nils-herrmann/clean_scirepeval huggingface dataset:
Exploring Gameplay Experiences on the Oculus Rift

Compared to previous head-mounted displays, the compact and low-cost Oculus Rift has claimed to offer improved virtual reality experiences. However, how and what kinds of user experiences are encountered by people when using the Rift in actual gameplay has not been examined. We present an exploration of 10 participants' experiences of playing a first-person shooter game using the Rift. Despite cybersickness and a lack of control, participants experienced heightened experiences, a richer engagement with passive game elements, a higher degree of flow and a deeper immersion on the Rift than on a desktop setup. Overly demanding movements, such as the large range of head motion required to navigate the game environment were found to adversely affect gaming experiences. Based on these and other findings, we also present some insights for designing games for the Rift.
Comparison of JSON and XML Data Interchange Formats: A Case Study

This paper compares two data interchange format s currently used by industry applications; XML and JSON. The choice of an adequate data interchange format can have significant consequences on data transmission rates and performance. We describe th e language specifications and their respective settin g of use. A case study is then conducted to compare the resource utilization and the relative performance o f applications that use the interchange formats. We find that JSON is significantly faster than XML and we further record other resource-related metrics in ou r results.
An architecture for software defined wireless networking

Software defined networking, characterized by a clear separation of the control and data planes, is being adopted as a novel paradigm for wired networking. With SDN, network operators can run their infrastructure more efficiently, supporting faster deployment of new services while enabling key features such as virtualization. In this article, we adopt an SDN-like approach applied to wireless mobile networks that will not only benefit from the same features as in the wired case, but will also leverage on the distinct features of mobile deployments to push improvements even further. We illustrate with a number of representative use cases the benefits of the adoption of the proposed architecture, which is detailed in terms of modules, interfaces, and high-level signaling. We also review the ongoing standardization efforts, and discuss the potential advantages and weaknesses, and the need for a coordinated approach.
Detect overlapping and hierarchical community structure in networks

Clustering and community structure is crucial for many network systems and the related dynamic processes. It has been shown that communities are usually overlapping and hierarchical. However, previous methods investigate these two properties of community structure separately. This paper proposes an algorithm (EAGLE) to detect both the overlapping and hierarchical properties of complex community structure together. This algorithm deals with the set of maximal cliques and adopts an agglomerative framework. The quality function of modularity is extended to evaluate the goodness of a cover. The examples of application to real world networks give excellent results.
CSRR Loaded Microstrip Array Antenna With Low Sidelobe Level

A novel technique for obtaining low sidelobes pattern in a planar microstrip array antenna is proposed. This technique involves the addition of two external complementary split ring resonators (CSRRs) at both the ends of each row of the antenna in the ground plane. These two CSRRs together produce an interferometer pattern for the reduction of sidelobes. An 8×4 planar array at 9.9 GHz is designed and fabricated to demonstrate the concept and a sidelobe reduction of 4.3 dB achieved.
Transformer modeling approaches for Frequency Response Analysis

Transformer modeling is a task nearly as old as transformer manufacturing. The methodology and principles used to build the model depend on its application, that will require specific features regarding aspects like frequency range, degree of accuracy, type of the model parameters and so on. The diagnosis of the transformer based on the Frequency Response Analysis (FRA) technique is one of the latest applications where transformer modeling has demonstrated that could be a useful tool. However, it is a difficult task and extended research is needed. In this paper, the particular features of the FRA oriented models will be discussed and the different and heterogeneous solutions available in the literature will be sorted, classified and analyzed. Problems in FRA oriented transformer modeling are indicated and possible future trends are exposed.
Extracting sentiment as a function of discourse structure and topicality

We present an approach to extracting sentiment from texts that makes use of contextual information. Using two di¤erent approaches, we extract the most relevant sentences of a text, and calculate semantic orientation weighing those more heavily. The rst approach makes use of discourse structure via Rhetorical Structure Theory, and extracts nuclei as the relevant parts; the second approach uses a topic classier built using support vector machines, which extracts topic sentences from texts. The use of weights on relevant sentences shows an improvement over word-based methods that consider the entire text equally. In the paper, we also describe an enhancement of our previous word-based methods in the treatment of intensiers and negation, and the addition of other parts of speech beyond adjectives. This work was supported by the Natural Sciences and Engineering Research Council of Canada, under a Discovery Grant and a University Faculty Award to Maite Taboada. We also thank members of the Sentiment Research Group at SFU for their feedback, and in particular Vita Markman, Milan Toloski and Ping Yang for their help in ranking our dictionaries.
Breadcrumbs: Efficient, Best-Effort Content Location in Cache Networks

For several years, web caching has been used to meet the ever-increasing Web access loads. A fundamental capability of all such systems is that of inter-cache coordination, which can be divided into two main types: explicit and implicit coordination. While the former allows for greater control over resource allocation, the latter does not suffer from the additional communication overhead needed for coordination. In this paper, we consider a network in which each router has a local cache that caches files passing through it. By additionally storing minimal information regarding caching history, we develop a simple content caching, location, and routing systems that adopts an implicit, transparent, and best-effort approach towards caching. Though only best effort, the policy outperforms classic policies that allow explicit coordination between caches.
IEC 61850 based OPC UA Communication-The Future of Smart Grid Automation

Within this contribution, we outline the use of the new automation standards family OPC Unified Architecture (IEC 62541) in scope with the IEC 61850 field automation standard. The IEC 61850 provides both an abstract data model and an abstract communication interface. Different technology mappings to implement the model exist. With the upcoming OPC UA, a new communication model to implement abstract interfaces has been introduced. We outline its use in this contribution and also give examples on how it can be used alongside the IEC 61970 Common Information Model to properly integrate ICT and field automation at communication standards level.
Speed Control of Induction Motor using PI and PID Controller

This paper based on the speed control of induction motor (IM) using proportional integral controller (PI controller) and proportional integral derivative controller (PID controller) with the use of vector control technique. The conventional PID controller is compared with the conventional PI controller for full load condition. MATLAB simulation is carried out and results are investigated for speed control of Induction Motor without any controller, with PI controller and with PID controller on full load condition.
Semantic trajectories: Mobility data computation and annotation

With the large-scale adoption of GPS equipped mobile sensing devices, positional data generated by moving objects (e.g., vehicles, people, animals) are being easily collected. Such data are typically modeled as streams of spatio-temporal (x,y,t) points, called trajectories. In recent years trajectory management research has progressed significantly towards efficient storage and indexing techniques, as well as suitable knowledge discovery. These works focused on the geometric aspect of the raw mobility data. We are now witnessing a growing demand in several application sectors (e.g., from shipment tracking to geo-social networks) on understanding the semantic behavior of moving objects. Semantic behavior refers to the use of semantic abstractions of the raw mobility data, including not only geometric patterns but also knowledge extracted jointly from the mobility data and the underlying geographic and application domains information. The core contribution of this article lies in a semantic model and a computation and annotation platform for developing a semantic approach that progressively transforms the raw mobility data into semantic trajectories enriched with segmentations and annotations. We also analyze a number of experiments we did with semantic trajectories in different domains.
Global Belief Recursive Neural Networks

Recursive Neural Networks have recently obtained state of the art performance on several natural language processing tasks. However, because of their feedforward architecture they cannot correctly predict phrase or word labels that are determined by context. This is a problem in tasks such as aspect-specific sentiment classification which tries to, for instance, predict that the word Android is positive in the sentence Android beats iOS. We introduce global belief recursive neural networks (GB-RNNs) which are based on the idea of extending purely feedforward neural networks to include one feedbackward step during inference. This allows phrase level predictions and representations to give feedback to words. We show the effectiveness of this model on the task of contextual sentiment analysis. We also show that dropout can improve RNN training and that a combination of unsupervised and supervised word vector representations performs better than either alone. The feedbackward step improves F1 performance by 3% over the standard RNN on this task, obtains state-of-the-art performance on the SemEval 2013 challenge and can accurately predict the sentiment of specific entities.
Target-Dependent Sentiment Classification with Long Short Term Memory

Target-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence. Different context words have different influences on determining the sentiment polarity of a sentence towards the target. Therefore, it is desirable to integrate the connections between target word and context words when building a learning system. In this paper, we develop two target dependent long short-term memory (LSTM) models, where target information is automatically taken into account. We evaluate our methods on a benchmark dataset from Twitter. Empirical results show that modeling sentence representation with standard LSTM does not perform well. Incorporating target information into LSTM can significantly boost the classification accuracy. The target-dependent LSTM models achieve state-of-the-art performances without using syntactic parser or external sentiment
Guide to measuring privacy concern: Review of survey and observational instruments

The debate about online privacy gives testimony of Web users’ concerns. Privacy concerns make consumers adopt data protection features, guide their appreciation for existing features, and can steer their consumption choices amongst competing businesses. However, approaches to measure privacy concern are fragmented and often ad-hoc, at the detriment of reliable results. The need for measurement instruments for privacy concern is twofold. First, attitudes and opinions about data protection cannot be established and compared without reliable mechanisms. Second, behavioural studies, notably in technology acceptance and the behavioural economics of privacy require measures for concern as a moderating factor. In its first part, this paper provides a comprehensive review of existing survey instruments for measuring privacy concerns. The second part focuses on revealed preferences that can be used for opportunistically measuring privacy concerns in the wild or for scale validation. Recommendations for scale selection and reuse are provided.
TrustCloud: A Framework for Accountability and Trust in Cloud Computing

The key barrier to widespread uptake of cloud computing is the lack of trust in clouds by potential customers. While preventive controls for security and privacy are actively researched, there is still little focus on detective controls related to cloud accountability and audit ability. The complexity resulting from large-scale virtualization and data distribution carried out in current clouds has revealed an urgent research agenda for cloud accountability, as has the shift in focus of customer concerns from servers to data. This paper discusses key issues and challenges in achieving a trusted cloud through the use of detective controls, and presents the Trust Cloud framework, which addresses accountability in cloud computing via technical and policy-based approaches.
An efficient segmentation algorithm for CAPTCHAs with line cluttering and character warping

A CAPTCHA is a test designed to distinguish computer programs from human beings, in order to prevent the abuse of networked resources. Academic research into CAPTCHAs includes designing friendly and secure CAPTCHA systems and defeating existing CAPTCHA systems. Traditionally, defeating a CAPTCHA test requires two procedures: segmentation and recognition. Recent research shows that the problem of segmentation is much harder than recognition. In this paper, two new segmentation techniques called projection and middle-axis point separation are proposed for CAPTCHAs with line cluttering and character warping. Experimental results show the proposed techniques can achieve segmentation rates of about 75%.
NLPTEA 2017 Shared Task - Chinese Spelling Check

This paper provides an overview along with our findings of the Chinese Spelling Check shared task at NLPTEA 2017. The goal of this task is to develop a computerassisted system to automatically diagnose typing errors in traditional Chinese sentences written by students. We defined six types of errors which belong to two categories. Given a sentence, the system should detect where the errors are, and for each detected error determine its type and provide correction suggestions. We designed, constructed, and released a benchmark dataset for this task.
SPICE: Semantic Propositional Image Caption Evaluation

There is considerable interest in the task of automatically generating image captions. However, evaluation is challenging. Existing automatic evaluation metrics are primarily sensitive to n-gram overlap, which is neither necessary nor sufficient for the task of simulating human judgment. We hypothesize that semantic propositional content is an important component of human caption evaluation, and propose a new automated caption evaluation metric defined over scene graphs coined SPICE. Extensive evaluations across a range of models and datasets indicate that SPICE captures human judgments over model-generated captions better than other automatic metrics (e.g., system-level correlation of 0.88 with human judgments on the MS COCO dataset, versus 0.43 for CIDEr and 0.53 for METEOR). Furthermore, SPICE can answer questions such as which caption-generator best understands colors? and can caption-generators count?
RDF2Vec: RDF Graph Embeddings for Data Mining

Linked Open Data has been recognized as a valuable source for background information in data mining. However, most data mining tools require features in propositional form, i.e., a vector of nominal or numerical features associated with an instance, while Linked Open Data sources are graphs by nature. In this paper, we present RDF2Vec, an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs. We generate sequences by leveraging local information from graph substructures, harvested by Weisfeiler-Lehman Subtree RDF Graph Kernels and graph walks, and learn latent numerical representations of entities in RDF graphs. Our evaluation shows that such vector representations outperform existing techniques for the propositionalization of RDF graphs on a variety of different predictive machine learning tasks, and that feature vector representations of general knowledge graphs such as DBpedia and Wikidata can be easily reused for different tasks.
Collaborative filtering with temporal dynamics

Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are evolving, leading them to ever redefine their taste. Thus, modeling temporal dynamics should be a key when designing recommender systems or general customer preference models. However, this raises unique challenges. Within the eco-system intersecting multiple products and customers, many different characteristics are shifting simultaneously, while many of them influence each other and often those shifts are delicate and associated with a few data instances. This distinguishes the problem from concept drift explorations, where mostly a single concept is tracked. Classical time-window or instance-decay approaches cannot work, as they lose too much signal when discarding data instances. A more sensitive approach is required, which can make better distinctions between transient effects and long term patterns. The paradigm we offer is creating a model tracking the time changing behavior throughout the life span of the data. This allows us to exploit the relevant components of all data instances, while discarding only what is modeled as being irrelevant. Accordingly, we revamp two leading collaborative filtering recommendation approaches. Evaluation is made on a large movie rating dataset by Netflix. Results are encouraging and better than those previously reported on this dataset.
Implementing an extension of the analytical hierarchy process using ordered weighted averaging operators with fuzzy quantifiers in ArcGIS

This paper focuses on the integration of GIS and an extension of the analytical hierarchy process (AHP) using quantifier-guided ordered weighted averaging (OWA) procedure. AHP_OWA is a multicriteria combination operator. The nature of the AHP_OWA depends on some parameters, which are expressed by means of fuzzy linguistic quantifiers. By changing the linguistic terms, AHP_OWA can generate a wide range of decision strategies. We propose a GIS-multicriteria evaluation (MCE) system through implementation of AHP_OWA within ArcGIS, capable of integrating linguistic labels within conventional AHP for spatial decision making. We suggest that the proposed GIS-MCE would simplify the definition of decision strategies and facilitate an exploratory analysis of multiple criteria by incorporating qualitative information within the analysis.
Multiple Account Identity Deception Detection in Social Media Using Nonverbal Behavior

Identity deception has become an increasingly important issue in the social media environment. The case of blocked users initiating new accounts, often called sockpuppetry, is widely known and past efforts, which have attempted to detect such users, have been primarily based on verbal behavior (e.g., using profile data or lexical features in text). Although these methods yield a high detection accuracy rate, they are computationally inefficient for the social media environment, which often involves databases with large volumes of data. To date, little attention has been paid to detecting online deception using nonverbal behavior. We present a detection method based on nonverbal behavior for identity deception, which can be applied to many types of social media. Using Wikipedia as an experimental case, we demonstrate that our proposed method results in high detection accuracy over previous methods proposed while being computationally efficient for the social media environment. We also demonstrate the potential of nonverbal behavior data that exists in social media and how designers and developers can leverage such nonverbal information in detecting deception to safeguard their online communities.
Reconfigurable Rhombus-Shaped Patch Antenna With Y-Shaped Feed for Polarization Diversity

In this letter, a reconfigurable rhombus-shaped patch antenna with a Y-shaped feedline for polarization diversity is proposed. This antenna exhibits a simple structure, which consists of a rhombus-shaped patch, two p-i-n diodes, and a Y-shaped feedline. The two p-i-n diodes are inserted in the gap between the rhombus-shaped patch and the Y-shaped feedline. In order to easily supply a dc bias voltage to the p-i-n diodes, a shorting pin is inserted in the center of the rhombus-shaped patch. The proposed antenna can be operated in two modes-the left-hand circular polarization (LHCP) and right-hand circular polarization (RHCP) modes-by electrically controlling the two p-i-n diodes. The measured 10-dB reflection coefficient and 3-dB axial-ratio bandwidths of the prototype antenna are approximately 5.62% and 2%, respectively.
Threshold voltage distribution in MLC NAND flash memory: Characterization, analysis, and modeling

With continued scaling of NAND flash memory process technology and multiple bits programmed per cell, NAND flash reliability and endurance are degrading. Understanding, characterizing, and modeling the distribution of the threshold voltages across different cells in a modern multi-level cell (MLC) flash memory can enable the design of more effective and efficient error correction mechanisms to combat this degradation. We show the first published experimental measurement-based characterization of the threshold voltage distribution of flash memory. To accomplish this, we develop a testing infrastructure that uses the read retry feature present in some 2Y-nm (i.e., 20-24nm) flash chips. We devise a model of the threshold voltage distributions taking into account program/erase (P/E) cycle effects, analyze the noise in the distributions, and evaluate the accuracy of our model. A key result is that the threshold voltage distribution can be modeled, with more than 95% accuracy, as a Gaussian distribution with additive white noise, which shifts to the right and widens as P/E cycles increase. The novel characterization and models provided in this paper can enable the design of more effective error tolerance mechanisms for future flash memories.