nils-herrmann/clean_scirepeval huggingface dataset:
State-ofthe-Art in Stock Prediction Techniques

Stock markets are affected by many uncertainties and interrelated economic and political factors at both local and global levels. The key to successful stock market forecasting is achieving best results with minimum required input data. To determine the set of relevant factors for making accurate predictions is a complicated task and so regular stock market analysis is very essential. More specifically, the stock market’s movements are analyzed and predicted in order to retrieve knowledge that could guide investors on when to buy and sell. It will also help the investor to make money through his investment in the stock market. This paper surveys large number of resources from research papers, web-sources, company reports and other available sources.
Virtual machine migration planning in software-defined networks

Live migration is a key technique for virtual machine (VM) management in data center networks, which enables flexibility in resource optimization, fault tolerance, and load balancing. Despite its usefulness, the live migration still introduces performance degradations during the migration process. Thus, there has been continuous efforts in reducing the migration time in order to minimize the impact. From the network's perspective, the migration time is determined by the amount of data to be migrated and the available bandwidth used for such transfer. In this paper, we examine the problem of how to schedule the migrations and how to allocate network resources for migration when multiple VMs need to be migrated at the same time. We consider the problem in the Software-defined Network (SDN) context since it provides flexible control on routing. More specifically, we propose a method that computes the optimal migration sequence and network bandwidth used for each migration. We formulate this problem as a mixed integer programming, which is NP-hard. To make it computationally feasible for large scale data centers, we propose an approximation scheme via linear approximation plus fully polynomial time approximation, and obtain its theoretical performance bound. Through extensive simulations, we demonstrate that our fully polynomial time approximation (FPTA) algorithm has a good performance compared with the optimal solution and two state-of-the-art algorithms. That is, our proposed FPTA algorithm approaches to the optimal solution with less than 10% variation and much less computation time. Meanwhile, it reduces the total migration time and the service downtime by up to 40% and 20% compared with the state-of-the-art algorithms, respectively.
Anti-Caching: A New Approach to Database Management System Architecture

The traditional wisdom for building disk-based relational database management systems (DBMS) is to organize data in heavily-encoded blocks stored on disk, with a main memory block cache. In order to improve performance given high disk latency, these systems use a multi-threaded architecture with dynamic record-level locking that allows multiple transactions to access the database at the same time. Previous research has shown that this results in substantial overhead for on-line transaction processing (OLTP) applications . The next generation DBMSs seek to overcome these limitations with architecture based on main memory resident data. To overcome the restriction that all data fit in main memory, we propose a new technique, called anti-caching, where cold data is moved to disk in a transactionally-safe manner as the database grows in size. Because data initially resides in memory, an anti-caching architecture reverses the traditional storage hierarchy of disk-based systems. Main memory is now the primary storage device. We implemented a prototype of our anti-caching proposal in a high-performance, main memory OLTP DBMS and performed a series of experiments across a range of database sizes, workload skews, and read/write mixes. We compared its performance with an open-source, disk-based DBMS optionally fronted by a distributed main memory cache. Our results show that for higher skewed workloads the anti-caching architecture has a performance advantage over either of the other architectures tested of up to 9⇥ for a data size 8⇥ larger than memory.
The Impact of Fake Reviews on Online Visibility: A Vulnerability Assessment of the Hotel Industry

Extant research has focused on the detection of fake reviews on online review platforms, motivated by the well-documented impact of customer reviews on the users’ purchase decisions. The problem is typically approached from the perspective of protecting the credibility of review platforms, as well as the reputation and revenue of the reviewed firms. However, there is little examination of the vulnerability of individual businesses to fake review attacks. This study focuses on formalizing the visibility of a business to the customer base and on evaluating its vulnerability to fake review attacks. We operationalize visibility as a function of the features that a business can cover and its position in the platform’s review-based ranking. Using data from over 2.3 million reviews of 4,709 hotels from 17 cities, we study how visibility can be impacted by different attack strategies. We find that even limited injections of fake reviews can have a significant effect and explore the factors that contribute to this vulnerable state. Specifically, we find that, in certain markets, 50 fake reviews are sufficient for an attacker to surpass any of its competitors in terms of visibility. We also compare the strategy of self-injecting positive reviews with that of injecting competitors with negative reviews and find that each approach can be as much as 40% more effective than the other across different settings. We empirically explore response strategies for an attacked hotel, ranging from the enhancement of its own features to detecting and disputing fake reviews. In general, our measure of visibility and our modeling approach regarding attack and response strategies shed light on how businesses that are targeted by fake reviews can detect and tackle such attacks.
Learning Analytics Dashboards to Support Adviser-Student Dialogue

This paper presents LISSA (“Learning dashboard for Insights and Support during Study Advice”), a learning analytics dashboard designed, developed, and evaluated in collaboration with study advisers. The overall objective is to facilitate communication between study advisers and students by visualizing grade data that is commonly available in any institution. More specifically, the dashboard attempts to support the dialogue between adviser and student through an overview of study progress, peer comparison, and by triggering insights based on facts as a starting point for discussion and argumentation. We report on the iterative design process and evaluation results of a deployment in 97 advising sessions. We have found that the dashboard supports the current adviser-student dialogue, helps them motivate students, triggers conversation, and provides tools to add personalization, depth, and nuance to the advising session. It provides insights at a factual, interpretative, and reflective level and allows both adviser and student to take an active role during the session.
Online Collaborative Learning for Open-Vocabulary Visual Classifiers

We focus on learning open-vocabulary visual classifiers, which scale up to a large portion of natural language vocabulary (e.g., over tens of thousands of classes). In particular, the training data are large-scale weakly labeled Web images since it is difficult to acquire sufficient well-labeled data at this category scale. In this paper, we propose a novel online learning paradigm towards this challenging task. Different from traditional N-way independent classifiers that generally fail to handle the extremely sparse and inter-related labels, our classifiers learn from continuous label embeddings discovered by collaboratively decomposing the sparse image-label matrix. Leveraging on the structure of the proposed collaborative learning formulation, we develop an efficient online algorithm that can jointly learn the label embeddings and visual classifiers. The algorithm can learn over 30,000 classes of 1,000 training images within 1 second on a standard GPU. Extensively experimental results on four benchmarks demonstrate the effectiveness of our method.
CATS: Characterizing automation of Twitter spammers

Twitter, with its rising popularity as a micro-blogging website, has inevitably attracted the attention of spammers. Spammers use myriad of techniques to evade security mechanisms and post spam messages, which are either unwelcome advertisements for the victim or lure victims in to clicking malicious URLs embedded in spam tweets. In this paper, we propose several novel features capable of distinguishing spam accounts from legitimate accounts. The features analyze the behavioral and content entropy, bait-techniques, and profile vectors characterizing spammers, which are then fed into supervised learning algorithms to generate models for our tool, CATS. Using our system on two real-world Twitter data sets, we observe a 96% detection rate with about 0.8% false positive rate beating state of the art detection approach. Our analysis reveals detection of more than 90% of spammers with less than five tweets and about half of the spammers detected with only a single tweet. Our feature computation has low latency and resource requirement making fast detection feasible. Additionally, we cluster the unknown spammers to identify and understand the prevalent spam campaigns on Twitter.
Robust Decision Trees: Removing Outliers from Databases

Finding and removing outliers is an important problem in data mining. Errors in large databases can be extremely common, so an important property of a data mining algorithm is robustness with respect to errors in the database. Most sophisticated methods in machine learning address this problem to some extent, but not fully, and can be improved by addressing the problem more directly. In this paper we examine C4.5, a decision tree algorithm that is already quite robust few algorithms have been shown to consistently achieve higher accuracy. C4.5 incorporates a pruning scheme that partially addresses the outfier removal problem. In our ROBUST-C4.5 algorithm we extend the pruning method to fully remove the effect of outliers, and this results in improvement on many databases.
Annotating Argument Components and Relations in Persuasive Essays

In this paper, we present a novel approach to model arguments, their components and relations in persuasive essays in English. We propose an annotation scheme that includes the annotation of claims and premises as well as support and attack relations for capturing the structure of argumentative discourse. We further conduct a manual annotation study with three annotators on 90 persuasive essays. The obtained inter-rater agreement of αU = 0.72 for argument components and α = 0.81 for argumentative relations indicates that the proposed annotation scheme successfully guides annotators to substantial agreement. The final corpus and the annotation guidelines are freely available to encourage future research in argument recognition.
New Dynamical Models of Lead – Acid Batteries

This paper documents the main results of studies that have been carried out, during a period of more than a decade, at University of Pisa in co-operation with other technical Italian institutions, about models of electrochemical batteries suitable for the use of the electrical engineer, in particular for the analysis of electrical systems with batteries. The problem of simulating electrochemical batteries by means of equivalent electric circuits is defined in a general way; then particular attention is then devoted to the problem of modeling of Lead–Acid batteries. For this kind of batteries general model structure is defined from which specific models can be inferred, having different degrees of complexity and simulation quality. In particular, the implementation of the third-order model, that shows a good compromise between complexity and precision, is developed in detail. The behavior of the proposed models is compared with results obtained with extensive lab tests on different types of lead–acid batteries.
Divide-and-Conquer Reinforcement Learning

Standard model-free deep reinforcement learning (RL) algorithms sample a new initial state for each trial, allowing them to optimize policies that can perform well even in highly stochastic environments. However, problems that exhibit considerable initial state variation typically produce high-variance gradient estimates for model-free RL, making direct policy or value function optimization challenging. In this paper, we develop a novel algorithm that instead partitions the initial state space into “slices”, and optimizes an ensemble of policies, each on a different slice. The ensemble is gradually unified into a single policy that can succeed on the whole state space. This approach, which we term divide-and-conquer RL, is able to solve complex tasks where conventional deep RL methods are ineffective. Our results show that divide-and-conquer RL greatly outperforms conventional policy gradient methods on challenging grasping, manipulation, and locomotion tasks, and exceeds the performance of a variety of prior methods. Videos of policies learned by our algorithm can be viewed at https://sites.google.com/view/dnc-rl/.
Improving Gender Classification of Blog Authors

The problem of automatically classifying the gender of a blog author has important applications in many commercial domains. Existing systems mainly use features such as words, word classes, and POS (part-ofspeech) n-grams, for classification learning. In this paper, we propose two new techniques to improve the current result. The first technique introduces a new class of features which are variable length POS sequence patterns mined from the training data using a sequence pattern mining algorithm. The second technique is a new feature selection method which is based on an ensemble of several feature selection criteria and approaches. Empirical evaluation using a real-life blog data set shows that these two techniques improve the classification accuracy of the current state-ofthe-art methods significantly.
SOFT-IoT: Self-Organizing FOG of Things

The Internet of Things (IoT) has advanced in different directions, including the development of new architectures, platforms and applications. This multidirectional progress has resulted in the creation of several parallel IoT ecosystems called verticals. Therefore, several approaches have proposed solutions for interoperability of these ecosystems through the delivery of services over virtual infrastructure (Cloud Computing). We introduce the paradigm of Fog of Things (FoT) and propose the design and development of a self-organizing platform called SOFT-IoT: Self-Organizing Fog of Things. SOFT-IoT implements the "Fog Computing" concept introduced by CISCO for the Internet of Things, where part of data processing capacity and service delivery operations are processed locally in "small servers", i.e., close to where data is collected. SOFT-IoT deals with protocols to facilitate the local computing processing and rely in more complex operations running in virtual entities avoiding the traditional approach of centralized cloud computing solutions. In this way, SOFT-IoT enables interoperability of local ecosystems in the fog and also at the cloud level, where other data is stored, processed and complex operations are resolved. In other words, in SOFT-IoT the data processing and delivery of services occurs locally in order to overcome with current infrastructure limitations and data processing alleviating those demands for heavy computational resources that can occur in remote servers, i.e., geographically away from where data is generated.
Different Texts, Same Metaphors: Unigrams and Beyond

Current approaches to supervised learning of metaphor tend to use sophisticated features and restrict their attention to constructions and contexts where these features apply. In this paper, we describe the development of a supervised learning system to classify all content words in a running text as either being used metaphorically or not. We start by examining the performance of a simple unigram baseline that achieves surprisingly good results for some of the datasets. We then show how the recall of the system can be improved over this strong baseline.
Embedded Multi-die Interconnect Bridge (EMIB) -- A High Density, High Bandwidth Packaging Interconnect

The EMIB dense MCP technology is a new packaging paradigm that provides localized high density interconnects between two or more die on an organic package substrate, opening up new opportunities for heterogeneous on-package integration. This paper provides an overview of EMIB architecture and package capabilities. First, EMIB is compared with other approaches for high density interconnects. Some of the inherent advantages of the technology, such as the ability to cost effectively implement high density interconnects without requiring TSVs, and the ability to support the integration of many large die in an area much greater than the typical reticle size limit are highlighted. Next, the overall EMIB architecture envelope is discussed along with its constituent building blocks, the package construction with the embedded bridge, die to package interconnect features. Next, the EMIB assembly process is described at a high level. Finally, high bandwidth signaling between the die is discussed and the link bandwidth envelope is quantified.
Convex Principal Feature Selection

A popular approach for dimensionality reduction and data analysis is principal component analysis (PCA). A limiting factor with PCA is that it does not inform us on which of the original features are important. There is a recent interest in sparse PCA (SPCA). By applying an L1 regularizer to PCA, a sparse transformation is achieved. However, true feature selection may not be achieved as non-sparse coefficients may be distributed over several features. Feature selection is an NP-hard combinatorial optimization problem. This paper relaxes and re-formulates the feature selection problem as a convex continuous optimization problem that minimizes a mean-squared-reconstruction error (a criterion optimized by PCA) and considers feature redundancy into account (an important property in PCA and feature selection). We call this new method Convex Principal Feature Selection (CPFS). Experiments show that CPFS performed better than SPCA in selecting features that maximize variance or minimize the mean-squaredreconstruction error.
MIMO State Feedback Controller for a Flexible Joint Robot with Strong Joint Coupling

The paper describes the modeling and control of a robot with flexible joints (the DLR medical robot), which has strong mechanical couplings between pairs of joints realized with a differential gear-box. Because of this coupling, controllers developed before for the DLR light-weight robots cannot be directly applied. The previous control approach is extended in order to allow a multi-input-multi-output (MIMO) design for the strongly coupled joints. Asymptotic stability is shown for the MIMO controller. Finally, experimental results with the DLR medical robot are presented.
ARTFul: adaptive review technology for flipped learning

Internet technology is revolutionizing education. Teachers are developing massive open online courses (MOOCs) and using innovative practices such as flipped learning in which students watch lectures at home and engage in hands-on, problem solving activities in class. This work seeks to explore the design space afforded by these novel educational paradigms and to develop technology for improving student learning. Our design, based on the technique of adaptive content review, monitors student attention during educational presentations and determines which lecture topic students might benefit the most from reviewing. An evaluation of our technology within the context of an online art history lesson demonstrated that adaptively reviewing lesson content improved student recall abilities 29% over a baseline system and was able to match recall gains achieved by a full lesson review in less time. Our findings offer guidelines for a novel design space in dynamic educational technology that might support both teachers and online tutoring systems.
A framework for HDR stereo matching using multi-exposed images

Real world scenes that contain high dynamic range illumination present a special challenge for stereo matching algorithms due to a lack of texture in overor under-exposed image regions. In this paper, we discuss possibilities for combining state-of-the-art stereo matching algorithms with High Dynamic Range (HDR) imaging techniques, in order to exploit a set of multi-exposed input images of both the left and right stereo view for high-quality stereo reconstruction. We sketch the overall concept of our HDR stereo matching framework and demonstrate some first steps of its implementation, including the acquisition of HDR stereo test data, stereo matching experiments on tone-mapped images, and ideas for combining disparity maps derived from different exposures.
Adaptive game level creation through rank-based interactive evolution

This paper introduces Rank-based Interactive Evolution (RIE) which is an alternative to interactive evolution driven by computational models of user preferences to generate personalized content. In RIE, the computational models are adapted to the preferences of users which, in turn, are used as fitness functions for the optimization of the generated content. The preference models are built via ranking-based preference learning, while the content is generated via evolutionary search. The proposed method is evaluated on the creation of strategy game maps, and its performance is tested using artificial agents. Results suggest that RIE is both faster and more robust than standard interactive evolution and outperforms other state-of-the-art interactive evolution approaches.
Factors Affecting Jordanian Consumers ’ Adoption of Mobile Banking Services

Banking in several developing countries has transcended from a traditional brick-and mortar model of customers queuing for services in the banks to modern day banking where banks can be reached at any point for their services. This can be attributed to the tremendous growth in mobile penetration in many countries across the globe including Jordan. The current exploratory study is an attempt to identify the underlying factors that affects mobile banking adoption in Jordan. Data for this study have been collected using a questionnaire containing 22 questions. Out of 450 questionnaires that have been distributed, 301 are returned (66.0%). In the survey, factors that may affect Jordanian mobile phone users' to adopt mobile banking services were examined. The research findings suggested that all the six factors; self efficacy, trailability, compatibility, complexity, risk and relative advantage were statistically significant in influencing mobile banking adoption.