nils-herrmann/clean_scirepeval huggingface dataset:
Hybrid prediction model with missing value imputation for medical data

Accurate prediction in the presence of large number of missing values in the data set has always been a challenging problem. Most of hybrid models to address this challenge have either deleted the missing instances from the data set (popularly known as case deletion) or have used some default way to fill the missing values. This paper, presents a novel Hybrid Prediction Model with missing value imputation (HPMMI) that analyze various imputation techniques using Simple K-means clustering and apply the best one to a data set. The proposed hybrid model is the first one to use combination of K-means clustering with Multilayer Perceptron. K-means Clustering is also used to validate class labels of given data (incorrectly classified instances are deleted i.e. pattern extracted from original data) before applying classifier. The proposed system has significantly improved data quality by use of best imputation technique after quantitative analysis of eleven imputation approaches. The efficiency of proposed model as predictive classification system is investigated on three benchmark medical data sets namely Pima Indians Diabetes, Wisconsin Breast Cancer, and Hepatitis from the UCI Repository of Machine Learning. In addition to accuracy, sensitivity, specificity; kappa statistics and the area under ROC are also computed. The experimental results show HPM-MI has produced accuracy, sensitivity, specificity, kappa and ROC as 99.82%, 100%, 99.7%, 0.996 and 1.0 respectively for Pima Indian diabetes data set, 99.39%, 99.31%, 99.54%, 0.986, and 1.0 respectively for Breast Cancer data set and 99.98 %, 100%, 96.55%, 0.978 and 0.99 respectively for Hepatitis data set. Results are best in comparison with existing methods. Further, the performance of our model is observed as function of missing rate and train-test ratio using 2D synthetic data set and Wisconsin Diagnostics Breast Cancer data sets. Results are promising and therefore the proposed model will be very useful in prediction for medical domain especially when numbers of missing value are large in the data set. Keyword Missing Value Imputation, Multilayer Perceptron (MLP), K-Means Clustering, Data mining.
Behavioral evaluation of consciousness in severe brain damage.

This paper reviews the current state of bedside behavioral assessment in brain-damaged patients with impaired consciousness (coma, vegetative state, minimally conscious state). As misdiagnosis in this field is unfortunately very frequent, we first discuss a number of fundamental principles of clinical evaluation that should guide the assessment of consciousness in brain-damaged patients in order to avoid confusion between vegetative state and minimally conscious state. The role of standardized behavioral assessment tools is particularly stressed. The second part of this paper reviews existing behavioral assessment techniques of consciousness, showing that there are actually a large number of these scales. After a discussion of the most widely used scale, the Glasgow Coma Scale, we present several new promising tools that show higher sensitivity and reliability for detecting subtle signs of recovery of consciousness in the post-acute setting.
A Mechatronic System for Robot-Mediated Hand Telerehabilitation

This paper presents a novel mechatronics master-slave setup for hand telerehabilitation. The system consists of a sensorized glove acting as a remote master and a powered hand exoskeleton acting as a slave. The proposed architecture presents three main innovative solutions. First, it provides the therapist with an intuitive interface (a sensorized wearable glove) for conducting the rehabilitation exercises. Second, the patient can benefit from a robot-aided physical rehabilitation in which the slave hand robotic exoskeleton can provide an effective treatment outside the clinical environment without the physical presence of the therapist. Third, the mechatronics setup is integrated with a sensorized object, which allows for the execution of manipulation exercises and the recording of patient's improvements. In this paper, we also present the results of the experimental characterization carried out to verify the system usability of the proposed architecture with healthy volunteers.
Development of a polymer-based tendon-driven wearable robotic hand

This paper presents the development of a polymer-based tendon-driven wearable robotic hand, Exo-Glove Poly. Unlike the previously developed Exo-Glove, a fabric-based tendon-driven wearable robotic hand, Exo-Glove Poly was developed using silicone to allow for sanitization between users in multiple-user environments such as hospitals. Exo-Glove Poly was developed to use two motors, one for the thumb and the other for the index/middle finger, and an under-actuation mechanism to grasp various objects. In order to realize Exo-Glove Poly, design features and fabrication processes were developed to permit adjustment to different hand sizes, to protect users from injury, to enable ventilation, and to embed Teflon tubes for the wire paths. The mechanical properties of Exo-Glove Poly were verified with a healthy subject through a wrap grasp experiment using a mat-type pressure sensor and an under-actuation performance experiment with a specialized test set-up. Finally, performance of the Exo-Glove Poly for grasping various shapes of object was verified, including objects needing under-actuation.
Finding Tiny Faces

Though tremendous strides have been made in object recognition, one of the remaining open challenges is detecting small objects. We explore three aspects of the problem in the context of finding small faces: the role of scale invariance, image resolution, and contextual reasoning. While most recognition approaches aim to be scale-invariant, the cues for recognizing a 3px tall face are fundamentally different than those for recognizing a 300px tall face. We take a different approach and train separate detectors for different scales. To maintain efficiency, detectors are trained in a multi-task fashion: they make use of features extracted from multiple layers of single (deep) feature hierarchy. While training detectors for large objects is straightforward, the crucial challenge remains training detectors for small objects. We show that context is crucial, and define templates that make use of massively-large receptive fields (where 99% of the template extends beyond the object of interest). Finally, we explore the role of scale in pre-trained deep networks, providing ways to extrapolate networks tuned for limited scales to rather extreme ranges. We demonstrate state-of-the-art results on massively-benchmarked face datasets (FDDB and WIDER FACE). In particular, when compared to prior art on WIDER FACE, our results reduce error by a factor of 2 (our models produce an AP of 82% while prior art ranges from 29-64%).
Personality and IT security: An application of the five-factor model

Despite numerous advances in IT security, many computer users are still vulnerable to security-related risks because they do not comply with organizational policies and procedures. In a network setting, individual risk can extend to all networked users. Endpoint security refers to the set of organizational policies, procedures, and practices directed at securing the endpoint of the network connections – the individual end user. As such, the challenges facing IT managers in providing effective endpoint security are unique in that they often rely heavily on end user participation. But vulnerability can be minimized through modification of desktop security programs and increased vigilance on the part of the system administrator or CSO. The cost-prohibitive nature of these measures generally dictates targeting high-risk users on an individual basis. It is therefore important to differentiate between individuals who are most likely to pose a security risk and those who will likely follow most organizational policies and procedures.
Mobile healthcare information management utilizing Cloud Computing and Android OS

Cloud Computing provides functionality for managing information data in a distributed, ubiquitous and pervasive manner supporting several platforms, systems and applications. This work presents the implementation of a mobile system that enables electronic healthcare data storage, update and retrieval using Cloud Computing. The mobile application is developed using Google's Android operating system and provides management of patient health records and medical images (supporting DICOM format and JPEG2000 coding). The developed system has been evaluated using the Amazon's S3 cloud service. This article summarizes the implementation details and presents initial results of the system in practice.
Self-disclosure on social networking sites, positive feedback, and social capital among Chinese college students

Drawing on social capital theory, this study examined whether college students’ self-disclosure on a social networking site was directly associated with social capital, or related indirectly through the degree of positive feedback students got from Internet friends. Structural equation models applied to anonymous, self-report survey data from 264 first-year students at 3 universities in Beijing, China, indicated direct effects on bridging social capital and indirect effects on bonding social capital. Effects remained significant, though modest in magnitude, after controlling for social skills level. Findings suggest ways in which social networking sites can foster social adjustment as an adolescent transition to residential col-
Using the DOM Tree for Content Extraction

The main information of a webpage is usually mixed between menus, advertisements, panels, and other not necessarily related information; and it is often difficult to automatically isolate this information. This is precisely the objective of content extraction, a research area of widely interest due to its many applications. Content extraction is useful not only for the final human user, but it is also frequently used as a preprocessing stage of different systems that need to extract the main content in a web document to avoid the treatment and processing of other useless information. Other interesting application where content extraction is particularly used is displaying webpages in small screens such as mobile phones or PDAs. In this work we present a new technique for content extraction that uses the DOM tree of the webpage to analyze the hierarchical relations of the elements in the webpage. Thanks to this information, the technique achieves a considerable recall and precision. Using the DOM structure for content extraction gives us the benefits of other approaches based on the syntax of the webpage (such as characters, words and tags), but it also gives us a very precise information regarding the related components in a block, thus, producing very cohesive blocks.
Using High-Definition maps for precise urban vehicle localization

Global Navigation Satellite Systems (GNSS) based localization in the context of Intelligent Transportation Systems, Advanced Driver Assistance Systems and ultimately the development of autonomous vehicles often requires accuracy with integrity. Low-cost data fusion algorithms for positioning that integrate GNSS and additional in-vehicle sensor information are able to handle short term GNSS outages and can provide increased availability and accuracy. Challenging are non-line-of-sight effects that are often present in urban areas. As those can hardly be modeled accurately, an unobservable position bias is often introduced which violates the estimated confidence. Therefor, mass-market applicable localization algorithms that mitigate their influence are desired to allow GNSS-based positioning for safety critical applications. With the beginning market introduction of high-definition maps (HD maps) by major map providers, another inexpensive tool to enhance data fusion based localization algorithms is available. Those maps offer means to further improve the accuracy and integrity of vehicle position estimation, especially in challenging urban environments. In this paper, a particle filter based localization algorithm is presented that integrates HD maps, GNSS and vehicle odometry measurements to demonstrate the benefits of HD maps using real world measurements.
A survey of clustering techniques and algorithms

Clustering is used data mining technique in which a group of similar objects is combined together to form clusters, these clusters are different from the objects in another clusters. This paper describes some clusterization techniques like, partitional technique, hierarchical technique, grid-based technique, density-based technique and their algorithms. Partitional method divides the data set into objects based on some similarity criterion, hierarchical method creates a hierarchy between clusters by combining the data objects into clusters, and then these clusters are further combined together to form large clusters and so on, grid-based method forms clusters by combining the data objects into grids or cells, density based method are used to separate the high dense clusters from low dense clusters.
Vehicle Traffic Light Control Using SARSA

SARSA (Sutton, 1996) is applied to a simulated, traac-light control problem (Thorpe, 1997) and its performance is compared with several, xed control strategies. The performance of SARSA with four diierent representations of the current state of traac is analyzed using two reinforcement schemes. Training on one intersection is compared to, and is as eeective as training on all intersections in the environment. SARSA is shown to be better than xed-duration light timing and four-way stops for minimizing total traac travel time, individual vehicle travel times, and vehicle wait times. Comparisons of performance using a constant reinforcement function versus a variable reinforcement function dependent on the number of vehicles at an intersection showed that the variable reinforcement resulted in slightly improved performance for some cases.
VANET: Architectures, research issues, routing protocols, and its applications

Vehicular Ad Hoc Networks (VANETs) is a form of mobile ad hoc network (MANETs)-where frequent changes in wireless network topology happen due to high mobility of vehicles. Routing of data packets in such highly mobile, multi hopped and without dedicated infrastructure environment is a big research challenges which need to be addressed by existing/proposing new solutions. In the past few years, a lot of research has happened targeting the various scenarios for VANET. The objective of this paper is to provide the details of Vehicular Ad Hoc Network basic architectures, present open research challenges, routing techniques present in literature, bio-inspired approaches towards VANET, applications and simulation tools for VANETs.
Exploring threats and vulnerabilities in hacker web: Forums, IRC and carding shops

Cybersecurity is a problem of growing relevance that impacts all facets of society. As a result, many researchers have become interested in studying cybercriminals and online hacker communities in order to develop more effective cyber defenses. In particular, analysis of hacker community contents may reveal existing and emerging threats that pose great risk to individuals, businesses, and government. Thus, we are interested in developing an automated methodology for identifying tangible and verifiable evidence of potential threats within hacker forums, IRC channels, and carding shops. To identify threats, we couple machine learning methodology with information retrieval techniques. Our approach allows us to distill potential threats from the entirety of collected hacker contents. We present several examples of identified threats found through our analysis techniques. Results suggest that hacker communities can be analyzed to aid in cyber threat detection, thus providing promising direction for future work.
An Empirical Study of Leader Ethical Values , Transformational and Transactional Leadership , and Follower Attitudes Toward Corporate Social Responsibility

Several leadership and ethics scholars suggest that the transformational leadership process is predicated on a divergent set of ethical values compared to transactional leadership. Theoretical accounts declare that deontological ethics should be associated with transformational leadership while transactional leadership is likely related to teleological ethics. However, very little empirical research supports these claims. Furthermore, despite calls for increasing attention as to how leaders influence their followers’ perceptions of the importance of ethics and corporate social responsibility (CSR) for organizational effectiveness, no empirical study to date has assessed the comparative impact of transformational and transactional leadership styles on follower CSR attitudes. Data from 122 organizational leaders and 458 of their followers indicated that leader deontological ethical values (altruism, universal rights, Kantian principles, etc.) were strongly associated with follower ratings of transformational leadership, while leader teleological ethical values (utilitarianism) were related to follower ratings of transactional leadership. As predicted, only transformational leadership was associated with follower beliefs in the stakeholder view of CSR. Implications for the study and practice of ethical leadership, future research directions, and management education are discussed.
Information as thing

Three meanings of “information” are distinguished: “Information-as-process”; “information-as-knowledge”; and “information-as-thing,” the attributive use of “information” to denote things regarded as informative. The nature and characteristics of “information-asthing” are discussed, using an indirect approach (“What things are informative?“). Varieties of “informationas-thing” include data, text, documents, objects, and events. On this view “information” includes but extends beyond communication. Whatever information storage and retrieval systems store and retrieve is necessarily “information-as-thing.” These three meanings of “information,” along with “information processing,” offer a basis for classifying disparate information-related activities (e.g., rhetoric, bibliographic retrieval, statistical analysis) and, thereby, suggest a topography for “information science.”
Deep learning in robotics: a review of recent research

Advances in deep learning over the last decade have led to a flurry of research in the application of deep artificial neural networks to robotic systems, with at least thirty papers published on the subject between 2014 and the present. This review discusses the applications, benefits, and limitations of deep learning vis-\`a-vis physical robotic systems, using contemporary research as exemplars. It is intended to communicate recent advances to the wider robotics community and inspire additional interest in and application of deep learning in robotics.
ROC graphs with instance-varying costs

Receiver Operating Characteristics (ROC) graphs are a useful technique for organizing classifiers and visualizing their performance. ROC graphs have been used in cost-sensitive learning because of the ease with which class skew and error cost information can be applied to them to yield cost-sensitive decisions. However, they have been criticized because of their inability to handle instance-varying costs; that is, domains in which error costs vary from one instance to another. This paper presents and investigates a technique for adapting ROC graphs for use with domains in which misclassification costs vary within the instance population.
Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks

We study the problem of learning generative models of 3D shapes. Voxels or 3D parts have been widely used as the underlying representations to build complex 3D shapes, however, voxel-based representations suffer from high memory requirements, and parts-based models require a large collection of cached or richly parametrized parts. We take an alternative approach: learning a generative model over multi-view depth maps or their corresponding silhouettes, and using a deterministic rendering function to produce 3D shapes from these images. A multi-view representation of shapes enables generation of 3D models with fine details, as 2D depth maps and silhouettes can be modeled at a much higher resolution than 3D voxels. Moreover, our approach naturally brings the ability to recover the underlying 3D representation from depth maps of one or a few viewpoints. Experiments show that our framework can generate 3D shapes with variations and details. We also demonstrate that our model has out-of-sample generalization power for real-world tasks with occluded objects.
Reinforcement Learning for Relation Classification From Noisy Data

Existing relation classification methods that rely on distant supervision assume that a bag of sentences mentioning an entity pair are all describing a relation for the entity pair. Such methods, performing classification at the bag level, cannot identify the mapping between a relation and a sentence, and largely suffers from the noisy labeling problem. In this paper, we propose a novel model for relation classification at the sentence level from noisy data. The model has two modules: an instance selector and a relation classifier. The instance selector chooses high-quality sentences with reinforcement learning and feeds the selected sentences into the relation classifier, and the relation classifier makes sentencelevel prediction and provides rewards to the instance selector. The two modules are trained jointly to optimize the instance selection and relation classification processes. Experiment results show that our model can deal with the noise of data effectively and obtains better performance for relation classification at the sentence level.
Word and World Order: Semantic, Phonological, and Metrical Determinants of Serial Position

Animacy, word length, and prosody have all been accorded prominent roles in explanations for word order variations in language use. We examined the sequencing effects of these factors in two types of tasks. In recall tasks designed to simulate language production, we found selective effects of animacy. Animate nouns tended to appear as subjects in transitive sentences, but showed no special affinity for initial position in conjunctions within sentences, but showed no special affinity for initial position in conjunctions within sentences, suggesting a stronger involvement of animacy in grammatical role assignment than in word ordering. Word length had no significant impact: Shorter words did not appear earlier than longer words within sentences or within isolated conjunctions of nouns. Prosody had a weak effect on word order in isolated conjunctions, favoring sequences with alternating rhythm, but only in the absence of an animacy contrast. These results tend to confirm a hypothesized role for conceptual (meaning-based) accessibility in grammatical role assignment and to disconfirm a hypothesized role for lexical (form-based) accessibility in word ordering. In a judgment task, forms with animate nouns early were preferred across all constructions, and forms with short words early were often preferred both in sentences and in conjunctions. The findings suggest a possible asymmetry between comprehension and production in functional accounts of word order variations.