nils-herrmann/clean_scirepeval huggingface dataset:
Una aproximación al uso de word embeddings en una tarea de similitud de textos en español

In this paper we show how a vector representation of words based on word embeddings can help to improve the results in tasks focused on the semantic similarity of texts. Thus we have experimented with two methods that rely on the vector representation of words to calculate the degree of similarity of two texts, one based on the aggregation of vectors and the other one based on the calculation of alignments. The alignment method relies on the similarity of word vectors to determine the semantic link between them. The aggregation method allows us to construct vector representations of the texts from the individual vectors of each word. These representations are compared by means of two classic distance measures: Euclidean distance and cosine similarity. We have evaluated our systems with the corpus based on Wikipedia distributed in the competition of similarity of texts in Spanish of SemEval-2015. Our experiments show that the method based on the alignment of words performs much better, obtaining results that are very close to the best system at SemEval. The method based on vector representations of texts behaves substantially worse. However, this second approach seems to capture aspects of similarity not detected by the first one, as when the outputs of both systems are combined the results of the alignment method are surpassed, even exceeding the results of the best system at SemEval.
Ontologies Improve Text Document Clustering

Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large sets of documents into a small number of meaningful clusters. The bag of words representation used for these clustering methods is often unsatisfactory as it ignores relationships between important terms that do not cooccur literally. In order to deal with the problem, we integrate core ontologies as background knowledge into the process of clustering text documents. Our experimental evaluations compare clustering techniques based on precategorizations of texts from Reuters newsfeeds and on a smaller domain of an eLearning course about Java. In the experiments, improvements of results by background knowledge compared to a baseline without background knowledge can be shown in many interesting combinations.
Real-time Business Process Intelligence. Comparison of different architectural approaches using the example of the order-to-cash process

Business operations are becoming more and more integrated with the realtime intelligence. Core business activities are being carried out through OLTP systems that provide limited monitoring capabilities of the running process instances. The article shows how to turn the gap between the classic transactional system and the processcentric approach into an organization that provides more accurate and faster decisions on the strategic and operational management levels. This study aims at determination of what kind of information can be retrieved during the process execution and it tries to identify the need for the real-time process intelligence on the example of the order-tocash process. Furthermore, we compare two architectural approaches of the real-time process intelligence monitoring system. The proposed frameworks retrieve process data from the ERP system in order to record crucial performance indicators on a real-time basis with the use of the in-memory technology.
CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases

How can we enable computers to automatically answer questions like “Who created the character Harry Potter”? Carefully built knowledge bases provide rich sources of facts. However, it remains a challenge to answer factoid questions raised in natural language due to numerous expressions of one question. In particular, we focus on the most common questions — ones that can be answered with a single fact in the knowledge base. We propose CFO, a Conditional Focused neuralnetwork-based approach to answering factoid questions with knowledge bases. Our approach first zooms in a question to find more probable candidate subject mentions, and infers the final answers with a unified conditional probabilistic framework. Powered by deep recurrent neural networks and neural embeddings, our proposed CFO achieves an accuracy of 75.7% on a dataset of 108k questions – the largest public one to date. It outperforms the current state of the art by an absolute margin of 11.8%.
Behavioral malware detection approaches for Android

Android, the fastest growing mobile operating system released in November 2007, boasts of a staggering 1.4 billion active users. Android users are susceptible to malicious applications that can hack into their personal data due to the lack of careful monitoring of their in-device security. There have been numerous works on devising malware detection methods. However, none of earlier works are conclusive enough for direct application and lack experimental validation. In this paper, we have investigated the natures and identities of malicious applications and devised two novel detection approaches for detection: network-based detection and system call based detection approaches. To evaluate our proposed approaches, we performed experiments on a subset of 1260 malwares, acquired from Android Malware Genome Project, a malware database created by Y. Zhou et al.  and 227 non-malware (benign) applications. Results show that our system call based approach is able to detect malwares with an accuracy of 87% which is quite significant in general malware detection context. Our proposed detection approaches along with the experimental results will provide security professionals with more precise and quantitative approaches in their investigations of mobile malwares on Android systems.
Personalized QoS-Aware Web Service Recommendation and Visualization

With the proliferation of web services, effective QoS-based approach to service recommendation is becoming more and more important. Although service recommendation has been studied in the recent literature, the performance of existing ones is not satisfactory, since (1) previous approaches fail to consider the QoS variance according to users' locations; and (2) previous recommender systems are all black boxes providing limited information on the performance of the service candidates. In this paper, we propose a novel collaborative filtering algorithm designed for large-scale web service recommendation. Different from previous work, our approach employs the characteristic of QoS and achieves considerable improvement on the recommendation accuracy. To help service users better understand the rationale of the recommendation and remove some of the mystery, we use a recommendation visualization technique to show how a recommendation is grouped with other choices. Comprehensive experiments are conducted using more than 1.5 million QoS records of real-world web service invocations. The experimental results show the efficiency and effectiveness of our approach.
Together we stand: Siamese Networks for Similar Question Retrieval

Community Question Answering (cQA) services like Yahoo! Answers1, Baidu Zhidao2, Quora3, StackOverflow4 etc. provide a platform for interaction with experts and help users to obtain precise and accurate answers to their questions. The time lag between the user posting a question and receiving its answer could be reduced by retrieving similar historic questions from the cQA archives. The main challenge in this task is the “lexicosyntactic” gap between the current and the previous questions. In this paper, we propose a novel approach called “Siamese Convolutional Neural Network for cQA (SCQA)” to find the semantic similarity between the current and the archived questions. SCQA consist of twin convolutional neural networks with shared parameters and a contrastive loss function joining them. SCQA learns the similarity metric for question-question pairs by leveraging the question-answer pairs available in cQA forum archives. The model projects semantically similar question pairs nearer to each other and dissimilar question pairs farther away from each other in the semantic space. Experiments on large scale reallife “Yahoo! Answers” dataset reveals that SCQA outperforms current state-of-theart approaches based on translation models, topic models and deep neural network https://answers.yahoo.com/ http://zhidao.baidu.com/  http://stackoverflow.com/ based models which use non-shared parameters.
How to evaluate technologies for health behavior change in HCI research

New technologies for encouraging physical activity, healthy diet, and other types of health behavior change now frequently appear in the HCI literature. Yet, how such technologies should be evaluated within the context of HCI research remains unclear. In this paper, we argue that the obvious answer to this question - that evaluations should assess whether a technology brought about the intended change in behavior - is too limited. We propose that demonstrating behavior change is often infeasible as well as unnecessary for a meaningful contribution to HCI research, especially when in the early stages of design or when evaluating novel technologies. As an alternative, we suggest that HCI contributions should focus on efficacy evaluations that are tailored to the specific behavior-change intervention strategies (e.g., self-monitoring, conditioning) embodied in the system and studies that help gain a deep understanding of people's experiences with the technology.
Reactive power capability of the wind turbine with Doubly Fed Induction Generator

With the increasing integration into power grids, wind power plants play an important role in the power system. Many requirements for the wind power plants have been proposed in the grid codes. According to these grid codes, wind power plants should have the ability to perform voltage control and reactive power compensation at the point of common coupling (PCC). Besides the shunt flexible alternating current transmission system (FACTS) devices such as the static var compensator (SVC) and the static synchronous compensator (STATCOM), the wind turbine itself can also provide a certain amount of reactive power compensation, depending on the wind speed and the active power control strategy. This paper analyzes the reactive power capability of Doubly Fed Induction Generator (DFIG) based wind turbine, considering the rated stator current limit, the rated rotor current limit, the rated rotor voltage limit, and the reactive power capability of the grid side convertor (GSC). The boundaries of reactive power capability of DFIG based wind turbine are derived. The result was obtained using the software MATLAB.
Course scheduler and recommendation system for students

Every semester, university students need to decide their course programs, and as the number of course offerings increase, it becomes challenge to form a schedule for the semester courses to be taken. In addition, students might have difficulty in finding courses that can match their schedules and course preferences. In order to address these problems, we build Mecanin, an online platform for Boğaziçi University students that provides an optimum schedule and course recommendation tools. In this paper, we propose an integer programming model to optimize course programs and also a recommendation algorithm to suggest courses to students. Both algorithms are tested and compared, and the results are presented.
Distributed Detection of Sensor Worms Using Sequential Analysis and Remote Software Attestations

Recent work has demonstrated that self-propagating worms are a real threat to sensor networks. Since worms can enable an adversary to quickly compromise an entire sensor network, they must be detected and stopped as quickly as possible. To meet this need, we propose a worm propagation detection scheme for sensor networks. The proposed scheme applies a sequential analysis to detect worm propagation by leveraging the intuition that a worm’s communication pattern is different from benign traffic. In particular, a worm in a sensor network requires a long sequence of packets propagating hop-by-hop to each new infected node in turn. We thus have detectors that observe communication patterns in the network, a worm spreading hop-by-hop will quickly create chains of connections that would not be seen in normal traffic. Once detector nodes identify the worm propagation pattern, they initiate remote software attestations to detect infected nodes. Through analysis and simulation, we demonstrate that the proposed scheme effectively and efficiently detects worm propagation. In particular, it blocks worm propagation while restricting the fraction of infected nodes to at most 13.5% with an overhead of at most 0.63 remote attestations per node per time slot.
On the Complexity of Bandit and Derivative-Free Stochastic Convex Optimization

The problem of stochastic convex optimization with bandit feedback (in the learning community) or without knowledge of gradients (in the optimization community) has received much attention in recent years, in the form of algorithms and performance upper bounds. However, much less is known about the inherent complexity of these problems, and there are few lower bounds in the literature, especially for nonlinear functions. In this paper, we investigate the attainable error/regret in the bandit and derivative-free settings, as a function of the dimension d and the available number of queries T . We provide a precise characterization of the attainable performance for strongly-convex and smooth functions, which also imply a non-trivial lower bound for more general problems. Moreover, we prove that in both the bandit and derivative-free setting, the required number of queries must scale at least quadratically with the dimension. Finally, we show that on the natural class of quadratic functions, it is possible to obtain a “fast” O(1/T ) error rate in terms of T , under mild assumptions, even without having access to gradients. To the best of our knowledge, this is the first such rate in a derivative-free stochastic setting, and holds despite previous results which seem to imply the contrary.
ServerSwitch: A Programmable and High Performance Platform for Data Center Networks

As one of the fundamental infrastructures for cloud computing, data center networks (DCN) have recently been studied extensively. We currently use pure software-based systems, FPGA based platforms, e.g., NetFPGA, or OpenFlow switches, to implement and evaluate various DCN designs including topology design, control plane and routing, and congestion control. However, software-based approaches suffer from high CPU overhead and processing latency; FPGA based platforms are difficult to program and incur high cost; and OpenFlow focuses on control plane functions at present. In this paper, we design a ServerSwitch to address the above problems. ServerSwitch is motivated by the observation that commodity Ethernet switching chips are becoming programmable and that the PCI-E interface provides high throughput and low latency between the server CPU and I/O subsystem. ServerSwitch uses a commodity switching chip for various customized packet forwarding, and leverages the server CPU for control and data plane packet processing, due to the low latency and high throughput between the switching chip and server CPU. We have built our ServerSwitch at low cost. Our experiments demonstrate that ServerSwitch is fully programmable and achieves high performance. Specifically, we have implemented various forwarding schemes including source routing in hardware. Our in-network caching experiment showed high throughput and flexible data processing. Our QCN (Quantized Congestion Notification) implementation further demonstrated that ServerSwitch can react to network congestions in 23us. ∗Correspondent author. †This work was performed when Zhiqiang Zhou was a visiting student at Microsoft Research Asia.
Resource provisioning and scheduling in clouds: QoS perspective

Resource provisioning of appropriate resources to cloud workloads depends on the quality of service (QoS) requirements of cloud applications and is a challenging task. In cloud environment, heterogeneity, uncertainty and dispersion of resources encounter a problem of allocation of resources, which cannot be addressed with existing resource management frameworks. Resource scheduling, if done after efficient resource provisioning, will be more effective and the cloud resources would be scheduled as per the user requirements (QoS) on provisioned resources. Execution of cloud workloads should be as per QoS parameters to fully satisfy the cloud consumer. Therefore, based on QoS parameters, it is mandatory to predict and verify the resource provisioning before actual resource scheduling. In this paper, a resource provisioning and scheduling framework has been presented which caters to provisioned resource distribution and scheduling of resources. Cloud workloads have been re-clustered using k-means-based clustering algorithm after firstly clustering them through workload patterns to identify the QoS requirements of a workload, and then based on identified QoS requirements resources are provisioned before actual scheduling. Further, scheduling has been done based on different scheduling policies. Finally, the performance of the proposed framework has been evaluated in both real and simulated cloud environment and experimental results show that the framework provisions and schedules resource efficiently by considering energy consumption, execution cost and execution time as QoS parameters.
Smart cities based on web semantic technologies

A good communication and interaction between citizens and the administration is important and crucial, also can greatly help in improving the quality of urban life of citizen. In this paper, we propose a semantic data model for managing and resolving the problems that exist in cities such as water leak, street faults, broken street lights, and potholes. The main idea is to focus on the best practices of linked open data to describe all issues, and then integrate them in the dataset provided by DBpedia. Hence, our approach is based on the standards of the World Wide Web Consortium.
Comparing discrimination powers of text and citation-based context types

Different context types have different potentialities in representing subjects of documents. However, little is known on their probable differences regarding discriminating documents with different subjects. The present study aimed to compare the discrimination powers of five context types (i.e. title, textual, text-citation, reference, and reference-title contexts). Using a content analysis method, a test collection consisted of 3637 papers containing 15 English homographs in 54 subject clusters were analyzed by discriminant analysis to determine the discrimination among the clusters. The results confirmed that the homograph context types significantly discriminate between the clusters. The contexts can correctly cluster the documents from about 40 to 80%, with text and reference-title context types being stronger in differentiating the clusters. This is the first study to compare the powers of different textual contexts kinds and recommend to concentrate on context types to discriminate between documents with homographs to improve the effectiveness of their retrieval. It is suggested that a comparison of the context types be used as a tool in re-ranking or clustering of the retrieved results.
A Robust Single-Phase PLL System With Stable and Fast Tracking

Phase, frequency, and amplitude of single-phase voltages are the most important and basic information required for single-phase grid-connected applications. This paper proposes a method for instantly and robustly estimating the phase, frequency, and amplitude of frequency-varying single-phase signals for such applications, which is a phase-locked loop (PLL) method based on a structure. The proposed method has the following attractive features: 1) the estimation system results in a nonlinear system, but it can be stabilized; 2) all subsystems constructing the system can be easily designed; 3) "two-phase signal generator"; can autotune a single system parameter in response to varying frequency of the injected signal; 4) high-order "PLL-Controllers" allowing fast tracking can be stably used; and 5) even in hostile envelopments, favorable instant estimates can be obtained. This paper proposes the estimation method and verifies its usefulness by extensive numerical experiments.
A hybrid latent variable neural network model for item recommendation

Collaborative filtering is used to recommend items to a user without requiring a knowledge of the item itself and tends to outperform other techniques. However, pure collaborative filtering technique suffer from the cold-start problem, which occurs when an item has not yet been rated or a user has not rated any items. Incorporating additional information, such as item or user descriptions, into collaborative filtering can address the cold-start problem. In this paper, we present a neural network model with latent input variables (latent neural network or LNN) as a hybrid collaborative filtering technique that addresses the cold-start problem. LNN outperforms a broad selection of content-based filters (which make recommendations based on item descriptions) and other hybrid approaches while maintaining the accuracy of state-of-the-art collaborative filtering techniques.
Secure, Efficient and Fine-Grained Data Access Control Mechanism for P2P Storage Cloud

By combining cloud computing and Peer-to-Peer computing, a P2P storage cloud can be formed to offer highly available storage services, lowering the economic cost by exploiting the storage space of participating users. However, since cloud severs and users are usually outside the trusted domain of data owners, P2P storage cloud brings forth new challenges for data security and access control when data owners store sensitive data for sharing in the trusted domain. Moreover, there are no mechanisms for access control in P2P storage cloud. To address this issue, we design a ciphertext-policy attribute-based encryption (ABE) scheme and a proxy re-encryption scheme. Based on them, we further propose a secure, efficient and fine-grained data Access Control mechanism for P2P storage Cloud named ACPC. We enforce access policies based on user attributes, and integrate P2P reputation system in ACPC. ACPC enables data owners to delegate most of the laborious user revocation tasks to cloud servers and reputable system peers. Our security analysis demonstrates that ACPC is provably secure. The performance evaluation shows that ACPC is highly efficient under practical settings, and it significantly reduces the computation overheads brought to data owners and cloud servers during user revocation, compared with other state-of-the-art revocable ABE schemes.
Topic-link LDA: joint models of topic and author community

Given a large-scale linked document collection, such as a collection of blog posts or a research literature archive, there are two fundamental problems that have generated a lot of interest in the research community. One is to identify a set of high-level topics covered by the documents in the collection; the other is to uncover and analyze the social network of the authors of the documents. So far these problems have been viewed as separate problems and considered independently from each other. In this paper we argue that these two problems are in fact inter-dependent and should be addressed together. We develop a Bayesian hierarchical approach that performs topic modeling and author community discovery in one unified framework. The effectiveness of our model is demonstrated on two blog data sets in different domains and one research paper citation data from CiteSeer.