nils-herrmann/clean_scirepeval huggingface dataset:
A Comprehensive Survey on Security in Cloud Computing

According to a Forbes’ report published in 2015, cloud-based security spending is expected to increase by 42%. According to another research, the IT security expenditure had increased to 79.1% by 2015, showing an increase of more than 10% each year. International Data Corporation (IDC) in 2011 showed that 74.6% of enterprise customers ranked security as a major challenge. This paper summarizes a number of peer-reviewed articles on security threats in cloud computing and the preventive methods. The objective of our research is to understand the cloud components, security issues, and risks, along with emerging solutions that may potentially mitigate the vulnerabilities in the cloud. It is a commonly accepted fact that since 2008, cloud is a viable hosting platform; however, the perception with respect to security in the cloud is that it needs significant improvements to realise higher rates of adaption in the enterprise scale. As identified by another research, many of the issues confronting the cloud computing need to be resolved urgently. The industry has made significant advances in combatting threats to cloud computing, but there is more to be done to achieve a level of maturity that currently exists with traditional/on-premise hosting.
Experimental personality designs: analyzing categorical by continuous variable interactions.

Theories hypothesizing interactions between a categorical and one or more continuous variables are common in personality research. Traditionally, such hypotheses have been tested using nonoptimal adaptations of analysis of variance (ANOVA). This article describes an alternative multiple regression-based approach that has greater power and protects against spurious conclusions concerning the impact of individual predictors on the outcome in the presence of interactions. We discuss the structuring of the regression equation, the selection of a coding system for the categorical variable, and the importance of centering the continuous variable. We present in detail the interpretation of the effects of both individual predictors and their interactions as a function of the coding system selected for the categorical variable. We illustrate two- and three-dimensional graphical displays of the results and present methods for conducting post hoc tests following a significant interaction. The application of multiple regression techniques is illustrated through the analysis of two data sets. We show how multiple regression can produce all of the information provided by traditional but less optimal ANOVA procedures.
A Broadband Dual-Mode Monopole Antenna Using NRI-TL Metamaterial Loading

A printed monopole antenna is proposed which uses negative-refractive-index transmission-line (NRI-TL) metamaterial loading in order to achieve a broadband dual-mode operation. The metamaterial-loaded monopole supports a predominately even-mode current at 5.5 GHz, which allows the antenna to be modeled as a short folded monopole. Around 3.55 GHz, the metamaterial-loaded monopole acts as a balun for the ground plane currents, therefore rendering the entire top edge of the ground plane as the main radiating element. This in turn radiates a dipolar mode that is orthogonal to the folded-monopole mode at 5.5 GHz. By virtue of the orthogonality between the two radiating modes, the metamaterial antenna exhibits a return-loss characteristic with a dual resonance, and therefore a very wide measured impedance bandwidth of 4.06 GHz. The total size of the antenna is only 22 times 30 mm, and the measured efficiency is on the order of 90% at both 3.55 and 5.5 GHz.
Data-free parameter pruning for Deep Neural Networks

Deep Neural nets (NNs) with millions of parameters are at the heart of many stateof-the-art computer vision systems today. However, recent works have shown that much smaller models can achieve similar levels of performance. In this work, we address the problem of pruning parameters in a trained NN model. Instead of removing individual weights one at a time as done in previous works, we remove one neuron at a time. We show how similar neurons are redundant, and propose a systematic way to remove them. Our experiments in pruning the densely connected layers show that we can remove upto 85% of the total parameters in an MNIST-trained network, and about 35% for AlexNet without significantly affecting performance. Our method can be applied on top of most networks with a fully connected layer to give a smaller network.
Inspection Robot for Hanger Cable of Suspension Bridge: Mechanism Design and Analysis

Periodic maintenance of suspension bridge is critical to the safety of the bridge structure. Because most of the load on the bridge is supported by the cables, inspections on main cable or hanger are very important. In this paper, we propose a cable-climbing robot enabling convenient and safe visual inspection of the hanger cable. The robot is comprised of three identical modules assembled 120 ° apart circumferentially on a frame around the cable, and each module has three functional mechanisms, that is, driving, adhesion, and safe landing. The robot is controlled wirelessly by an operator on the ground, though ac power is supplied via a tether cable. In addition, the vision sensor consisting of three cameras provides images taken from three different directions covering the cable and the odometer made with a soft idler wheel locates the robot on the cable. The robot is able to transmit the visual images of the cable surfaces in realtime at a position, while climbing up and down. In this paper, the kinematic and force analysis of the robot is performed and its feasibility is experimentally demonstrated.
Re-tiling polygonal surfaces

This paper presents an automatic method of creating surface models at several levels of detail from an original polygonal description of a given object. Representing models at various levels of detail is important for achieving high frame rates in interactive graphics applications and also for speeding-up the off-line rendering of complex scenes. Unfortunately, generating these levels of detail is a time-consuming task usually left to a human modeler. This paper shows how a new set of vertices can be distributed over the surface of a model and connected to one another to create a re-tiling of a surface that is faithful to both the geometry and the topology of the original surface. The main contributions of this paper are: 1) a robust method of connecting together new vertices over a surface, 2) a way of using an estimate of surface curvature to distribute more new vertices at regions of higher curvature and 3) a method of smoothly interpolating between models that represent the same object at different levels of detail. The key notion in the re-tiling procedure is the creation of an intermediate model called the mutual tessellation of a surface that contains both the vertices from the original model and the new points that are to become vertices in the re-tiled surface. The new model is then created by removing each original vertex and locally re-triangulating the surface in a way that matches the local connectedness of the initial surface. This technique for surface retessellation has been successfully applied to iso-surface models derived from volume data, Connolly surface molecular models and a tessellation of a minimal surface of interest to mathematicians.
Variable-Frequency Grid-Sequence Detector Based on a Quasi-Ideal Low-Pass Filter Stage and a Phase-Locked Loop

This paper proposes a filtered-sequence phase-locked loop (FSPLL) structure for detection of the positive sequence in three-phase systems. The structure includes the use of the Park transformation and moving average filters (MAF). Performance of the MAF is mathematically analyzed and represented in Bode diagrams. The analysis allows a proper selection of the window width of the optimal filter for its application in the dq transformed variables. The proposed detector structure allows fast detection of the grid voltage positive sequence (within one grid voltage cycle). The MAF eliminates completely any oscillation multiple of the frequency for which it is designed; thus, this algorithm is not affected by the presence of imbalances or harmonics in the electrical grid. Furthermore, the PLL includes a simple-frequency detector that makes frequency adaptive the frequency depending blocks. This guarantees the proper operation of the FSPLL under large frequency changes. The performance of the entire PLL-based detector is verified through simulation and experiment. It shows very good performance under several extreme grid voltage conditions.
A pseudo-CCM/DCM SIMO switching converter with freewheel switching

A single-inductor multiple-output switching converter operates in pseudo-CCM/DCM. It requires freewheeling of the inductor current during the instants when the n switch and all output p switches are off. It is fabricated in a 0.5 /spl mu/m CMOS n-well process with Voa=2.5 V and Vob=3.0 V. With 1 /spl mu/H inductor, converter efficiency is 84.7% at 1 MHz.
Decoding by linear programming

This paper considers a natural error correcting problem with real valued input/output. We wish to recover an input vector f/spl isin/R/sup n/ from corrupted measurements y=Af+e. Here, A is an m by n (coding) matrix and e is an arbitrary and unknown vector of errors. Is it possible to recover f exactly from the data y? We prove that under suitable conditions on the coding matrix A, the input f is the unique solution to the /spl lscr//sub 1/-minimization problem (/spl par/x/spl par//sub /spl lscr/1/:=/spl Sigma//sub i/|x/sub i/|) min(g/spl isin/R/sup n/) /spl par/y - Ag/spl par//sub /spl lscr/1/ provided that the support of the vector of errors is not too large, /spl par/e/spl par//sub /spl lscr/0/:=|{i:e/sub i/ /spl ne/ 0}|/spl les//spl rho//spl middot/m for some /spl rho/>0. In short, f can be recovered exactly by solving a simple convex optimization problem (which one can recast as a linear program). In addition, numerical experiments suggest that this recovery procedure works unreasonably well; f is recovered exactly even in situations where a significant fraction of the output is corrupted. This work is related to the problem of finding sparse solutions to vastly underdetermined systems of linear equations. There are also significant connections with the problem of recovering signals from highly incomplete measurements. In fact, the results introduced in this paper improve on our earlier work. Finally, underlying the success of /spl lscr//sub 1/ is a crucial property we call the uniform uncertainty principle that we shall describe in detail.
Breaking 104 bit WEP in less than 60 seconds

We demonstrate an active attack on the WEP protocol that is able to recover a 104-bit WEP key using less than 40.000 frames with a success probability of 50%. In order to succeed in 95% of all cases, 85.000 packets are needed. The IV of these packets can be randomly chosen. This is an improvement in the number of required frames by more than an order of magnitude over the best known key-recovery attacks for WEP. On a IEEE 802.11g network, the number of frames required can be obtained by re-injection in less than a minute. The required computational effort is approximately 2 RC4 key setups, which on current desktop and laptop CPUs is neglegible.
Data Analytics Using Ontologies of Management Theories: Towards Implementing "From Theory to Practice" (Application Paper)

We explore how computational ontologies can be impactful vis-à-vis the developing discipline of "data science." We posit an approach wherein management theories are represented as formal axioms, and then applied to draw inferences about data that reside in corporate databases. That is, management theories would be implemented as rules within a data analytics engine. We demonstrate a case study development of such an ontology by formally representing an accounting theory in First-Order Logic. Though quite preliminary, the idea that an information technology, namely ontologies, can potentially actualize the academic cliché, "From Theory to Practice," and be applicable to the burgeoning domain of data analytics is novel and exciting.
Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image

Recently, more and more attention is drawn to the field of medical image synthesis across modalities. Among them, the synthesis of computed tomography (CT) image from T1-weighted magnetic resonance (MR) image is of great importance, although the mapping between them is highly complex due to large gaps of appearances of the two modalities. In this work, we aim to tackle this MR-to-CT synthesis task by a novel deep embedding convolutional neural network (DECNN). Specifically, we generate the feature maps from MR images, and then transform these feature maps forward through convolutional layers in the network. We can further compute a tentative CT synthesis from the midway of the flow of feature maps, and then embed this tentative CT synthesis result back to the feature maps. This embedding operation results in better feature maps, which are further transformed forward in DECNN. After repeating this embedding procedure for several times in the network, we can eventually synthesize a final CT image in the end of the DECNN. We have validated our proposed method on both brain and prostate imaging datasets, by also comparing with the state-of-the-art methods. Experimental results suggest that our DECNN (with repeated embedding operations) demonstrates its superior performances, in terms of both the perceptive quality of the synthesized CT image and the run-time cost for synthesizing a CT image.
A report: the definition and classification of cerebral palsy April 2006.

For a variety of reasons, the definition and the classification of cerebral palsy (CP) need to be reconsidered. Modern brain imaging techniques have shed new light on the nature of the underlying brain injury and studies on the neurobiology of and pathology associated with brain development have further explored etiologic mechanisms. It is now recognized that assessing the extent of activity restriction is part of CP evaluation and that people without activity restriction should not be included in the CP rubric. Also, previous definitions have not given sufficient prominence to the non-motor neurodevelopmental disabilities of performance and behaviour that commonly accompany CP, nor to the progression of musculoskeletal difficulties that often occurs with advancing age. In order to explore this information, pertinent material was reviewed on July 11-13, 2004 at an international workshop in Bethesda, MD (USA) organized by an Executive Committee and participated in by selected leaders in the preclinical and clinical sciences. At the workshop, it was agreed that the concept 'cerebral palsy' should be retained. Suggestions were made about the content of a revised definition and classification of CP that would meet the needs of clinicians, investigators, health officials, families and the public and would provide a common language for improved communication. Panels organized by the Executive Committee used this information and additional comments from the international community to generate a report on the Definition and Classification of Cerebral Palsy, April 2006. The Executive Committee presents this report with the intent of providing a common conceptualization of CP for use by a broad international audience.
Email classification with co-training

The main problems in text classification are lack of labeled data, as well as the cost of labeling the unlabeled data. We address these problems by exploring co-training an algorithm that uses unlabeled data along with a few labeled examples to boost the performance of a classifier. We experiment with co-training on the email domain. Our results show that the performance of co-training depends on the learning algorithm it uses. In particular, Support Vector Machines significantly outperforms Naive Bayes on email classification.
Portrayals of schizophrenia by entertainment media: a content analysis of contemporary movies.

Critics of entertainment media have indicated that cinematic depictions of schizophrenia are stereotypic and characterized by misinformation about symptoms, causes, and treatment. The pervasiveness and nature of misinformation are difficult to ascertain because of the lack of empirically based studies of movies portraying schizophrenia. This study analyzed portrayals of schizophrenia in contemporary movies to ascertain prevalence of stereotypes and misinformation about schizophrenia.



English-language movies featuring at least one main character with schizophrenia that were released for showing in theaters between 1990 and 2010 were analyzed for depictions of schizophrenia. Two researchers independently rated each character with a checklist that assessed demographic characteristics, symptoms and stereotypes, causation, and treatment.



Forty-two characters from 41 movies were identified, a majority of whom were male and Caucasian. Most characters displayed positive symptoms of schizophrenia. Delusions were featured most frequently, followed by auditory and visual hallucinations. A majority of characters displayed violent behavior toward themselves or others, and nearly one-third of violent characters engaged in homicidal behavior. About one-fourth of characters committed suicide. Causation of schizophrenia was infrequently noted, although about one-fourth of movies implied that a traumatic life event was significant in causation. Of movies alluding to or showing treatment, psychotropic medications were most commonly portrayed.



The finding that misinformation and negative portrayals of schizophrenia in contemporary movies are common underscores the importance of determining how viewers interpret media messages and how these interpretations inform attitudes and beliefs both of the general public and of people with schizophrenia.
Solving Problems with Visual Analytics: Challenges and Applications

Never before in history data has been generated and collected in such high volumes as it is today. Keeping up to date with the flood of data, using standard tools for data analysis and exploration, is fraught with difficulty. Visual analytics seeks to provide people with better and more effective ways to understand and analyze large datasets, while also enabling them to act upon their findings immediately. The field integrates the analytic capabilities of the computer and the abilities of the human analyst, allowing novel discoveries and empowering individuals to take control of the analytical process. In this paper we present the challenges of visual analytics and exemplify them with a couple of application examples that illustrate the existing potential of current visual analysis techniques but also their limitations.
A database of German emotional speech

The article describes a database of emotional speech. Ten actors (5 female and 5 male) simulated the emotions, producing 10 German utterances (5 short and 5 longer sentences) which could be used in everyday communication and are interpretable in all applied emotions. The recordings were taken in an anechoic chamber with high-quality recording equipment. In addition to the sound electro-glottograms were recorded. The speech material comprises about 800 sentences (seven emotions * ten actors * ten sentences + some second versions). The complete database was evaluated in a perception test regarding the recognisability of emotions and their naturalness. Utterances recognised better than 80% and judged as natural by more than 60% of the listeners were phonetically labelled in a narrow transcription with special markers for voice-quality, phonatory and articulatory settings and articulatory features. The database can be accessed by the public via the internet (http://www.expressive-speech.net/emodb/).
Karma: A System for Mapping Structured Sources into the Semantic Web

The Linked Data cloud contains large amounts of RDF data generated from databases. Much of this RDF data, generated using tools such as D2R, is expressed in terms of vocabularies automatically derived from the schema of the original database. The generated RDF would be significantly more useful if it were expressed in terms of commonly used vocabularies. Using today’s tools, it is labor-intensive to do this. For example, one can first use D2R to automatically generate RDF from a database and then use R2R to translate the automatically generated RDF into RDF expressed in a new vocabulary. The problem is that defining the R2R mappings is difficult and labor intensive because one needs to write the mapping rules in terms of SPARQL graph patterns. In this work, we present a semi-automatic approach for building mappings that translate data in structured sources to RDF expressed in terms of a vocabulary of the user’s choice. Our system, Karma, automatically derives these mappings, and provides an easy to use interface that enables users to control the automated process to guide the system to produce the desired mappings. In our evaluation, users need to interact with the system less than once per column (on average) in order to construct the desired mapping rules. The system then uses these mapping rules to generate semantically rich RDF for the data sources. We demonstrate Karma using a bioinformatics example and contrast it with other approaches used in that community.  and Semantic MediaWiki Linked Data Extension (SMW-LDE)  are examples of efforts that integrate bioinformatics datasets by mapping them to a common vocabulary. We applied our approach to a scenario used in the SMW-LDE that integrate ABA, Uniprot, KEGG Pathway, PharmGKB and Linking Open Drug Data datasets using a
Exploring the link between reading fiction and empathy : Ruling out individual differences and examining outcomes

Readers of fiction tend to have better abilities of empathy and theory of mind (Mar et al., 2006). We present a study designed to replicate this finding, rule out one possible explanation, and extend the assessment of social outcomes. In order to rule out the role of personality, we first identified Openness as the most consistent correlate. This trait was then statistically controlled for, along with two other important individual differences: the tendency to be drawn into stories and gender. Even after accounting for these variables, fiction exposure still predicted performance on an empathy task. Extending these results, we also found that exposure to fiction was positively correlated with social support. Exposure to nonfiction, in contrast, was associated with loneliness, and negatively related to social support.
Automatic Annotation of Semantic Term Types in the Complete ACL Anthology Reference Corpus

In the present paper, we present an automated tagging approach aimed at enhancing a well-known resource, the ACL Anthology Reference Corpus, with semantic class labels for more than 20,000 technical terms that are relevant to the domain of computational linguistics. We use state-of-the-art classification techniques to assign semantic class labels to technical terms extracted from several reference term lists. We also sketch a set of research questions and approaches directed towards the integrated analysis of scientific corpora. To this end, we query the data set resulting from our annotation effort on both the term and the semantic class level level.