240,504 VBScript files.
STOCK MARKET FORECASTING TECHNIQUES : LITERATURE SURVEY

The goal of this paper is to study different techniques to predict stock price movement using the sentiment analysis from social media, data mining. In this paper we will find efficient method which can predict stock movement more accurately. Social media offers a powerful outlet for people’s thoughts and feelings it is an enormous ever-growing source of texts ranging from everyday observations to involved discussions. This paper contributes to the field of sentiment analysis, which aims to extract emotions and opinions from text. A basic goal is to classify text as expressing either positive or negative emotion. Sentiment classifiers have been built for social media text such as product reviews, blog posts, and even twitter messages. With increasing complexity of text sources and topics, it is time to re-examine the standard sentiment extraction approaches, and possibly to redefine and enrich the definition of sentiment. Next, unlike sentiment analysis research to date, we examine sentiment expression and polarity classification within and across various social media streams by building topical datasets within each stream. Different data mining methods are used to predict market more efficiently along with various hybrid approaches. We conclude that stock prediction is very complex task and various factors should be considered for forecasting the market more accurately and efficiently.
A New Framework for Machine Intelligence: Concepts and Prototype

Machine learning (ML) and artificial intelligence (AI) have become hot topics in many information processing areas, from chatbots to scientific data analysis. At the same time, there is uncertainty about the possibility of extending predominant ML technologies to become general solutions with continuous learning capabilities. Here, a simple, yet comprehensive, theoretical framework for intelligent systems is presented. A combination of Mirror Compositional Representations (MCR) and a Solution-Critic Loop (SCL) is proposed as a generic approach for different types of problems. A prototype implementation is presented for document comparison using English Wikipedia corpus.
Exploring GPU Acceleration of Apache Spark

Apache Spark is one of the most active open-source cluster computing frameworks. However, there has been very limited research on improving Spark performance using GPUs. This paper explores the feasibility and benefits of off loading Spark core operations to GPUs. Experimental results from popular Spark applications show a performance improvement of up to 17X for a K-Means Clustering application. The results are used to suggest the type of applications that will benefit the most from off loading Spark workloads to GPUs.
i, Poet: Automatic Chinese Poetry Composition through a Generative Summarization Framework under Constrained Optimization

Part of the long lasting cultural heritage of China is the classical ancient Chinese poems which follow strict formats and complicated linguistic rules. Automatic Chinese poetry composition by programs is considered as a challenging problem in computational linguistics and requires high Artificial Intelligence assistance, and has not been well addressed. In this paper, we formulate the poetry composition task as an optimization problem based on a generative summarization framework under several constraints. Given the user specified writing intents, the system retrieves candidate terms out of a large poem corpus, and then orders these terms to fit into poetry formats, satisfying tonal and rhythm requirements. The optimization process under constraints is conducted via iterative term substitutions till convergence, and outputs the subset with the highest utility as the generated poem. For experiments, we perform generation on large datasets of 61,960 classic poems from Tang and Song Dynasty of China. A comprehensive evaluation, using both human judgments and ROUGE scores, has demonstrated the effectiveness of our proposed approach.
REM: Resource-Efficient Mining for Blockchains

Blockchains show promise as potential infrastructure for financial transaction systems. The security of blockchains today, however, relies critically on Proof-ofWork (PoW), which forces participants to waste computational resources. We present REM (Resource-Efficient Mining), a new blockchain mining framework that uses trusted hardware (Intel SGX). REM achieves security guarantees similar to PoW, but leverages the partially decentralized trust model inherent in SGX to achieve a fraction of the waste of PoW. Its key idea, Proof-of-Useful-Work (PoUW), involves miners providing trustworthy reporting on CPU cycles they devote to inherently useful workloads. REM flexibly allows any entity to create a useful workload. REM ensures the trustworthiness of these workloads by means of a novel scheme of hierarchical attestations that may be of independent interest. To address the risk of compromised SGX CPUs, we develop a statistics-based formal security framework, also relevant to other trusted-hardware-based approaches such as Intel’s Proof of Elapsed Time (PoET). We show through economic analysis that REM achieves less waste than PoET and variant schemes. We implement REM and, as an example application, swap it into the consensus layer of Bitcoin core. The result is the first full implementation of an SGX-based blockchain. We experiment with four example applications as useful workloads for our implementation of REM, and report a computational overhead of 5−15%.
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and prison sentencing. This paper introduces a new open source Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360), released under an Apache v2.0 license (https://github.com/ibm/aif360). The main objectives of this toolkit are to help facilitate the transition of fairness research algorithms to use in an industrial setting and to provide a common framework for fairness researchers to share and evaluate algorithms. The package includes a comprehensive set of fairness metrics for datasets and models, explanations for these metrics, and algorithms to mitigate bias in datasets and models. It also includes an interactive Web experience (https://aif360.mybluemix.net) that provides a gentle introduction to the concepts and capabilities for line-of-business users, as well as extensive documentation, usage guidance, and industry-specific tutorials to enable data scientists and practitioners to incorporate the most appropriate tool for their problem into their work products. The architecture of the package has been engineered to conform to a standard paradigm used in data science, thereby further improving usability for practitioners. Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking. A built-in testing infrastructure maintains code quality.
An empirical study of semantic similarity in WordNet and Word2Vec

This thesis performs an empirical analysis of Word2Vec by comparing its output to WordNet, a well-known, human-curated lexical database. It finds that Word2Vec tends to uncover more of certain types of semantic relations than others – with Word2Vec returning more hypernyms, synonomyns and hyponyms than hyponyms or holonyms. It also shows the probability that neighbors separated by a given cosine distance in Word2Vec are semantically related in WordNet. This result both adds to our understanding of the stillunknown Word2Vec and helps to benchmark new semantic tools built from word vectors. Word2Vec, Natural Language Processing, WordNet,
Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images

Segmentation of pulmonary X-ray computed tomography (CT) images is a precursor to most pulmonary image analysis applications. This paper presents a fully automatic method for identifying the lungs in three-dimensional (3-D) pulmonary X-ray CT images. The method has three main steps. First, the lung region is extracted from the CT images by gray-level thresholding. Then, the left and right lungs are separated by identifying the anterior and posterior junctions by dynamic programming. Finally, a sequence of morphological operations is used to smooth the irregular boundary along the mediastinum in order to obtain results consistent with these obtained by manual analysis, in which only the most central pulmonary arteries are excluded from the lung region. The method has been tested by processing 3-D CT data sets from eight normal subjects, each imaged three times at biweekly intervals with lungs at 90% vital capacity. The authors present results by comparing their automatic method to manually traced borders from two image analysts. Averaged over all volumes, the root mean square difference between the computer and human analysis is 0.8 pixels (0.54 mm). The mean intrasubject change in tissue content over the three scans was 2.75%/spl plusmn/2.29% (mean/spl plusmn/standard deviation).
Facial Action Unit Recognition Augmented by Their Dependencies

Due to the underlying anatomic mechanism that govern facial muscular interactions, there exist inherent dependencies between facial action units (AU). Such dependencies carry crucial information for AU recognition, yet have not been thoroughly exploited. Therefore, in this paper, we propose a novel AU recognition method with a three-layer hybrid Bayesian network, whose top two layers consist of a latent regression Bayesian network (LRBN), and the bottom two layers are Bayesian networks. The LRBN is a directed graphical model consisting of one latent layer and one visible layer. Specifically, the visible nodes of LRBN represent the ground-truth AU labels. Due to the "explaining away" effect in Bayesian networks, LRBN is able to capture both the dependencies among the latent variables given the observation and the dependencies among visible variables. Such dependencies successfully and faithfully represent relations among multiple AUs. The bottom two layers are two node Bayesian networks, connecting the ground truth AU labels and their measurements. Efficient learning and inference algorithms are also proposed. Furthermore, we extend the proposed hybrid Bayesian network model for facial expression-assisted AU recognition, since AU relations are influenced by expressions. By introducing facial expression nodes in the middle visible layer, facial expressions, which are only required during training, facilitate the estimation of label dependencies among AUs. Experimental results on three benchmark databases, i.e. the CK+ database, the SEMAINE database, and the BP4D database, demonstrate that the proposed approaches can successfully capture complex AU relationships, and the expression labels available only during training are benefit for AU recognition during testing.
Series Solution of Weakly-Singular Kernel Volterra Integro-Differential Equations by the Combined Laplace-Adomian Method

To solve the weakly-singular Volterra integro-differential equations, the combined method of the Laplace Transform Method and the Adomian Decomposition Method is used. As a result, series solutions of the equations are constructed. In order to explore the rapid decay of the equations, the pade approximation is used. The results present validity and great potential of the method as a powerful algorithm in order to present series solutions for singular kind of differential equations.
Formalizing a Systematic Review Updating Process

The objective of a systematic review is to obtain empirical evidence about the topic under review and to allow moving forward the body of knowledge of a discipline. Therefore, systematic reviewing is a tool we can apply in Software Engineering to develop well founded guidelines with the final goal of improving the quality of the software systems. However, we still do not have as much experience in performing systematic reviews as in other disciplines like medicine, and therefore we need detailed guidance. This paper presents a proposal of a improved process to perform systematic reviews in software engineering. This process is the result of the tasks carried out in a first review and a subsequent update concerning the effectiveness of elicitation techniques.