nils-herrmann/clean_scirepeval huggingface dataset:
Knowledge Management in

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. Is knowledge management one of those hyped concepts that rise quickly, ambitiously claims to cure organizational headaches and then fails and falls quietly? Or is it an instrument that will really help organizations address some of the problems they face while trying to achieve their business objectives? In particular, is knowledge management valuable to software development organizations? What kind of problems can KM help address and solve? What kind of solutions does KM propose to these problems? How can a KM system for a software organization be implemented? What are the challenges? What are the success factors? This report addresses the above questions and attempts to provide answers that resulted from an extensive research of the state-of-the-art and state-of-the-practice of this subject.
Hydra: an Ensemble of Convolutional Neural Networks for Geospatial Land Classification

We describe in this paper Hydra, an ensemble of convolutional neural networks (CNN) for geospatial land classification. The idea behind Hydra is to create an initial CNN that is coarsely optimized but provides a good starting pointing for further optimization, which will serve as the Hydra’s body. Then, the obtained weights are fine tuned multiple times to form an ensemble of CNNs that represent the Hydra’s heads. By doing so, we were able to reduce the training time while maintaining the classification performance of the ensemble. We created ensembles using two state-of-the-art CNN architectures, ResNet and DenseNet, to participate in the Functional Map of the World challenge. With this approach, we finished the competition in third place. We also applied the proposed framework to the NWPU-RESISC45 database and achieved the best reported performance so far. Code and CNN models are available at .
Conditional Random Field (CRF)-Boosting: Constructing a Robust Online Hybrid Boosting Multiple Object Tracker Facilitated by CRF Learning

Due to the reasonably acceptable performance of state-of-the-art object detectors, tracking-by-detection is a standard strategy for visual multi-object tracking (MOT). In particular, online MOT is more demanding due to its diverse applications in time-critical situations. A main issue of realizing online MOT is how to associate noisy object detection results on a new frame with previously being tracked objects. In this work, we propose a multi-object tracker method called CRF-boosting which utilizes a hybrid data association method based on online hybrid boosting facilitated by a conditional random field (CRF) for establishing online MOT. For data association, learned CRF is used to generate reliable low-level tracklets and then these are used as the input of the hybrid boosting. To do so, while existing data association methods based on boosting algorithms have the necessity of training data having ground truth information to improve robustness, CRF-boosting ensures sufficient robustness without such information due to the synergetic cascaded learning procedure. Further, a hierarchical feature association framework is adopted to further improve MOT accuracy. From experimental results on public datasets, we could conclude that the benefit of proposed hybrid approach compared to the other competitive MOT systems is noticeable.
Comorbid substance use disorder in schizophrenia: a selective overview of neurobiological and cognitive underpinnings.

Although individuals with schizophrenia show a lifetime prevalence of 50% for suffering from a comorbid substance use disorder, substance abuse usually represents an exclusion criterion for studies on schizophrenia. This implies that surprisingly little is known about a large group of patients who are particularly difficult to treat. The aim of the present work is to provide a brief and non-exhaustive overview of the current knowledgebase about neurobiological and cognitive underpinnings for dual diagnosis schizophrenia patients. Studies published within the last 20 years were considered using computerized search engines. The focus was on nicotine, caffeine, alcohol, cannabis and cocaine being among the most common substances of abuse. All drugs of abuse target dopaminergic, glutamatergic and GABAergic transmission which are also involved in the pathophysiology of schizophrenia. Current literature suggests that neurocognitive function might beless disrupted in substance-abusing compared to non-abusing schizophrenia patients, but in particular the neuroimaging database on this topic is sparse. Detrimental effects on brain structure and function were shown for patients for whom alcohol is the main substance of abuse. It is as yet unclear whether this finding might be an artifact of age differences of patient subgroups with different substance abuse patterns. More research is warranted on the specific neurocognitive underpinnings of schizophrenia patients abusing distinct psychoactive substances. Treatment programs might either benefit from preserved cognitive function as a resource or specifically target cognitive impairment in different subgroups of addicted schizophrenia patients.
NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems

Accurate and fine-grained prediction of future user location and geographical profile has interesting and promising applications including targeted content service, advertisement dissemination for mobile users, and recreational social networking tools for smart-phones. Existing techniques based on linear and probabilistic models are not able to provide accurate prediction of the location patterns from a spatio-temporal perspective, especially for long-term estimation. More specifically, they are able to only forecast the next location of a user, but not his/her arrival time and residence time, i.e., the interval of time spent in that location. Moreover, these techniques are often based on prediction models that are not able to extend predictions further in the future. In this paper we present NextPlace, a novel approach to location prediction based on nonlinear time series analysis of the arrival and residence times of users in relevant places. NextPlace focuses on the predictability of single users when they visit their most important places, rather than on the transitions between different locations. We report about our evaluation using four different datasets and we compare our forecasting results to those obtained by means of the prediction techniques proposed in the literature. We show how we achieve higher performance compared to other predictors and also more stability over time, with an overall prediction precision of up to 90% and a performance increment of at least 50% with respect to the state of the art.
A Brief History of Cloud Offload: A Personal Journey from Odyssey Through Cyber Foraging to Cloudlets

O CTO B E R 2014 | Volume 18, Issue 4 GetMobile Every time you use a voice command on your smartphone, you are benefitting from a technique called cloud offload. Your speech is captured by a microphone, pre-processed, then sent over a wireless network to a cloud service that converts speech to text. The result is then forwarded to another cloud service or sent back to your mobile device, depending on the application. Speech recognition and many other resource-intensive mobile services require cloud offload. Otherwise, the service would be too slow and drain too much of your battery. Research projects on cloud offload are hot today, with MAUI  in 2010, Odessa [13] and CloneCloud  in 2011, and COMET  in 2012. These build on a rich heritage of work dating back to the mid-1990s on a theme that is broadly characterized as cyber foraging. They are also relevant to the concept of cloudlets [18] that has emerged as an important theme in mobile-cloud convergence. Reflecting my participation in this evolution from its origins, this article is a personal account of the key developments in this research area. It focuses on mobile computing, ignoring many other uses of remote execution since the 1980s such as distributed processing, query processing, distributed object systems, and distributed partitioning.
Reading linear texts on paper versus computer screen : Effects on reading comprehension

To explore effects of the technological interface on reading comprehension in a Norwegian school context.  72 tenth graders from two different primary schools in Norway.  The students were randomized into two groups, where the first group read two texts (1400–2000 words) in print, and the other group read the same texts as PDF on a computer screen. In addition pretests in reading comprehension, word reading and vocabulary were administered. A multiple regression analysis was carried out to investigate to what extent reading modality would influence the students’ scores on the reading comprehension measure.  Main findings show that students who read texts in print scored significantly better on the reading comprehension test than students who read the texts digitally. Implications of these findings for policymaking and test development are discussed.
Data Mining: A prediction for performance improvement using classification

Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students’ performance. The performance in higher education in India is a turning point in the academics for all students. This academic performance is influenced by many factors, therefore it is essential to develop predictive data mining model for students’ performance so as to identify the difference between high learners and slow learners student. In the present investigation, an experimental methodology was adopted to generate a database. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 300 student records, which were used for by Byes classification prediction model construction.  Mining, Educational Data Mining, Predictive Model, Classification.
"NOFUS: Automatically Detecting" + String.fromCharCode(32) + "ObFuSCateD ".toLowerCase() + "JavaScript Code"

Obfuscation is applied to large quantities of benign and malicious JavaScript throughout the web. In situations where JavaScript source code is being submitted for widespread use, such as in a gallery of browser extensions (e.g., Firefox), it is valuable to require that the code submitted is not obfuscated and to check for that property. In this paper, we describe NOFUS, a static, automatic classifier that distinguishes obfuscated and non-obfuscated JavaScript with high precision. Using a collection of examples of both obfuscated and non-obfuscated JavaScript, we train NOFUS to distinguish between the two and show that the classifier has both a low false positive rate (about 1%) and low false negative rate (about 5%). Applying NOFUS to collections of deployed JavaScript, we show it correctly identifies obfuscated JavaScript files from Alexa top 50 websites. While prior work conflates obfuscation with maliciousness (assuming that detecting obfuscation implies maliciousness), we show that the correlation is weak. Yes, much malware is hidden using obfuscation, but so is benign JavaScript. Further, applying NOFUS to known JavaScript malware, we show our classifier finds 15% of the files are unobfuscated, showing that not all malware is obfuscated.
Adversarial Attacks on Node Embeddings

The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks. However, despite the proliferation of such methods there is currently no study of their robustness to adversarial attacks. We provide the first adversarial vulnerability analysis on the widely used family of methods based on random walks. We derive efficient adversarial perturbations that poison the network structure and have a negative effect on both the quality of the embeddings and the downstream tasks. We further show that our attacks are transferable – they generalize to many models – and are successful even when the attacker has restricted actions.
BIM Adoption and Implementation for Architectural Practices

Severe issues about data acquisition and management arise during the design creation and development due to complexity, uncertainty and ambiguity. BIM (Building Information Modelling) is a tool for a team based lean design approach towards improved architectural practice across the supply chain. However, moving from a CAD (Computer Aided Design) approach to BIM (Building Information Modelling) represents a fundamental change for individual disciplines and the construction industry as a whole. Although BIM has been implemented by large practices, it is not widely used by SMEs (Small and Medium Sized Enterprises).  This paper aims to present a systematic approach for BIM implementation for Architectural SMEs at the organizational level Design/ The research is undertaken through a KTP (Knowledge transfer Partnership) project between the University of Salford and John McCall Architects (JMA) a SME based in Liverpool. The overall aim of the KTP is to develop lean design practice through BIM adoption. The BIM implementation approach uses a socio-technical view which does not only consider the implementation of technology but also considers the socio-cultural environment that provides the context for its implementation. The action research oriented qualitative and quantitative research is used for discovery, comparison, and experimentation as it provides “learning by doing”.  The strategic approach to BIM adoption incorporated people, process and technology equally and led to capacity building through the improvements in process, technological infrastructure and upskilling of JMA staff to attain efficiency gains and competitive advantages.  This paper introduces a systematic approach for BIM adoption based on the action research philosophy and demonstrates a roadmap for BIM adoption at the operational level for SME companies.
Memory systems in the brain.

The operation of different brain systems involved in different types of memory is described. One is a system in the primate orbitofrontal cortex and amygdala involved in representing rewards and punishers, and in learning stimulus-reinforcer associations. This system is involved in emotion and motivation. A second system in the temporal cortical visual areas is involved in learning invariant representations of objects. A third system in the hippocampus is implicated in episodic memory and in spatial function. Fourth, brain systems in the frontal and temporal cortices involved in short term memory are described. The approach taken provides insight into the neuronal operations that take place in each of these brain systems, and has the aim of leading to quantitative biologically plausible neuronal network models of how each of these memory systems actually operates.
OFDM Simulation Using Matlab

Orthogonal frequency division multiplexing (OFDM) is becoming the chosen modulation technique for wireless communications. OFDM can provide large data rates with sufficient robustness to radio channel impairments. Many research centers in the world have specialized teams working in the optimization of OFDM for countless applications. Here, at the Georgia Institute of Technology, one of such teams is in Dr. M. A. Ingram's Smart Antenna Research Laboratory (SARL), a part of the Georgia Center for Advanced Telecommunications Technology (GCATT). The purpose of this report is to provide Matlab code to simulate the basic processing involved in the generation and reception of an OFDM signal in a physical channel and to provide a description of each of the steps involved. For this purpose, we shall use, as an example, one of the proposed OFDM signals of the Digital Video Broadcasting (DVB) standard for the European terrestrial digital television (DTV) service.
Improving Low Quality Stack Overflow Post Detection

Stack Overflow is a popular questions and answers (Q&A) website among software developers. It counts more than two millions of users who actively contribute by asking and answering thousands of questions daily. Identifying and reviewing low quality posts preserves the quality of site's contents and it is crucial to maintain a good user experience. In Stack Overflow the identification of poor quality posts is performed by selected users manually. The system also uses an automated identification system based on textual features. Low quality posts automatically enter a review queue maintained by experienced users. We present an approach to improve the automated system in use at Stack Overflow. It analyzes both the content of a post (e.g., simple textual features and complex readability metrics) and community-related aspects (e.g., popularity of a user in the community). Our approach reduces the size of the review queue effectively and removes misclassified good quality posts.
Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling

contrast, the proposed nonlocal network is more robust to the number of blocks. When employing the same number of nonlocal blocks, the proposed model consistently performs better than the original one. Moreover, the proposed model also reduces the computational cost because the affinity function can be pre-assigned in a nonlocal stage. Although the two formulations of nonlocal blocks appear similar, they are different in essence. Figure 4 shows the top 32 eigenvalues of the symmetric part of weight matrices after training when adding 4 new nonlocal blocks to PreResNet-20, where we can see that most of the eigenvalues are positive. In the following section, we will show that the formulation of new nonlocal blocks is exactly the nonlocal analogue of local diffusion with positive coefficients. In addition, one can observe from Table 1 and Table 2 that adding too many proposed nonlocal blocks reduces the performance. A potential reason is that all the nonlocal blocks in a stage share the same affinity function, which is pre-computed given the input features. Therefore, the kernel function cannot precisely describe the affinity between pairs of features after many sub-blocks. Note that in , the nonlocal blocks are always added individually in each fixed place of ResNets. We also compare the two nonlocal networks in the second part of Table 1, where we in total add 3 nonlocal blocks to PreResNet-20 but into different residual blocks. Although in this case the two kinds of nonlocal blocks have the same affinity kernel, the proposed nonlocal network still performs
An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems

Communication at millimeter wave (mmWave) frequencies is defining a new era of wireless communication. The mmWave band offers higher bandwidth communication channels versus those presently used in commercial wireless systems. The applications of mmWave are immense: wireless local and personal area networks in the unlicensed band, 5G cellular systems, not to mention vehicular area networks, ad hoc networks, and wearables. Signal processing is critical for enabling the next generation of mmWave communication. Due to the use of large antenna arrays at the transmitter and receiver, combined with radio frequency and mixed signal power constraints, new multiple-input multiple-output (MIMO) communication signal processing techniques are needed. Because of the wide bandwidths, low complexity transceiver algorithms become important. There are opportunities to exploit techniques like compressed sensing for channel estimation and beamforming. This article provides an overview of signal processing challenges in mmWave wireless systems, with an emphasis on those faced by using MIMO communication at higher carrier frequencies.
Bleach: A Distributed Stream Data Cleaning System

Existing scalable data cleaning approaches have focused on batch data cleaning. However, batch data cleaning is not suitable for streaming big data systems, in which dynamic data is generated continuously. Despite the increasing popularity of stream-processing systems, few stream data cleaning techniques have been proposed so far. In this paper, we bridge this gap by addressing the problem of rule-based stream data cleaning, which sets stringent requirements on latency, rule dynamics and ability to cope with the continuous nature of data streams. We design a system, called Bleach, which achieves real-time violation detection and data repair on a dirty data stream. Bleach relies on efficient, compact and distributed data structures to maintain the necessary state to repair data. Additionally, it supports rule dynamics and uses a "cumulative" sliding window operation to improve cleaning accuracy. We evaluate a prototype of Bleach using both synthetic and real data streams and experimentally validate its high throughput, low latency and high cleaning accuracy, which are preserved even with rule dynamics.
Social Media Marketing Content on Consumer Engagement : Evidence from Facebook ∗

We investigate the effect of social media content on customer engagement using a large-scale field study on Facebook. We content-code more than 100,000 unique messages across 800 companies engaging with users on Facebook using a combination of Amazon Mechanical Turk and state-of-the-art Natural Language Processing algorithms. We use this large-scale database of content attributes to test the effect of social media marketing content on subsequent user engagement − defined as Likes and comments − with the messages. We develop methods to account for potential selection biases that arise from Facebook’s filtering algorithm, EdgeRank, that assigns messages non-randomly to users. We find that inclusion of persuasive content − like emotional and philanthropic content − increases engagement with a message. We find that informative content − like mentions of prices, availability, and product features − reduce engagement when included in messages in isolation, but increase engagement when provided in combination with persuasive attributes. Persuasive content thus seems to be the key to effective engagement. Our results inform content design strategies in social media, and the methodology we develop to content-code large-scale textual data provides a framework for future studies on unstructured natural language data such as advertising content or product reviews.