nils-herrmann/clean_scirepeval huggingface dataset:
Affirmation of personal values buffers neuroendocrine and psychological stress responses.

Stress is implicated in the development and progression of a broad array of mental and physical health disorders. Theory and research on the self suggest that self-affirming activities may buffer these adverse effects. This study experimentally investigated whether affirmations of personal values attenuate physiological and psychological stress responses. Eighty-five participants completed either a value-affirmation task or a control task prior to participating in a laboratory stress challenge. Participants who affirmed their values had significantly lower cortisol responses to stress, compared with control participants. Dispositional self-resources (e.g., trait self-esteem and optimism) moderated the relation between value affirmation and psychological stress responses, such that participants who had high self-resources and had affirmed personal values reported the least stress. These findings suggest that reflecting on personal values can keep neuroendocrine and psychological responses to stress at low levels. Implications for research on the self, stress processes, health, and interventions are discussed.
Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation

Sharp, T., Keskin, C., Robertson, D., Taylor, J., Shotton, J., Leichter, D.K.C.R.I., Wei, A.V.Y., Krupka, D.F.P.K.E., Fitzgibbon, A., Izadi, S.: Accurate, robust, and flexible real-time hand tracking.  (2015)  Oberweger, M., Wohlhart, P., Lepetit, V.: Training a feedback loop for hand pose estimation.  (2015)  Oberweger, M., Wohlhart, P., Lepetit, V.: Hands deep in deep learning for hand pose estimation.  (2015)  Tang, D., Taylor, J., Kohli, P., Keskin, C., Kim, T.K., Shotton,  Opening the black box: Hierarchical sampling optimization for estimating human hand pose.  (2015) * Indicates equal contribution.
Modeling in OWL 2 without Restrictions

The Semantic Web ontology language OWL 2 DL comes with a variety of language features that enable sophisticated and practically useful modeling. However, the use of these features has been severely restricted in order to retain decidability of the language. For example, OWL 2 DL does not allow a property to be both transitive and asymmetric, which would be desirable, e.g., for representing an ancestor relation. In this paper, we argue that the so-called “global restrictions” of OWL 2 DL preclude many useful forms of modeling, by providing a catalog of basic modeling patterns that would be available in OWL 2 DL if the global restrictions were discarded. We then report on the results of evaluating several state-of-the-art OWL 2 DL reasoners on problems that use combinations of features in a way that the global restrictions are violated. The systems turn out to rely heavily on the global restrictions and are thus largely incapable of coping with the modeling patterns. Next we show how off-the-shelf first-order logic theorem proving technology can be used to perform reasoning in the OWL 2 direct semantics, the semantics that underlies OWL 2 DL, but without requiring the global restrictions. Applying a naive proof-of-concept implementation of this approach to the test problems was successful in all cases. Based on our observations, we make suggestions for future lines of research on expressive description logic-style OWL reasoning.
A Social Curiosity Inspired Recommendation Model to Improve Precision, Coverage and Diversity

With the prevalence of social networks, social recommendation is rapidly gaining popularity. Currently, social information has mainly been utilized for enhancing rating prediction accuracy, which may not be enough to satisfy user needs. Items with high prediction accuracy tend to be the ones that users are familiar with and may not interest them to explore. In this paper, we take a psychologically inspired view to recommend items that will interest users based on the theory of social curiosity and study its impact on important dimensions of recommender systems. We propose a social curiosity inspired recommendation model which combines both user preferences and user curiosity. The proposed recommendation model is evaluated using large scale real world datasets and the experimental results demonstrate that the inclusion of social curiosity significantly improves recommendation precision, coverage and diversity.
The clinical characteristics of dementia with Lewy bodies and a consideration of prodromal diagnosis

Dementia with Lewy bodies (DLB) is the second most common type of degenerative dementia following Alzheimer’s disease (AD). DLB is clinically and pathologically related to Parkinson's disease (PD) and PD dementia, and the three disorders can be viewed as existing on a spectrum of Lewy body disease. In recent years there has been a concerted effort to establish the phenotypes of AD and PD in the prodromal phase (before the respective syndromes of cognitive and motor impairment are expressed). Evidence for the prodromal presentation of DLB is also emerging. This paper briefly reviews what is known about the clinical presentation of prodromal DLB before discussing the pathology of Lewy body disease and how this relates to potential biomarkers of prodromal DLB. The presenting features of DLB can be broadly placed in three categories: cognitive impairment (particularly nonamnestic cognitive impairments), behavioural/psychiatric phenomena (for example, hallucinations, rapid eye movement sleep behaviour disorder (RBD)) and physical symptoms (for example, parkinsonism, decreased sense of smell, autonomic dysfunction). Some noncognitive symptoms such as constipation, RBD, hyposmia and postural dizziness can predate the onset of memory impairment by several years in DLB. Pathological studies of Lewy body disease have found that the earliest sites of involvement are the olfactory bulb, the dorsal motor nucleus of the vagal nerve, the peripheral autonomic nervous system, including the enteric nervous system, and the brainstem. Some of the most promising early markers for DLB include the presence of RBD, autonomic dysfunction or hyposmia, 123I-metaiodobenzylguanidine cardiac scintigraphy, measures of substantia nigra pathology and skin biopsy for α-synuclein in peripheral autonomic nerves. In the absence of disease-modifying therapies, the diagnosis of prodromal DLB is of limited use in the clinic. That said, knowledge of the prodromal development of DLB could help clinicians identify cases of DLB where the diagnosis is uncertain. Prodromal diagnosis is of great importance in research, where identifying Lewy body disease at an earlier stage may allow researchers to investigate the initial phases of dementia pathophysiology, develop treatments designed to interrupt the development of the dementia syndrome and accurately identify the patients most likely to benefit from these treatments.
PV-OWL — Pharmacovigilance surveillance through semantic web-based platform for continuous and integrated monitoring of drug-related adverse effects in open data sources and social media

The recent EU regulation on Pharmacovigilance [Regulation (EU) 1235/2010, Directive 2010/84/EU] imposes both to Pharmaceutical companies and Public health agencies to maintain updated safety information of drugs, monitoring all available data sources. Here, we present our project aiming to develop a web platform for continuous monitoring of adverse effects of medicines (pharmacovigilance), by integrating information from public databases, scientific literature and social media. The project will start by scanning all available data sources concerning drug adverse events, both open (e.g., FAERS — FDA Adverse Event Reporting Systems, medical literature, social media, etc.) and proprietary data (e.g., discharge hospital records, drug prescription archives, electronic health records), that require agreement with respective data owners. Subsequent, pharmacovigilance experts will perform a semi-automatic mapping of codes identifying drugs and adverse events, to build the thesaurus of the web based platform. After these preliminary activities, signal generation and prioritization will be the core of the project. This task will result in risk confidence scores for each included data source and a comprehensive global score, indicating the possible association between a specific drug and an adverse event. The software framework MOMIS, an open source data integration system, will allow semi-automatic virtual integration of heterogeneous and distributed data sources. A web platform, based on MOMIS, able to merge many heterogeneous data sets concerning adverse events will be developed. The platform will be tested by external specialized subjects (clinical researchers, public or private employees in pharmacovigilance field). The project will provide a) an innovative way to link, for the first time in Italy, different databases to obtain novel safety indicators; b) a web platform for a fast and easy integration of all available data, useful to verify and validate hypothesis generated in signal detection. Finally, the development of the unified safety indicator (global risk score) will result in a compelling, easy-to-understand, visual format for a broad range of professional and not professional users like patients, regulatory authorities, clinicians, lawyers, human scientists.
Storekeeper: A Security-Enhanced Cloud Storage Aggregation Service

Cloud storage services are currently a commodity that allows users to store data persistently, access the data from everywhere, and share it with friends or co-workers. However, due to the proliferation of cloud storage accounts and lack of interoperability between cloud services, managing and sharing cloud-hosted files is a nightmare for many users. To address this problem, specialized cloud aggregator systems emerged that provide users a global view of all files in their accounts and enable file sharing between users from different clouds. Such systems, however, have limited security: not only they fail to provide end-to-end privacy from cloud providers, but they require users to grant full access privileges to individual cloud storage accounts. In this paper, we present Storekeeper, a privacy-preserving cloud aggregation service that enables file sharing on multi-user multi-cloud storage platforms while preserving data confidentiality from cloud providers and from the cloud aggregator service. To provide this property, Storekeeper decentralizes most of the cloud aggregation logic to the client side enabling security sensitive functions to be performed only on the trusted client endpoints. This decentralization brings new challenges related with file update propagation, access control, user authentication, and key management that are addressed by Storekeeper. This is provided at a low cost (7% on average) when compared with the underlining cloud providers.
Understanding the Effects of Violent Video Games on Violent Crime

Psychological studies invariably find a positive relationship between violent video game play and aggression. However, these studies cannot account for either aggressive effects of alternative activities video game playing substitutes for or the possible selection of relatively violent people into playing violent video games. That is, they lack external validity. We investigate the relationship between the prevalence of violent video games and violent crimes. Our results are consistent with two opposing effects. First, they support the behavioral effects as in the psychological studies. Second, they suggest a larger voluntary incapacitation effect in which playing either violent or non-violent games decrease crimes. Overall, violent video games lead to decreases in violent crime.
A Case for Hardware Protection of Guest VMs from Compromised Hypervisors in Cloud Computing

Cloud computing, enabled by virtualization technologies, is becoming a mainstream computing model. Many companies are starting to utilize the infrastructure-as-a-service (IaaS) cloud computing model, leasing guest virtual machines (VMs) from the infrastructure providers for economic reasons: to reduce their operating costs and to increase the flexibility of their own infrastructures. Yet, many companies may be hesitant to move to cloud computing due to security concerns. An integral part of any virtualization technology is the all-powerful hyper visor. A hyper visor is a system management software layer which can access all resources of the platform. Much research has been done on using hyper visors to monitor guest VMs for malicious code and on hardening hyper visors to make them more secure. There is, however, another threat which has not been addressed by researchers -- that of compromised or malicious hyper visors that can extract sensitive or confidential data from guest VMs. Consequently, we propose that a new research direction needs to be undertaken to tackle this threat. We further propose that new hardware mechanisms in the multi core microprocessors are a viable way of providing protections for the guest VMs from the hyper visor, while still allowing the hyper visor to flexibly manage the resources of the physical platform.
Unsupervised Pattern Discovery in Speech

We present a novel approach to speech processing based on the principle of pattern discovery. Our work represents a departure from traditional models of speech recognition, where the end goal is to classify speech into categories defined by a prespecified inventory of lexical units (i.e., phones or words). Instead, we attempt to discover such an inventory in an unsupervised manner by exploiting the structure of repeating patterns within the speech signal. We show how pattern discovery can be used to automatically acquire lexical entities directly from an untranscribed audio stream. Our approach to unsupervised word acquisition utilizes a segmental variant of a widely used dynamic programming technique, which allows us to find matching acoustic patterns between spoken utterances. By aggregating information about these matching patterns across audio streams, we demonstrate how to group similar acoustic sequences together to form clusters corresponding to lexical entities such as words and short multiword phrases. On a corpus of academic lecture material, we demonstrate that clusters found using this technique exhibit high purity and that many of the corresponding lexical identities are relevant to the underlying audio stream.
Cloud-based adaptive compression and secure management services for 3D healthcare data

Several studies show that the lack of access to resources and shared data is one of the main causes of errors in the healthcare sector. In particular, 3D medical images play a fundamental role in healthcare environment, but they are typically very large in size. Therefore, their management, which should be performed also by means of devices with limited characteristics, requires complex network protocols along with advanced compression and security techniques. This work concerns the secure management of 3D medical images, with themain aim that suchmanagementmust take place in an almost completely transparent manner for the end-user, regardless of the computational and networking capabilities hemay use. In particular, our contribution is twofold: first, we propose an engine for lossless dynamic and adaptive compression of 3Dmedical images,which also allows the embedding of securitywatermarkswithin them. Furthermore, in order to provide effective, secure and flexible access to healthcare resources that need to be managed by medical applications, we define the architecture of a SaaS Cloud system, which is based on the aforementioned engine. The resulting architecture allows devices with totally different and heterogeneous hardware and software characteristics to interact among them, so that these differences are almost completely transparent to the end-user.
A Study of Different Approaches to Aspect-based Opinion Mining

In recent years, Opinion mining has been an active research area in Text mining and analysis, natural language processing. Opinion mining is the computational study of people’s opinion expressed in written language or text towards entities and their aspects. With the growth of internet, social networking sites, blogs, discussion forums, e-commerce websites have gained a tremendous importance and have provided platform for people to express and share their opinion on entities and their aspects. As opinionated web content is increasing rapidly in the form of reviews, comments, blogs, status updates, tweets, etc. it is practically impossible for people or organization to analyze all opinions at a time to make good decisions. Hence, there is a need for effective automated system to evaluate opinions and generate accurate results. This paper describes opinion mining and focuses on the sub topic aspect-based opinion mining, tasks in aspect-based opinion mining, current state-of-the-art methods used for aspect-based opinion mining, advantages and disadvantages of these methods and latest research challenges in aspect-based opinion mining. Our experimental results based on some of the aspect extraction techniques, gives an idea of which aspect extraction techniques are efficient
Fast restoration of natural images corrupted by high-density impulse noise

In this paper, we suggest a general model for the fixed-valued impulse noise and propose a two-stage method for high density noise suppression while preserving the image details. In the first stage, we apply an iterative impulse detector, exploiting the image entropy, to identify the corrupted pixels and then employ an Adaptive Iterative Mean filter to restore them. The filter is adaptive in terms of the number of iterations, which is different for each noisy pixel, according to the Euclidean distance from the nearest uncorrupted pixel. Experimental results show that the proposed filter is fast and outperforms the best existing techniques in both objective and subjective performance measures.
A Bayesian LSTM Model to Evaluate the Effects of Air Pollution Control Regulations in China

Rapid socio-economic development and urbanization have resulted in serious deterioration in air-quality in many world cities, including Beijing, China. This preliminary study is the first attempt to examine the effectiveness of air pollution control regulations implemented in Beijing during 2013 – 2017 through a data-driven regulatory intervention analysis. Our proposed machine-learning model utilizes proxy data including Aerosol Optical Depth (AOD) and meteorology; it can explain 80% of the PM2.5 variability. Our preliminary results show that air pollution control regulatory measures introduced in China and Beijing have reduced PM2.5 pollution in Beijing by 23% on average.
Workload and Delay Analysis in Manufacturing Process Using Process Mining

Process analysis is one of the important topics in manufacturing industry. Recently process mining has been applied to analyze manufacturing processes. In this paper we investigate the characteristics of event logs in make-toorder production and propose a method to analyze manufacturing processes in make-to-order production such as construction, shipbuilding and aviation by utilizing and extending existing process mining techniques. Among three major analysis perspectives in process mining such as process discovery, performance analysis, and conformance checking, this paper focuses on the performance analysis including workload analysis and delay analysis. To validate the proposed method, a case study with real data is conducted.
Real time lane detection for autonomous vehicles

An increasing safety and reducing road accidents, thereby saving lives are one of great interest in the context of Advanced Driver Assistance Systems. Apparently, among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. It is based on lane detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehiclepsilas heading direction). One of the principal approaches to detect road boundaries and lanes using vision system on the vehicle. However, lane detection is a difficult problem because of the varying road conditions that one can encounter while driving. In this paper, a vision-based lane detection approach capable of reaching real time operation with robustness to lighting change and shadows is presented. The system acquires the front view using a camera mounted on the vehicle then applying few processes in order to detect the lanes. Using a pair of hyperbolas which are fitting to the edges of the lane, those lanes are extracted using Hough transform. The proposed lane detection system can be applied on both painted and unpainted road as well as curved and straight road in different weather conditions. This approach was tested and the experimental results show that the proposed scheme was robust and fast enough for real time requirements. Eventually, a critical overview of the methods were discussed, their potential for future deployment were assist.
Wind-Aware Emergency Landing Assistant Based on Dubins Curves

A total engine failure poses a major threat to passengers as well as the aircraft and requires a fast decision by the pilot. We develop an assistant system to support the pilot in this decision process. An aircraft is able to glide a certain distance without thrust power by converting the potential energy of the altitude into distance. The objective of our work is to calculate an approach route which allows the aircraft to reach a suitable landing field at an appropriate altitude. This is a non-trivial problem because of many free parameters like wind direction and wind velocity. Our solution computes an approach route with two tangents and two co-rotating circular segments. For this purpose, valid approach routes can be calculated for many general cases. The method has a constant complexity and can dispense with iterative approaches. The route is calculated entirely in the wind frame which avoids complex calculations with trochoids. The central idea is to take the wind into account by moving the target towards the wind direction.
Central Bank Communication and the Yield Curve: A Semi-Automatic Approach using Non-Negative Matrix Factorization

Communication is now a standard tool in the central bank’s monetary policy toolkit. Theoretically, communication provides the central bank an opportunity to guide public expectations, and it has been shown empirically that central bank communication can lead to financial market fluctuations. However, there has been little research into which dimensions or topics of information are most important in causing these fluctuations. We develop a semi-automatic methodology that summarizes the FOMC statements into its main themes, automatically selects the best model based on coherency, and assesses whether there is a significant impact of these themes on the shape of the U.S Treasury yield curve using topic modeling methods from the machine learning literature. Our findings suggest that the FOMC statements can be decomposed into three topics: (i) information related to the economic conditions and the mandates, (ii) information related to monetary policy tools and intermediate targets, and (iii) information related to financial markets and the financial crisis. We find that statements are most influential during the financial crisis and the effects are mostly present in the curvature of the yield curve through information related to the financial theme.