nils-herrmann/clean_scirepeval huggingface dataset:
Narcissism at the crossroads: phenotypic description of pathological narcissism across clinical theory, social/personality psychology, and psychiatric diagnosis.

This review documents two themes of emphasis found in phenotypic descriptions of pathological narcissism across clinical theory, social/personality psychology, and psychiatric diagnosis. Clinical theories of narcissism spanning 35 years consistently describe variations in the expression of pathological narcissism that emphasize either grandiosity or vulnerable affects and self-states. Recent research in social/personality psychology examining the structure of narcissistic personality traits consistently finds two broad factors representing Grandiosity-Exhibitionism and Vulnerability-Sensitivity-Depletion respectively. However, the majority of psychiatric criteria for narcissistic personality disorder (NPD) in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; American Psychiatric Association, 1994) emphasize expressions of grandiosity. By placing most of the diagnostic emphasis on overt grandiosity, DSM NPD has been limited by poor discriminant validity, modest levels of temporal stability, and the lowest prevalence rate on Axis II. Despite converging support for two phenotypic themes associated with pathological narcissism, psychiatric diagnosis and social/personality psychology research often focus only on grandiosity in the assessment of narcissism. In contrast, clinical theory struggles with a proliferation of labels describing these broad phenotypic variations. We conclude that the construct of pathological narcissism is at a crossroads and provide recommendations for diagnostic assessment, clinical conceptualization, and future research that could lead to a more integrated understanding of narcissistic personality and narcissistic personality pathology.
On the reliable detection of concept drift from streaming unlabeled data

Classifiers deployed in the real world operate in a dynamic environment, where the data distribution can change over time. These changes, referred to as concept drift, can cause the predictive performance of the classifier to drop over time, thereby making it obsolete. To be of any real use, these classifiers need to detect drifts and be able to adapt to them, over time. Detecting drifts has traditionally been approached as a supervised task, with labeled data constantly being used for validating the learned model. Although effective in detecting drifts, these techniques are impractical, as labeling is a difficult, costly and time consuming activity. On the other hand, unsupervised change detection techniques are unreliable, as they produce a large number of false alarms. The inefficacy of the unsupervised techniques stems from the exclusion of the characteristics of the learned classifier, from the detection process. In this paper, we propose the Margin Density Drift Detection (MD3) algorithm, which tracks the number of samples in the uncertainty region of a classifier, as a metric to detect drift. The MD3 algorithm is a distribution independent, application independent, model independent, unsupervised and incremental algorithm for reliably detecting drifts from data streams. Experimental evaluation on 6 drift induced datasets and 4 additional datasets from the cybersecurity domain demonstrates that the MD3 approach can reliably detect drifts, with significantly fewer false alarms compared to unsupervised feature based drift detectors. At the same time, it produces performance comparable to that of a fully labeled drift detector. The reduced false alarms enables the signaling of drifts only when they are most likely to affect classification performance. As such, the MD3 approach leads to a detection scheme which is credible, label efficient and general in its applicability.
Active cleaning of label noise

Mislabeled examples in the training data can severely affect the performance of supervised classifiers. In this paper, we present an approach to remove any mislabeled examples in the dataset by selecting suspicious examples as targets for inspection. We show that the large margin and soft margin principles used in support vector machines (SVM) have the characteristic of capturing the mislabeled examples as support vectors. Experimental results on two character recognition datasets show that one-class and two-class SVMs are able to capture around 85% and 99% of label noise examples, respectively, as their support vectors. We propose another new method that iteratively builds two-class SVM classifiers on the non-support vector examples from the training data followed by an expert manually verifying the support vectors based on their classification score to identify any mislabeled examples. We show that this method reduces the number of examples to be reviewed, as well as providing parameter independence of this method, through experimental results on four data sets. So, by (re-)examining the labels of the selective support vectors, most noise can be removed. This can be quite advantageous when rapidly building a labeled data set. & 2015
AskNow: A Framework for Natural Language Query Formalization in SPARQL

Natural Language Query Formalization involves semantically parsing queries in natural language and translating them into their corresponding formal representations. It is a key component for developing question-answering (QA) systems on RDF data. The chosen formal representation language in this case is often SPARQL. In this paper, we propose a framework, called AskNow, where users can pose queries in English to a target RDF knowledge base (e.g. DBpedia), which are first normalized into an intermediary canonical syntactic form, called Normalized Query Structure (NQS), and then translated into SPARQL queries. NQS facilitates the identification of the desire (or expected output information) and the user-provided input information, and establishing their mutual semantic relationship. At the same time, it is sufficiently adaptive to query paraphrasing. We have empirically evaluated the framework with respect to the syntactic robustness of NQS and semantic accuracy of the SPARQL translator on standard benchmark datasets.
The role of BPR in the implementation of ERP systems

The role of business process re-engineering (BPR) in implementing enterprise resource planning (ERP) systems is of paramount importance. A variety of approaches are used for such re-engineering as the best-fitting ERP solution can only give a maximum of 80 percent fit with the existing workflow of the organization in which ERP is being implemented. The aim of this paper is to focus on the issues involved.  The paper presents some sample ERP installations to come out with various types of business BPR, ranging from small-r to big-R, practised while implementing ERP.  Based on the literature review, it could be found that all organizations implementing ERP have chosen their own approach based on organizational constraints and the needs. The importance of BPR in ERP implementation is highlighted in earlier studies.  The recursive relationship between BPR and information technology has only resulted in the rapid evolution of ERP systems. ERP integration with supply chain management and its web readiness are major agenda for ERP developers. Many re-engineering possibilities are yet to evolve based on research in knowledge, artificial intelligence and expert database systems. A suitable approach to implement the same in an organization can only be done using the most suitable approach and hence the importance of the BPR approaches presented here along with their advantages and disadvantages.  As the various BPR approaches are categorized based on implementation followed by major corporations, it is definitely a forerunner for any ERP-implementing organization under study. Also the common problems encountered during re-engineering for ERP implementation and the suggested remedial measures are presented based on earlier work.  An attempt has been made to identify BPR problems, causes and approaches used in implementing ERP solutions. It is only aimed at synthesis of technical and general issues using the literature.
Phylogeny and cryptic diversification in Southeast Asian flying geckos.

The closed-canopy forests of Southeast Asia are home to an impressive number of vertebrates that have independently evolved morphologies that enhance directed aerial descent (gliding, parachuting). These assemblages include numerous mammal, frog, snake, and lizard clades. Several genera of gekkonid lizards, in particular, have evolved specialized structures such as cutaneous expansions, flaps, and midbody patagia, that enhance lift generation in the context of unique gliding and parachuting locomotion. The genus Ptychozoon represents arguably the most morphologically extreme, highly specialized clade of gliding geckos. Despite their notoriety and celebrated locomotor ability, members of the genus Ptychozoon have never been the subject of a species-level molecular phylogenetic analysis. In this paper, we utilize molecular sequence data from mitochondrial and nuclear gene fragments to estimate the evolutionary relationships of this unique group of flying geckos. Capitalizing on the recent availability of genetic samples for even the rarest of known species, we include the majority of known taxa and use model-based phylogenetic methods to reconstruct their evolutionary history. Because one species, P. kuhli, exhibits an unusually wide distribution coupled with an impressive range of morphological variation, we additionally use intensive phylogeographic/population genetic sampling, phylogenetic network analyses, and Bayesian species delimitation procedures to evaluate this taxon for the possible presence of cryptic evolutionary lineages. Our results suggest that P. kuhli may consist of between five and nine unrecognized, distinct species. Although we do not elevate these lineages to species status here, our findings suggest that lineage diversity in Ptychozoon is likely dramatically underestimated.
Helix: Accelerating Human-in-the-loop Machine Learning

Data application developers and data scientists spend an inordinate amount of time iterating on machine learning (ML) workflows— by modifying the data pre-processing, model training, and postprocessing steps—via trial-and-error to achieve the desired model performance. Existing work on accelerating machine learning focuses on speeding up one-shot execution of workflows, failing to address the incremental and dynamic nature of typical ML development. We propose HELIX, a declarative machine learning system that accelerates iterative development by optimizing workflow execution end-to-end and across iterations. HELIX minimizes the runtime per iteration via program analysis and intelligent reuse of previous results, which are selectively materialized—trading off the cost of materialization for potential future benefits—to speed up future iterations. Additionally, HELIX offers a graphical interface to visualize workflow DAGs and compare versions to facilitate iterative development. Through two ML applications, in classification and in structured prediction, attendees will experience the succinctness of HELIX’s programming interface and the speed and ease of iterative development using HELIX. In our evaluations, HELIX achieved up to an order of magnitude reduction in cumulative run time compared to state-of-the-art machine learning tools. PVLDB Reference Format: Doris Xin, Litian Ma, Jialin Liu, Stephen Macke, Shuchen Song, Aditya Parameswaran.  Accelerating Human-in-the-loop Machine Learning. PVLDB, 11 (12): 1958 1961, 2018.
2 . 3 Van Heerden ’ s network attack taxonomy

In the past few years network security threats have increased significantly. Methods for attacks have not only grown in diversity but also became more sophisticated. The increased need for security mechanisms and countermeasures requires a comprehensive understanding of those attacks and their characteristics. To organize the knowledge of attacks a large variety of classifications were proposed in form of taxonomies and ontologies. The development of these classifications has emerged as an effective means for developing awareness systems and creating common descriptive languages. However, due to the high diversity of attacks no standard classification of network attacks exists so far. In this paper, a survey of existing attack taxonomies and ontologies is presented to create an overview of conducted work in this field of research. Furthermore, issues and drawbacks are discussed in a comparative analysis. The conducted survey has shown the need of a flexible, standardized classification of attacks and vulnerabilities to enable efficient knowledge sharing among the scientific community.
IoT network protocols comparison for the purpose of IoT constrained networks

In this paper, a comparison of Internet of Things protocols used for data transfer in Internet of Things constrained networks is presented. Setting up such a network with a large number of physical interconnected IoT devices can be a challenge. In the IoT world, one of the key challenges is to efficiently support M2M communication in constrained networks. This can be achieved using MQTT (Message Queuing Telemetry Transport) and CoAP (Constrained Application Protocol) protocols. Choosing the appropriate protocol can be difficult while developing IoT application. There are several conditions that need to be considered while determining which protocol should be used. In this paper, we will evaluate performance and compare these protocols through different scenarios.
Spoofing 2D face recognition systems with 3D masks

Vulnerability to spoofing attacks is a serious drawback for many biometric systems. Among all biometric traits, face is the one that is exposed to the most serious threat, since it is exceptionally easy to access. The limited work on fraud detection capabilities for face mainly shapes around 2D attacks forged by displaying printed photos or replaying recorded videos on mobile devices. A significant portion of this work is based on the flatness of the facial surface in front of the sensor. In this study, we complicate the spoofing problem further by introducing the 3rd dimension and examine possible 3D attack instruments. A small database is constructed with six different types of 3D facial masks and experimented on to determine the right direction to study 3D attacks. Spoofing performance for each type of mask is assessed and analysed thoroughly using two Gabor-wavelet-based algorithms.
Activity Recognition with Smartphone Sensors Second Exam Literature Review

The ubiquity of smartphones together with their ever-growing computing, networking and sensing powers have been changing the landscape of people’s daily life. Among others, activity recoginition, which takes the raw sensor reading as inputs and predicts a user’s motion activity, has become an active research area in recent years. It is the core building block in many high-impact applications, ranging from health and fitness monitoring, personal biometric signature, urban computing, assistive technology and elder-care, to indoor localization and navigation, etc. This paper presents a comprehensive survey of the recent advances in activity recognition with smartphones’ sensors. We start with the basic concepts such as sensors, activity types, etc. We review the core data mining techniques behind the main stream activity recognition algorithms, analyze their major challenges and introduce a variety of real applications enabled by activity recognition. Professor: Dr. Hanghang Tong Signature: Date: 06-12-2014 Professor: Dr. Ping Ji Signature: Date: 06-12-2014 Professor: Dr. Ted Brown Signature: Date: 06-12-2014 Advisor: Dr. Hanghang Tong
AppIntent: analyzing sensitive data transmission in android for privacy leakage detection

Android phones often carry personal information, attracting malicious developers to embed code in Android applications to steal sensitive data. With known techniques in the literature, one may easily determine if sensitive data is being transmitted out of an Android phone. However, transmission of sensitive data in itself does not necessarily indicate privacy leakage; a better indicator may be whether the transmission is by user intention or not. When transmission is not intended by the user, it is more likely a privacy leakage. The problem is how to determine if transmission is user intended. As a first solution in this space, we present a new analysis framework called AppIntent. For each data transmission, AppIntent can efficiently provide a sequence of GUI manipulations corresponding to the sequence of events that lead to the data transmission, thus helping an analyst to determine if the data transmission is user intended or not. The basic idea is to use symbolic execution to generate the aforementioned event sequence, but straightforward symbolic execution proves to be too time-consuming to be practical. A major innovation in AppIntent is to leverage the unique Android execution model to reduce the search space without sacrificing code coverage. We also present an evaluation of AppIntent with a set of 750 malicious apps, as well as 1,000 top free apps from Google Play. The results show that AppIntent can effectively help separate the apps that truly leak user privacy from those that do not.
Linggle: a Web-scale Linguistic Search Engine for Words in Context

In this paper, we introduce a Web-scale linguistics search engine, Linggle, that retrieves lexical bundles in response to a given query. The query might contain keywords, wildcards, wild parts of speech (PoS), synonyms, and additional regular expression (RE) operators. In our approach, we incorporate inverted file indexing, PoS information from BNC, and semantic indexing based on Latent Dirichlet Allocation with Google Web 1T. The method involves parsing the query to transforming it into several keyword retrieval commands. Word chunks are retrieved with counts, further filtering the chunks with the query as a RE, and finally displaying the results according to the counts, similarities, and topics. Clusters of synonyms or conceptually related words are also provided. In addition, Linggle provides example sentences from The New York Times on demand. The current implementation of Linggle is the most functionally comprehensive, and is in principle language and dataset independent. We plan to extend Linggle to provide fast and convenient access to a wealth of linguistic information embodied in Web scale datasets including Google Web 1T and Google Books Ngram for many major languages in the world.
Abstractive Cross-Language Summarization via Translation Model Enhanced Predicate Argument Structure Fusing

Cross-language multidocument summarization is the task to generate a summary in a target language e.g., Chinese from a collection of documents in a different source language e.g., English. Previous methods such as the extractive and compressive algorithms focus only on single sentence selection and compression, which cannot make full use of the similar sentences containing complementary information. Furthermore, the translation model knowledge is not fully explored in previous approaches. To address these two problems, we propose in this paper an abstractive cross-language summarization framework. First, the source language documents are translated into target language with a machine translation system. Then, the method constructs a pool of bilingual concepts and facts represented by the bilingual elements of the source-side predicate-argument structures PAS and their target-side counterparts. Finally, new summary sentences are produced by fusing bilingual PAS elements with the integer linear programming algorithm to maximize both of the salience and translation quality of the PAS elements. The experimental results on English-to-Chinese cross-language summarization demonstrate that our proposed method outperforms the state-of-the-art extractive systems in both automatic and manual evaluations.
Using hybrid data mining and machine learning clustering analysis to predict the turnover rate for technology professionals

This study applies clustering analysis for data mining and machine learning to predict trends in technology professional turnover rates, including the hybrid artificial neural network and clustering analysis known as the self-organizing map (SOM). This hybrid clustering method was used to study the individual characteristics of turnover trend clusters. Using a transaction questionnaire, we studied the period of peak turnover, which occurs after the Chinese New Year, for individuals divided into various age groups. The turnover trend of technology professionals was examined in well-known Taiwanese companies. The results indicate that the high outstanding turnover trend circle was primarily caused by a lack of inner fidelity identification, leadership and management. Based on cross-verification, the clustering accuracy rate was 92.7%. This study addressed problems related to the rapid loss of key human resources and should help organizations learn how to enhance competitiveness and efficiency.
The Cambridge Face Tracker: Accurate, Low Cost Measurement of Head Posture Using Computer Vision and Face Recognition Software

We validate a video-based method of head posture measurement.



The Cambridge Face Tracker uses neural networks (constrained local neural fields) to recognize facial features in video. The relative position of these facial features is used to calculate head posture. First, we assess the accuracy of this approach against videos in three research databases where each frame is tagged with a precisely measured head posture. Second, we compare our method to a commercially available mechanical device, the Cervical Range of Motion device: four subjects each adopted 43 distinct head postures that were measured using both methods.



The Cambridge Face Tracker achieved confident facial recognition in 92% of the approximately 38,000 frames of video from the three databases. The respective mean error in absolute head posture was 3.34°, 3.86°, and 2.81°, with a median error of 1.97°, 2.16°, and 1.96°. The accuracy decreased with more extreme head posture. Comparing The Cambridge Face Tracker to the Cervical Range of Motion Device gave correlation coefficients of 0.99 (P < 0.0001), 0.96 (P < 0.0001), and 0.99 (P < 0.0001) for yaw, pitch, and roll, respectively.



The Cambridge Face Tracker performs well under real-world conditions and within the range of normally-encountered head posture. It allows useful quantification of head posture in real time or from precaptured video. Its performance is similar to that of a clinically validated mechanical device. It has significant advantages over other approaches in that subjects do not need to wear any apparatus, and it requires only low cost, easy-to-setup consumer electronics.



Noncontact assessment of head posture allows more complete clinical assessment of patients, and could benefit surgical planning in future.