nils-herrmann/clean_scirepeval huggingface dataset:
REAL TIME WIRELESS HEALTH MONITORING APPLICATION USING MOBILE DEVICES

In the last decade the healthcare monitoring systems have drawn considerable attentions of the researchers. The prime goal was to develop a reliable patient monitoring system so that the healthcare professionals can monitor their patients, who are either hospitalized or executing their normal daily life activities. In this work we present a mobile device based wireless healthcare monitoring system that can provide real time online information about physiological conditions of a patient. Our proposed system is designed to measure and monitor important physiological data of a patient in order to accurately describe the status of her/his health and fitness. In addition the proposed system is able to send alarming message about the patient’s critical health data by text messages or by email reports. By using the information contained in the text or e-mail message the healthcare professional can provide necessary medical advising. The system mainly consists of sensors, the data acquisition unit, microcontroller (i.e., Arduino), and software (i.e., LabVIEW). The patient’s temperature, heart beat rate, muscles, blood pressure, blood glucose level, and ECG data are monitored, displayed, and stored by our system. To ensure reliability and accuracy the proposed system has been field tested. The test results show that our system is able to measure the patient’s physiological data with a very high accuracy.
Bayesian Optimization Algorithm

In this paper an algorithm based on the concepts of genetic algorithms that uses an estimation of a probability distribution of promising solutions in order to generate new candidate solutions is proposed To esti mate the distribution techniques for model ing multivariate data by Bayesian networks are used The proposed algorithm identi es reproduces and mixes building blocks up to a speci ed order It is independent of the ordering of the variables in the strings rep resenting the solutions Moreover prior in formation about the problem can be incor porated into the algorithm However prior information is not essential Preliminary ex periments show that the BOA outperforms the simple genetic algorithm even on decom posable functions with tight building blocks as a problem size grows
Masquerade Detection Using Enriched Command Lines

A masquerade attack, in which one user impersonates another, is among the most serious forms of computer abuse, largely because such attacks are often mounted by insiders, and can be very difficult to detect. Automatic discovery of masqueraders is sometimes undertaken by detecting significant departures from normal user behavior, as represented by user profiles based on users’ command histories. A series of experiments performed by Schonlau et al.  achieved moderate success in masquerade detection based on a data set comprised of truncated command lines, i.e., single commands, stripped of any accompanying flags, arguments or elements of shell grammar such as pipes or semi-colons. Using the same data, Maxion and Townsend  improved on the Schonlau et al. results by 56%, raising the detection rate from 39.4% to 61.5% at false-alarm rates near 1%. The present paper extends this work by testing the hypothesis that a limitation of these approaches is the use of truncated command-line data, as opposed to command lines enriched with flags, shell grammar, arguments and information about aliases. Enriched command lines were found to facilitate correct detection at the 82% level, far exceeding previous results, with a corresponding 30% reduction in the overall cost of errors, and only a small increase in false alarms. Descriptions of pathological cases illustrate strengths and limitations of both the data and the detection algorithm.
BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

We introduce BinaryNet, a method which trains DNNs with binary weights and activations when computing parameters’ gradient. We show that it is possible to train a Multi Layer Perceptron (MLP) on MNIST and ConvNets on CIFAR-10 and SVHN with BinaryNet and achieve nearly state-of-the-art results. At run-time, BinaryNet drastically reduces memory usage and replaces most multiplications by 1-bit exclusive-not-or (XNOR) operations, which might have a big impact on both general-purpose and dedicated Deep Learning hardware. We wrote a binary matrix multiplication GPU kernel with which it is possible to run our MNIST MLP 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy. The code for BinaryNet is available.
Phrase-based Image Captioning

Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing. In this paper, we present a simple model that is able to generate descriptive sentences given a sample image. This model has a strong focus on the syntax of the descriptions. We train a purely bilinear model that learns a metric between an image representation (generated from a previously trained Convolutional Neural Network) and phrases that are used to described them. The system is then able to infer phrases from a given image sample. Based on caption syntax statistics, we propose a simple language model that can produce relevant descriptions for a given test image using the phrases inferred. Our approach, which is considerably simpler than state-of-the-art models, achieves comparable results in two popular datasets for the task: Flickr30k and the recently proposed Microsoft COCO.
Wordnet Enhanced Automatic Crossword Generation

We report on a system for automatically generating and displaying crosswords from a system manager supplied database of potential clues and corresponding words that index those clues. The system relies on the lexical relations encoded in WordNet to enhance the aesthetics of the resulting crossword by making it easier to automatically identify a grid that may be populated with words and clues that have a thematic focus. The system architecture is provided in overview, as is empirical evaluation.
Artistic reality: fast brush stroke stylization for augmented reality

The goal of augmented reality is to provide the user with a view of the surroundings enriched by virtual objects. Practically all augmented reality systems rely on standard real-time rendering methods for displaying graphical objects. Although such conventional computer graphics algorithms are fast, they often fail to produce sufficiently realistic renderings. Therefore, virtual models can easily be distinguished from the real environment. We have recently proposed a novel approach for generating augmented reality images . Our method is based on the idea of applying stylization techniques for adapting the visual realism of both the camera image and the virtual graphical objects. Since both the camera image and the virtual objects are stylized in a corresponding way, they appear very similar. Here, we present a new method for the stylization of augmented reality images. This approach generates a painterly brush stroke rendering. The resulting stylized augmented reality video frames look similar to paintings created in the pointillism style. We describe the implementation of the camera image filter and the non-photorealistic rendererfor virtual objects. These components have been newly designed or adapted for this purpose. They are fast enough for generating augmented reality images in near real-time (more than 14 frames per second).
Exploiting Heterogeneous Human Mobility Patterns for Intelligent Bus Routing

Optimal planning for public transportation is one of the keys to sustainable development and better quality of life in urban areas. Compared to private transportation, public transportation uses road space more efficiently and produces fewer accidents and emissions. In this paper, we focus on the identification and optimization of flawed bus routes to improve utilization efficiency of public transportation services, according to people's real demand for public transportation. To this end, we first provide an integrated mobility pattern analysis between the location traces of taxicabs and the mobility records in bus transactions. Based on mobility patterns, we propose a localized transportation mode choice model, with which we can accurately predict the bus travel demand for different bus routing. This model is then used for bus routing optimization which aims to convert as many people from private transportation to public transportation as possible given budget constraints on the bus route modification. We also leverage the model to identify region pairs with flawed bus routes, which are effectively optimized using our approach. To validate the effectiveness of the proposed methods, extensive studies are performed on real world data collected in Beijing which contains 19 million taxi trips and 10 million bus trips.
Data summaries for on-demand queries over linked data

Typical approaches for querying structured Web Data collect (crawl) and pre-process (index) large amounts of data in a central data repository before allowing for query answering. However, this time-consuming pre-processing phase however leverages the benefits of Linked Data -- where structured data is accessible live and up-to-date at distributed Web resources that may change constantly -- only to a limited degree, as query results can never be current. An ideal query answering system for Linked Data should return current answers in a reasonable amount of time, even on corpora as large as the Web. Query processors evaluating queries directly on the live sources require knowledge of the contents of data sources. In this paper, we develop and evaluate an approximate index structure summarising graph-structured content of sources adhering to Linked Data principles, provide an algorithm for answering conjunctive queries over Linked Data on theWeb exploiting the source summary, and evaluate the system using synthetically generated queries. The experimental results show that our lightweight index structure enables complete and up-to-date query results over Linked Data, while keeping the overhead for querying low and providing a satisfying source ranking at no additional cost.
Clustering Semantic Spaces of Suicide Notes and Newsgroups Articles

Historically, suicide risk assessment has relied on question-and-answer type tools. These tools, built on psychometric advances, are widely used because of availability. Yet there is no known tool based on biologic and cognitive evidence. This absence often cause a vexing clinical problem for clinicians who question the value of the result as time passes. The purpose of this paper is to describe one experiment in a series of experiments to develop a tool that combines Biological Markers ( Bm) with Thought Markers ( Tm), and use machine learning to compute a real-time index for assessing the likelihood repeated suicide attempt in the next six-months. For this study we focus using unsupervised machine learning to distinguish between actual suicide notes and newsgroups. This is important because it gives us insight into how well these methods discriminate between real notes and general conversation.
Automated Verification of Neural Networks: Advances, Challenges and Perspectives

Neural networks are one of the most investigated and widely used techniques in Machine Learning. In spite of their success, they still find limited application in safetyand security-related contexts, wherein assurance about networks’ performances must be provided. In the recent past, automated reasoning techniques have been proposed by several researchers to close the gap between neural networks and applications requiring formal guarantees about their behavior. In this work, we propose a primer of such techniques and a comprehensive categorization of existing approaches for the automated verification of neural networks. A discussion about current limitations and directions for future investigation is provided to foster research on this topic at the crossroads of Machine Learning and Automated Reasoning.
Conflict in Adult Close Relationships : An Attachment Perspective

Relationship researchers have focused on the frequency of conflict in couples' relationships and the manner in which couples engage in and try to resolve conflicts. conflict, under some conditions, may facilitate the development and maintenance of associated with patterns of behavior (e.g., negative affect reciprocity, demand-withdraw) and thought that tend to escalate conflict and make it more difficult to negotiate a facilitates intimacy or exacerbates distress may depend on individual differences in the way in which people interpret and respond to conflict. framework for understanding different responses to conflict. People are thought to differ in their working models of attachment, which include expectations, beliefs, and goals These working models are likely to shape people's thoughts, feelings, and behavior during conflict. For example, a person who expects close others to be generally responsive and available is likely to interpret and respond to conflict very differently from a person who expects close others to be rejecting and unavailable. Attachment theory may be able to inform the literature on 3 conflict in close relationships by suggesting how individuals might differ in how they construe conflict. At the same time, the study of relationship conflict provides a useful context for testing important aspects of attachment theory. Conflict may be particularly likely to reveal attachment processes because (a) it may act as a stressor on the relationship and thereby activate the attachment system (Simpson, Rholes, & Phillips, 1996), (b) it challenges partners' abilities to regulate their emotions and behavior (Kobak & Duemmler, 1994), which are thought to be connected to attachment processes and (c) it may trigger behaviors (e.g., personal disclosures) that typically promote intimacy, thereby providing evidence relevant to different attachment goals such as achieving intimacy or maintaining self-reliance (Pietromonaco & Feldman Barrett, 1997). In this chapter, we first discuss how conflict can be conceptualized within an attachment framework. Specifically, we propose that conflict may pose a threat to the attachment bond, but that it also may provide an opportunity for perceiving or experiencing greater intimacy. Furthermore, the degree to which people perceive conflict as a threat, opportunity, or both will depend on the content (e.g., expectations, beliefs, goals) of their working models of attachment. Next, we identify a set of predictions that follow from this framework, and evaluate extent to which empirical findings support these predictions; in particular, we attempt to integrate divergent findings in the empirical literature. Finally, we outline several critical …
Evaluation of a dynamic role-playing platform for simulations based on Octalysis gamification framework

The use of serious games (SG) in education and their pedagogical benefit is being widely recognized. However, effective integration of SGs in education depends on addressing two big challenges: successful incorporation of motivation and engagement that can lead to learning; and high cost and specialised skills associated with customised development to achieve the required pedagogical results. This paper presents a SG platform that offers tools to educators to dynamically create three dimensional (3D) scenes and verbal and non-verbal interaction with fully embodied conversational agents (ECA) that can be used to simulate numerous educational scenarios. We then evaluate the effectiveness of the platform in terms of supporting the creation of motivating and engaging educational simulations based on the Octalysis gamification framework. The latter includes a number of game design elements that can be used as appropriate in the optimization of the experience in each phase of the player’s journey. We conclude with directions of the further extension to our platform to address the aforementioned gamification framework, as well as directions for further development of the Octalysis framework.
Model Checking CSP Revisited: Introducing a Process Analysis Toolkit

FDR, initially introduced decades ago, is the de facto analyzer for Communicating Sequential Processes (CSP). Model checking techniques have been evolved rapidly since then. This paper describes PAT, i.e., a process analysis toolkit which complements FDR in several aspects. PAT is designed to analyze event-based compositional system models specified using CSP as well as shared variables and asynchronous message passing. It supports automated refinement checking, model checking of LTL extended with events, etc. In this paper, we highlight how partial order reduction is applied to improve refinement checking in PAT. Experiment results show that PAT outperforms FDR in some cases.
SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning

Visual attention has been successfully applied in structural prediction tasks such as visual captioning and question answering. Existing visual attention models are generally spatial, i.e., the attention is modeled as spatial probabilities that re-weight the last conv-layer feature map of a CNN encoding an input image. However, we argue that such spatial attention does not necessarily conform to the attention mechanism &#x2014; a dynamic feature extractor that combines contextual fixations over time, as CNN features are naturally spatial, channel-wise and multi-layer. In this paper, we introduce a novel convolutional neural network dubbed SCA-CNN that incorporates Spatial and Channel-wise Attentions in a CNN. In the task of image captioning, SCA-CNN dynamically modulates the sentence generation context in multi-layer feature maps, encoding where (i.e., attentive spatial locations at multiple layers) and what (i.e., attentive channels) the visual attention is. We evaluate the proposed SCA-CNN architecture on three benchmark image captioning datasets: Flickr8K, Flickr30K, and MSCOCO. It is consistently observed that SCA-CNN significantly outperforms state-of-the-art visual attention-based image captioning methods.
Cannabinoids and the skeleton: from marijuana to reversal of bone loss.

The active component of marijuana, Delta(9)-tetrahydrocannabinol, activates the CB1 and CB2 cannabinoid receptors, thus mimicking the action of endogenous cannabinoids. CB1 is predominantly neuronal and mediates the cannabinoid psychotropic effects. CB2 is predominantly expressed in peripheral tissues, mainly in pathological conditions. So far the main endocannabinoids, anandamide and 2-arachidonoylglycerol, have been found in bone at 'brain' levels. The CB1 receptor is present mainly in skeletal sympathetic nerve terminals, thus regulating the adrenergic tonic restrain of bone formation. CB2 is expressed in osteoblasts and osteoclasts, stimulates bone formation, and inhibits bone resorption. Because low bone mass is the only spontaneous phenotype so far reported in CB2 mutant mice, it appears that the main physiologic involvement of CB2 is associated with maintaining bone remodeling at balance, thus protecting the skeleton against age-related bone loss. Indeed, in humans, polymorphisms in CNR2, the gene encoding CB2, are strongly associated with postmenopausal osteoporosis. Preclinical studies have shown that a synthetic CB2-specific agonist rescues ovariectomy-induced bone loss. Taken together, the reports on cannabinoid receptors in mice and humans pave the way for the development of 1) diagnostic measures to identify osteoporosis-susceptible polymorphisms in CNR2, and 2) cannabinoid drugs to combat osteoporosis.
Small Cluster in Cyber Physical Systems: Network Topology, Interdependence and Cascading Failures

In cyber physical system (CPS), computational resources and physical resources are strongly correlated and mutually dependent. Cascading failures occur between coupled networks, cause the system more fragile than single network. Besides widely used metric giant component, we study small cluster (small component) in interdependent networks after cascading failures occur. We first introduce an overview on how small clusters distribute in various single networks. Then we propose a percolation theory based mathematical method to study how small clusters be affected by the interdependence between two coupled networks. We prove that the upper bounds exist for both the fraction and the number of operating small clusters. Without loss of generality, we apply both synthetic network and real network data in simulation to study small clusters under different interdependence models and network topologies. The extensive simulations highlight our findings: except the giant component, considerable proportion of small clusters exists, with the remaining part fragmenting to very tiny pieces or even massive isolated single vertex; no matter how the two networks are tightly coupled, an upper bound exists for the size of small clusters. We also discover that the interdependent small-world networks generally have the highest fractions of operating small clusters. Three attack strategies are compared: Inter Degree Priority Attack, Intra Degree Priority Attack and Random Attack. We observe that the fraction of functioning small clusters keeps stable and is independent from the attack strategies.
Integrating low-rank and group-sparse structures for robust multi-task learning

Multi-task learning (MTL) aims at improving the generalization performance by utilizing the intrinsic relationships among multiple related tasks. A key assumption in most MTL algorithms is that all tasks are related, which, however, may not be the case in many real-world applications. In this paper, we propose a robust multi-task learning (RMTL) algorithm which learns multiple tasks simultaneously as well as identifies the irrelevant (outlier) tasks. Specifically, the proposed RMTL algorithm captures the task relationships using a low-rank structure, and simultaneously identifies the outlier tasks using a group-sparse structure. The proposed RMTL algorithm is formulated as a non-smooth convex (unconstrained) optimization problem. We propose to adopt the accelerated proximal method (APM) for solving such an optimization problem. The key component in APM is the computation of the proximal operator, which can be shown to admit an analytic solution. We also theoretically analyze the effectiveness of the RMTL algorithm. In particular, we derive a key property of the optimal solution to RMTL; moreover, based on this key property, we establish a theoretical bound for characterizing the learning performance of RMTL. Our experimental results on benchmark data sets demonstrate the effectiveness and efficiency of the proposed algorithm.
Feature selection methods for conversational recommender systems

This paper focuses on question selection methods for conversational recommender systems. We consider a scenario, where given an initial user query, the recommender system may ask the user to provide additional features describing the searched products. The objective is to generate questions/features that a user would likely reply, and if replied, would effectively reduce the result size of the initial query. Classical entropy-based feature selection methods are effective in term of result size reduction, but they select questions uncorrelated with user needs and therefore unlikely to be replied. We propose two feature-selection methods that combine feature entropy with an appropriate measure of feature relevance. We evaluated these methods in a set of simulated interactions where a probabilistic model of user behavior is exploited. The results show that these methods outperform entropy-based feature selection.