nils-herrmann/clean_scirepeval huggingface dataset:
Clustering Spatial Data in the Presence of Obstacles: a Density-Based Approach

Clustering spatial data is a well-known problem that has been extensively studied. Grouping similar data in large 2-dimensional spaces to find hidden patterns or meaningful sub-groups has many applications such as satellite imagery, geographic information systems, medical image analysis, marketing, computer visions, etc. Although many methods have been proposed in the literature, very few have considered physical obstacles that may have significant consequences on the effectiveness of the clustering. Taking into account these constraints during the clustering process is costly and the modeling of the constraints is paramount for good performance. In this paper, we investigate the problem of clustering in the presence of constraints such as physical obstacles and introduce a new approach to model these constraints using polygons. We also propose a strategy to prune the search space and reduce the number of polygons to test during clustering. We devise a density-based clustering algorithm, DBCluC, which takes advantage of our constraint modeling to efficiently cluster data objects while considering all physical constraints. The algorithm can detect clusters of arbitrary shape and is insensitive to noise, the input order, and the difficulty of constraints. Its average running complexity is O(NlogN) where N is the number of
Probability Risk Identification Based Intrusion Detection System for SCADA Systems

As Supervisory Control and Data Acquisition (SCADA) systems control several critical infrastructures, they have connected to the internet. Consequently, SCADA systems face different sophisticated types of cyber adversaries. This paper suggests a Probability Risk Identification based Intrusion Detection System (PRI-IDS) technique based on analysing network traffic of Modbus TCP/IP for identifying replay attacks. It is acknowledged that Modbus TCP is usually vulnerable due to its unauthenticated and unencrypted nature. Our technique is evaluated using a simulation environment by configuring a testbed, which is a custom SCADA network that is cheap, accurate and scalable. The testbed is exploited when testing the IDS by sending individual packets from an attacker located on the same LAN as the Modbus master and slave. The experimental results demonstrated that the proposed technique can effectively and efficiently recognise replay attacks.
Literature Review of Automatic Multiple Documents Text Summarization

For the blessing of World Wide Web, the corpus of online information is gigantic in its volume. Search engines have been developed such as Google, AltaVista, Yahoo, etc., to retrieve specific information from this huge amount of data. But the outcome of search engine is unable to provide expected result as the quantity of information is increasing enormously day by day and the findings are abundant. So, the automatic text summarization is demanded for salient information retrieval. Automatic text summarization is a system of summarizing text by computer where a text is given to the computer as input and the output is a shorter and less redundant form of the original text. An informative précis is very much helpful in our daily life to save valuable time. Research was first started naively on single document abridgement but recently information is found from various sources about a single topic in different website, journal, newspaper, text book, etc., for which multi-document summarization is required. In this paper, automatic multiple documents text summarization task is addressed and different procedure of various researchers are discussed. Various techniques are compared here that have done for multi-document summarization. Some promising approaches are indicated here and particular concentration is dedicated to describe different methods from raw level to similar like human experts, so that in future one can get significant instruction for further analysis.
Instance-level Facial Attributes Transfer with Geometry-Aware Flow

We address the problem of instance-level facial attribute transfer without paired training data, e.g., faithfully transferring the exact mustache from a source face to a target face. This is a more challenging task than the conventional semantic-level attribute transfer, which only preserves the generic attribute style instead of instance-level traits. We propose the use of geometry-aware flow, which serves as a wellsuited representation for modeling the transformation between instance-level facial attributes. Specifically, we leverage the facial landmarks as the geometric guidance to learn the differentiable flows automatically, despite of the large pose gap existed. Geometry-aware flow is able to warp the source face attribute into the target face context and generate a warp-and-blend result. To compensate for the potential appearance gap between source and target faces, we propose a hallucination sub-network that produces an appearance residual to further refine the warp-and-blend result. Finally, a cycle-consistency framework consisting of both attribute transfer module and attribute removal module is designed, so that abundant unpaired face images can be used as training data. Extensive evaluations validate the capability of our approach in transferring instance-level facial attributes faithfully across large pose and appearance gaps. Thanks to the flow representation, our approach can readily be applied to generate realistic details on high-resolution images.
An Update on Business-IT Alignment: "A Line" Has Been Drawn

Our research has found that no alignment silver bullet exists. Rather, alignment involves interrelated capabilities that can be gauged by measuring six components: communications, value, governance, partnership, scope and architecture, and skills. These six components can then be placed in a five-level maturity model, where Level 5 is the highest maturity. From measuring these six components in 197 mainly Global 1,000 organizations in the United States, Latin America, Europe, and India, we found that most organizations today are at Level 3.
Reasons for Playing Casual Video Games and Perceived Benefits Among Adults 18 to 80 Years Old

Casual video games (CVGs) are becoming increasingly popular among middle-aged and older adults, yet there are few studies documenting why adults of different ages play these games, what benefits they perceive, and how regularly they play. The present study compared the online survey responses of 10,308 adults ranging from 18 to 80 years of age to questions regarding PopCap's popular free online game, Bejeweled Blitz (BJB). All respondents cited playing against friends as their main reason for playing. However, there were differences by age in the second most frequently cited reason. Middle-aged adults cited stress relief, and older adults reported that they seek the game's challenges. As a result of playing CVGs, younger adults noted that they felt sharper and experienced improved memory; older adults were more likely to feel that their visuospatial skills and response time benefited. Adults aged 60 and older had heavier patterns of game play than did adults under the age of 60 years. A significant number of respondents (14.7%) spontaneously noted that they felt that BJB had addictive qualities. CVG players seem to be drawn into this activity by its social nature and to a certain extent by its reinforcing properties. Once involved, however, they believe that they derive a number of benefits that, for older adults, appear to offset declines in age-sensitive cognitive functions.
An Italian Twitter Corpus of Hate Speech against Immigrants

The paper describes a recently-created Twitter corpus of about 6,000 tweets, annotated for hate speech against immigrants, and developed to be a reference dataset for an automatic system of hate speech monitoring. The annotation scheme was therefore specifically designed to account for the multiplicity of factors that can contribute to the definition of a hate speech notion, and to offer a broader tagset capable of better representing all those factors, which may increase, or rather mitigate, the impact of the message. This resulted in a scheme that includes, besides hate speech, the following categories: aggressiveness, offensiveness, irony, stereotype, and (on an experimental basis) intensity. The paper hereby presented namely focuses on how this annotation scheme was designed and applied to the corpus. In particular, also comparing the annotation produced by CrowdFlower contributors and by expert annotators, we make some remarks about the value of the novel resource as gold standard, which stems from a preliminary qualitative analysis of the annotated data and on future corpus development.
Location-based Resource Allocation for mobile D2D communications in multicell deployments

Cooperative intelligent traffic systems (C-ITS) will help improve the safety and efficiency of ground transportation by enabling the cooperation between traffic participants. Applications based on the C-ITS paradigm rely on vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-device (collectively, V2X) communication for the exchange of critical information and have very stringent Quality of Service (QoS) requirements on the reliability and availability of the communication links. In order to enable V2X communication using the envisioned device-to-device underlay of future 5G cellular networks, we have introduced a Location Dependent Resource Allocation Scheme (LDRAS) in . In this paper, we enhance and extend LDRAS to multicell deployments, and quantify its performance based on extensive system level simulations. The results show that LDRAS significantly improves the QoS for future C-ITS applications, from radio resource management point of view. However, satisfying the requirements of such services necessitates further improvements of 5G networks over current 4G deployments.
Personality traits associated with problematic and non-problematic massively multiplayer online role playing game use

This research investigated the associations between personality traits and both normal and problematic massively multiplayer online role playing game (MMORPGs) play, as measured by a self report scale. A total of 225 participants completed the online questionnaire, 66 of whom played MMORPGs. Correlational analyses indicated that low levels of functional impulsivity and agreeableness alongside high levels of verbal aggression and video game dependency were associated with greater amount of time spent playing MMORPGs. When comparing problematic and non-problematic MMORPG players directly, problematic players were found to be lower in self regulation, dysfunctional impulsivity and agreeableness, suggesting that these traits may be important in the development and maintenance of problematic
Online learning for “thing-adaptive” Fog Computing in IoT

The present paper deals with online convex optimization involving time-varying loss functions and time-varying constraints. The constraints are revealed after making decisions, and allow instantaneous violations yet they must be satisfied in the long term. This setting fits nicely emerging online tasks such as fog computing, where online decisions need to flexibly adapt to the temporally unpredictable availability of resources. Tailored for heterogeneous systems such as those involved in the Internet of Things (IoT), a "thing-adaptive" online saddle-point (TAOSP) scheme is developed, which automatically adjusts the stepsize to offer desirable task-specific learning rates. Performance here is assessed by: i) dynamic regret that generalizes the widely used static regret; and, ii) dynamic fit that captures the accumulated amount of constraint violations. Specifically, TAOSP is proved to simultaneously yield sub-linear dynamic regret and fit, provided that the best dynamic solutions vary slowly over time. Numerical tests in fog offloading tasks corroborate that our TAOSP approach outperforms the state of the art in speeding up computations.
A Web Scraping Methodology for Bypassing Twitter API Restrictions

Retrieving information from social networks is the first and primordial step many data analysis fields such as Natural Language Processing, Sentiment Analysis and Machine Learning. Important data science tasks relay on historical data gathering for further predictive results. Most of the recent works use Twitter API, a public platform for collecting public streams of information, which allows querying chronological tweets for no more than three weeks old. In this paper, we present a new methodology for collecting historical tweets within any date range using web scraping techniques bypassing for Twitter API restrictions.
OCR binarization and image pre-processing for searching historical documents

We consider the problem of document binarization as a pre-processing step for optical character recognition (OCR) for the purpose of keyword search of historical printed documents. A number of promising techniques from the literature for binarization, pre-filtering, and post-binarization denoising were implemented along with newly developed methods for binarization: an error diffusion binarization, a multiresolutional version of Otsu’s binarization, and denoising by despeckling. The OCR in the ABBYY FineReader 7.1 SDK is used as a black box metric to compare methods. Results for 12 pages from six newspapers of differing quality show that performance varies widely by image, but that the classic Otsu method and Otsu-based methods perform best on average. 2006 Pattern Recognition Society.
Natural Image Stitching with the Global Similarity Prior

This paper proposes a method for stitching multiple images together so that the stitched image looks as natural as possible. Our method adopts the local warp model and guides the warping of each image with a grid mesh. An objective function is designed for specifying the desired characteristics of the warps. In addition to good alignment and minimal local distortion, we add a global similarity prior in the objective function. This prior constrains the warp of each image so that it resembles a similarity transformation as a whole. The selection of the similarity transformation is crucial to the naturalness of the results. We propose methods for selecting the proper scale and rotation for each image. The warps of all images are solved together for minimizing the distortion globally. A comprehensive evaluation shows that the proposed method consistently outperforms several state-of-the-art methods, including AutoStitch, APAP, SPHP and ANNAP.
Cooperation between the default mode network and the frontal–parietal network in the production of an internal train of thought

The ability to generate and sustain an internal train of thought unrelated to external reality frees an agent from the constraints of only acting on immediate, environmentally triggered events. The current paper proposes that such thought is produced through cooperation between autobiographical information provided by the default mode network and a frontal-parietal control network which helps sustain and buffer internal trains of thought against disruption by the external world. This hypothesis explains at least two features of the literature on internally guided thought. First, access to the top-down control system is a generally accepted prerequisite of conscious experience; this explains why activation of this system and default mode activity is often observed together during periods of internally guided thought. Second, because the top-down attentional control system has a limited capacity, internally and externally driven streams can come into conflict, with the result that perceptual information must be denied attentional amplification if the internal stream is to be maintained. This explains why internal thought is routinely associated with a state of perceptual decoupling, reflected in both measured anticorrelations between the default mode network and sensory areas and the manner in which task unrelated thoughts compromise task performance. This paper offers a hypothesis that should help to constrain and guide interpretations, investigations, and analyses of the neural processes involved in internally driven cognition. This article is part of a Special Issue entitled Special Issue The Cognitive Neuroscience.
Dynamic complex event processing — Adaptive rule engine

Recently Complex Event Processing has emerged as a lightweight way to monitor multiple data streams to identify events or patterns of interest. Complex Event Processing is a rule based technology. Rules which represent the patterns of interest are defined by domain experts. Existing CEP engines have limitation on addition, deletion and modification of rules. Rules are configured initially once and are not changed later. Some of the engines support initial configuration and occasionally support addition of rules at runtime using the polling mechanism. This paper proposes a dynamic CEP by making the rule engine adaptive by using Event driven or push based approach for updating the rules dynamically in the Complex Event Processing System at runtime. Event driven approach facilitates Knowledgebase to update rules without polling or observing any resource and hence enhances the system performance. The resulting CEP engine with dynamic infrastructure facilitates easy addition and deletion of the rules in an easy and efficient way.
Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train

For the past 5 years, the ILSVRC competition and the ImageNet dataset have attracted a lot of interest from the Computer Vision community, allowing for state-of-the-art accuracy to grow tremendously. This should be credited to the use of deep artificial neural network designs. As these became more complex, the storage, bandwidth, and compute requirements increased. This means that with a non-distributed approach, even when using the most high-density server available, the training process may take weeks, making it prohibitive. Furthermore, as datasets grow, the representation learning potential of deep networks grows as well by using more complex models. This synchronicity triggers a sharp increase in the computational requirements and motivates us to explore the scaling behaviour on petaflop scale supercomputers. In this paper we will describe the challenges and novel solutions needed in order to train ResNet50 in this large scale environment. We demonstrate above 90% scaling efficiency and a training time of 28 minutes using up to 104K x86 cores. This is supported by software tools from Intel’s ecosystem. Moreover, we show that with regular 90 120 epoch train runs we can achieve a top-1 accuracy as high as 77% for the unmodified ResNet-50 topology. We also introduce the novel Collapsed Ensemble (CE) technique that allows us to obtain a 77.5% top-1 accuracy, similar to that of a ResNet-152, while training a unmodified ResNet-50 topology for the same fixed training budget. All ResNet-50 models as well as the scripts needed to replicate them will be posted shortly. deep learning, scaling, convergence, large minibatch, ensembles.
Assisting Users in a World Full of Cameras: A Privacy-Aware Infrastructure for Computer Vision Applications

Computer vision based technologies have seen widespread adoption over the recent years. This use is not limited to the rapid adoption of facial recognition technology but extends to facial expression recognition, scene recognition and more. These developments raise privacy concerns and call for novel solutions to ensure adequate user awareness, and ideally, control over the resulting collection and use of potentially sensitive data. While cameras have become ubiquitous, most of the time users are not even aware of their presence. In this paper we introduce a novel distributed privacy infrastructure for the Internet-of-Things and discuss in particular how it can help enhance user's awareness of and control over the collection and use of video data about them. The infrastructure, which has undergone early deployment and evaluation on two campuses, supports the automated discovery of IoT resources and the selective notification of users. This includes the presence of computer vision applications that collect data about users. In particular, we describe an implementation of functionality that helps users discover nearby cameras and choose whether or not they want their faces to be denatured in the video streams.
Supply chain information systems strategy : Impacts on supply chain performance and firm performance

This paper examines the relationship between supply chain (SC) strategy and supply chain information systems (IS) strategy, and its impact on supply chain performance and firm performance. Theorizing from the supply chain and IS literatures within an overarching framework of the information processing theory (IPT), we develop hypotheses proposing a positive moderating effect of two supply chain IS strategies – IS for Efficiency and IS for Flexibility – on the respective relationships between two SC strategies – Lean and Agile, and supply chain performance. Based on confirmatory analysis and structural equation modeling of survey data from members of senior and executive management in the purchase/materials management/logistics/supply chain functions, from 205 firms, we validate these hypotheses and show that the IS for Efficiency (IS for Flexibility) IS strategy enhances the relationship between Lean (Agile) SC strategy and supply chain performance. We also show a positive association between supply chain performance and firm performance, and a full (partial) mediation effect of supply chain performance on the relation between Agile (Lean) SC strategy and firm performance. The paper contributes to the supply chain literature by providing theoretical understanding and empirical support of how SC strategies and IS strategies can work together to boost supply chain performance. In doing so, it identifies particular types of supply chain IS application portfolios that can enhance the benefits from specific SC strategies. The paper also develops and validates instruments for measuring two types of SC strategies and supply chain IS strategies. For practice, the paper offers guidance in making investment decisions for adopting and deploying IS appropriate to particular SC strategies and analyzing possible lack of alignment between applications that the firm deploys in its supply chain, and the information processing needs of its SC strategy. & 2012 Elsevier B.V. All rights reserved.
Implementation of a vehicle tracking system using smartphone and SMS service

In this dynamic life where everyone is in a hurry to reach their destinations, waiting for bus is hectic and even many of us are unaware of the bus timing. To overcome this difficulty, an easy system is proposed in this paper to aid tracking real time bus location. The proposed solution takes advantages of the two main features in mobile platform nowadays which are location services, mainly GPS based, and basic telephony services, mainly SMS based. The system consists of two sides, server side and client side. The server device's main responsibility is to provide the exact location of the bus to the server, or to the user in case of SMS based query from client's device. On the other hand, client's device can find bus location either using SMS service or using internet service. If clients' device is an android based smartphone, he can install our application to track bus location using internet service. The server's device will be placed on the vehicle of interest with android application installed on it. Experiments were made with this system and found that it performs better in many ways than other similar vehicle tracking systems.