nils-herrmann/clean_scirepeval huggingface dataset:
Stacked Cross Attention for Image-Text Matching

In this paper, we study the problem of image-text matching. Inferring the latent semantic alignment between objects or other salient stuff (e.g. snow, sky, lawn) and the corresponding words in sentences allows to capture fine-grained interplay between vision and language, and makes image-text matching more interpretable. Prior work either simply aggregates the similarity of all possible pairs of regions and words without attending differentially to more and less important words or regions, or uses a multi-step attentional process to capture limited number of semantic alignments which is less interpretable. In this paper, we present Stacked Cross Attention to discover the full latent alignments using both image regions and words in a sentence as context and infer image-text similarity. Our approach achieves the state-of-the-art results on the MSCOCO and Flickr30K datasets. On Flickr30K, our approach outperforms the current best methods by 22.1% relatively in text retrieval from image query, and 18.2% relatively in image retrieval with text query (based on Recall@1). On MS-COCO, our approach improves sentence retrieval by 17.8% relatively and image retrieval by 16.6% relatively (based on Recall@1 using the 5K test set). Code has been made available at: https: //github.com/kuanghuei/SCAN.
DS-Ontology: A Disease-Symptom Ontology for General Diagnosis Enhancement

The diagnosis process is often challenging, it involves the correlation of various pieces of information followed by several possible conclusions and iterations of diseases that may overload physicians when facing urgent cases that may lead to bad consequences threatening people's lives. The physician is asked to search for all symptoms related to a specific disease. To make this kind of search possible, there is a strong need for an effective way to store and retrieve medical knowledge from various datasets in order to find links between human disease and symptoms. For this purpose, we propose in this work a new Disease-Symptom Ontology (DS-Ontology). Utilizing existing biomedical ontologies, we integrate all available disease-symptom relationships to create a DS-Ontology that will be used latter in an ontology-based Clinical Decision Support System to determine a highly effective medical diagnosis.
Development and Validation of a Smartphone Addiction Scale (SAS)

The aim of this study was to develop a self-diagnostic scale that could distinguish smartphone addicts based on the Korean self-diagnostic program for Internet addiction (K-scale) and the smartphone's own features. In addition, the reliability and validity of the smartphone addiction scale (SAS) was demonstrated.



A total of 197 participants were selected from Nov. 2011 to Jan. 2012 to accomplish a set of questionnaires, including SAS, K-scale, modified Kimberly Young Internet addiction test (Y-scale), visual analogue scale (VAS), and substance dependence and abuse diagnosis of DSM-IV. There were 64 males and 133 females, with ages ranging from 18 to 53 years (M = 26.06; SD = 5.96). Factor analysis, internal-consistency test, t-test, ANOVA, and correlation analysis were conducted to verify the reliability and validity of SAS.



Based on the factor analysis results, the subscale "disturbance of reality testing" was removed, and six factors were left. The internal consistency and concurrent validity of SAS were verified (Cronbach's alpha = 0.967). SAS and its subscales were significantly correlated with K-scale and Y-scale. The VAS of each factor also showed a significant correlation with each subscale. In addition, differences were found in the job (p<0.05), education (p<0.05), and self-reported smartphone addiction scores (p<0.001) in SAS.



This study developed the first scale of the smartphone addiction aspect of the diagnostic manual. This scale was proven to be relatively reliable and valid.
Coingestion of protein with carbohydrate during recovery from endurance exercise stimulates skeletal muscle protein synthesis in humans.

Coingestion of protein with carbohydrate (CHO) during recovery from exercise can affect muscle glycogen synthesis, particularly if CHO intake is suboptimal. Another potential benefit of protein feeding is an increased synthesis rate of muscle proteins, as is well documented after resistance exercise. In contrast, the effect of nutrient manipulation on muscle protein kinetics after aerobic exercise remains largely unexplored. We tested the hypothesis that ingesting protein with CHO after a standardized 2-h bout of cycle exercise would increase mixed muscle fractional synthetic rate (FSR) and whole body net protein balance (WBNB) vs. trials matched for total CHO or total energy intake. We also examined whether postexercise glycogen synthesis could be enhanced by adding protein or additional CHO to a feeding protocol that provided 1.2 g CHO x kg(-1) x h(-1), which is the rate generally recommended to maximize this process. Six active men ingested drinks during the first 3 h of recovery that provided either 1.2 g CHO.kg(-1).h(-1) (L-CHO), 1.2 g CHO + 0.4 g protein x kg(-1) x h(-1) (PRO-CHO), or 1.6 g CHO x kg(-1) x h(-1) (H-CHO) in random order. Based on a primed constant infusion of l-[ring-(2)H(5)]phenylalanine, analysis of biopsies (vastus lateralis) obtained at 0 and 4 h of recovery showed that muscle FSR was higher (P < 0.05) in PRO-CHO (0.09 +/- 0.01%/h) vs. both L-CHO (0.07 +/- 0.01%/h) and H-CHO (0.06 +/- 0.01%/h). WBNB assessed using [1-(13)C]leucine was positive only during PRO-CHO, and this was mainly attributable to a reduced rate of protein breakdown. Glycogen synthesis rate was not different between trials. We conclude that ingesting protein with CHO during recovery from aerobic exercise increased muscle FSR and improved WBNB, compared with feeding strategies that provided CHO only and were matched for total CHO or total energy intake. However, adding protein or additional CHO to a feeding strategy that provided 1.2 g CHO x kg(-1) x h(-1) did not further enhance glycogen resynthesis during recovery.
Colour and its effects in interior environment : a review

2013 30 Sept. 2013 07 Oct. 2013 Colour is an inseparable as well as an important aspect of an interior design. The maximum influence in interior comes with the design of colour. So it is very important to study the colour and its effect in interior environment, it may be physiological as well as psychological. For this, articles were reviewed and analyzed from the existing literature, related to use of colour in both residence as well as commercial interior. The three major areas reviewed were (1) Psychological and physiological effect of colour (2) Meaning of Warm, Cool and Neutral Colour (3) Effect of Colour in forms. The results show that colour is important in designing functional spaces. The results of this analysis may benefit to architects, interior designer, and homeowner to use colour effectively in interior environment.
Mining Software Engineering Data from GitHub

GitHub is the largest collaborative source code hosting site built on top of the Git version control system. The availability of a comprehensive API has made GitHub a target for many software engineering and online collaboration research efforts. In our work, we have discovered that a) obtaining data from GitHub is not trivial, b) the data may not be suitable for all types of research, and c) improper use can lead to biased results. In this tutorial, we analyze how data from GitHub can be used for large-scale, quantitative research, while avoiding common pitfalls. We use the GHTorrent dataset, a queryable offline mirror of the GitHub API data, to draw examples from and present pitfall avoidance strategies.
Fog Computing for Smart Living

The World Health Organization projects that the number of people living in cities will nearly double over the next few decades, so urban centers need to provide more sustainable solutions for smart living. New technologies— such as materials, sensors, wireless communications, and controls—will be necessary to manage living environments that proactively sense behavioral and health risks and provide situationaware responses t o emergencies or disasters. In addition, utility and transportation networks must adapt to dynamic usage, tra c conditions, and user behavior with a minimal carbon footprint; a clean and renewable energy grid must actuate localized energy and power control; and pervasive security is needed to detect and prevent potential threats. This vision is bold but critical to enabling smart living. Cloud-only models face serious challenges in latency, network bandwidth, geographic focus, reliability, and security. Fog computing reduces these challenges by providing a systemlevel horizontal architecture to distribute computing, storage, control, and networking resources and services from the cloud to connected devices (“things”). Think of fog computing as the cloud on the ground: it enables latency-sensitive computing to be performed in close proximity to the things it controls. Over time, fog and cloud computing will converge into uni ed end-to-end platforms o ering integrated services and applications along the continuum from the cloud to things. Applications developed and deployed for the cloud will be able to run in fog and vice versa.
Learning content–social influential features for influence analysis

We address how to measure the information propagation probability between users given certain contents. In sharp contrast to existing works that oversimplify the propagation model as predefined distributions, our approach fundamentally attempts to answer why users are influenced (e.g., by content or relations) and whether the corresponding influential features (e.g., hidden factors) can be inferred from the propagation in the entire network. In particular, we propose a novel method to deeply learn the unified feature representations for both user pair and content, where the homogeneous feature similarity can be used to estimate the propagation probability between users with given content. The features are dubbed content–social influential feature since we consider not only the content of the propagation information but also how it propagates over the social network. We design a fast asynchronous parallel algorithm for the feature learning. Through extensive experiments on a real-world social network with 53 million users and 838 million tweets, we show significantly improved performance as compared to other state-of-the-art methods on various social influence analysis tasks.
"Is this an example image?" - Predicting the Relative Abstractness Level of Image and Text

te the feasibility of the approach. Successful multimodal search and retrieval requires the automatic understanding of semantic cross-modal relations, which, however, is still an open research problem. Previous work has suggested the metrics crossmodal mutual information and semantic correlation to model and predict cross-modal semantic relations of image and text. In this paper, we present an approach to predict the (cross-modal) relative abstractness level of a given image-text pair, that is whether the image is an abstraction of the text or vice versa. For this purpose, we introduce a new metric that captures this specific relationship between image and text at the Abstractness Level (ABS). We present a deep learning approach to predict this metric, which relies on an autoencoder architecture that allows us to significantly reduce the required amount of labeled training data. For this, a comprehensive set of publicly available scientific documents has been accumulated. Experimental results on a challenging test set demonstrate the feasibility of the approach.
On Identifying Cited Texts for Citances and Classifying Their Discourse Facets by Classification Techniques

1 †This paper is an extended version of the ICSCA 2017 paper “Reference scope identification for citances by classification with text similarity measures” [55]. This work is supported by the Ministry of Science and Technology (MOST), Taiwan (Grant number: MOST 104-2221-E-178-001). Corresponding author. Tel: +886 4 23226940728; fax: +886 4 23222621. On Identifying Cited Texts for Citances and Classifying Their Discourse Facets by Classification Techniques
Medical devices Inspection and Maintenance ; A Literature Review

Modern medical devices and equipment have become very complex and sophisticated and are expected to operate under stringent environments. Hospitals must ensure that their critical medical devices are safe, accurate, reliable and operating at the required level of performance. Even though the importance, the application of all inspection, maintenance and optimization models to medical devices is fairly new. In Canada, most, if not all healthcare organizations include all their medical equipment in their maintenance program and just follow manufacturers’ recommendations for preventative maintenance. Then, current maintenance strategies employed in hospitals and healthcare organizations have difficulty in identifying specific risks and applying optimal risk reduction activities. This paper addresses these gaps found in literature for medical equipment inspection and maintenance and reviews various important aspects including current policies applied in hospitals. Finally we suggest future research which will be the starting point to develop tools and policies for better medical devices management in the future.
PATTERNS OF INFORMATION SOURCES USED BY GRADUATE STUDENTS: A CITATION ANALYSIS OF DOCTORAL DISSERTATIONS IN THE FIELD OF ISLAMIC STUDIES

This article depicts the results of a study carried out to ascertain the information pattern based on the sources used by graduate students from the Islamic Studies Academy submitted at the University of Malaya, Kuala Lumpur. A total of 14377 citations consisting of 54 doctoral dissertations from the Year 2005 to 2009 were examined using the citation analysis. The highest citations per dissertation was 684, while the lowest being 105 citations. The result shows that the materials used by graduate students in this field vary and are multidisciplinary by nature. Books were cited more than other forms of sources contributing 65%, where journal articles contributed 20%.Conference proceedings contributed 11%, dissertations and thesis 3% and other categories consisted of web sites, interviews and legal documents contributing 9%. These findings corroborate with previous citations done in the Humanities discipline. Among the most popular cited journals are in-house journals namely Jurnal Syariah and Jurnal Usuluddin. In addition, graduate students used a substantial amount of Malaysian language sources at the rate of 60%, Arabic language scholarships contributed to 40% of the total citations. Approximately 30% of all sources cited are over 10 years of age. Hence, this study provides valuable insights to guide librarians in understanding the sources used and serves as an analytic tool for the development of source collection in the library services.
Classification of defect types in requirements specifications: Literature review, proposal and assessment

Requirements defects have a major impact throughout the whole software lifecycle. Having a specific defects classification for requirements is important to analyse the root causes of problems, build checklists that support requirements reviews and to reduce risks associated with requirements problems. In our research we analyse several defects classifiers; select the ones applicable to requirements specifications, following rules to build defects taxonomies; and assess the classification validity in an experiment of requirements defects classification performed by graduate and undergraduate students. Not all subjects used the same type of defect to classify the same defect, which suggests that defects classification is not consensual. Considering our results we give recommendations to industry and other researchers on the design of classification schemes and treatment of classification results.
Next-Generation Machine Learning for Biological Networks

Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology.
The Time Machine in Our Mind

This article provides the first comprehensive conceptual account for the imagistic mental machinery that allows us to travel through time--for the time machine in our mind. It is argued that language reveals this imagistic machine and how we use it. Findings from a range of cognitive fields are theoretically unified and a recent proposal about spatialized mental time travel is elaborated on. The following novel distinctions are offered: external versus internal viewing of time; ''watching" time versus projective ''travel" through time; optional versus obligatory mental time travel; mental time travel into anteriority or posteriority versus mental time travel into the past or future; single mental time travel versus nested dual mental time travel; mental time travel in episodic memory versus mental time travel in semantic memory; and ''seeing" versus ''sensing" mental imagery. Theoretical, empirical, and applied implications are discussed.
Toward a Fully Autonomous UAV: Research Platform for Indoor and Outdoor Urban Search and Rescue

Urban search and rescue missions raise special requirements on robotic systems. Small aerial systems provide essential support to human task forces in situation assessment and surveillance. As external infrastructure for navigation and communication is usually not available, robotic systems must be able to operate autonomously. A limited payload of small aerial systems poses a great challenge to the system design. The optimal tradeoff between flight performance, sensors, and computing resources has to be found. Communication to external computers cannot be guaranteed; therefore, all processing and decision making has to be done on board. In this article, we present an unmanned aircraft system design fulfilling these requirements. The components of our system are structured into groups to encapsulate their functionality and interfaces. We use both laser and stereo vision odometry to enable seamless indoor and outdoor navigation. The odometry is fused with an inertial measurement unit in an extended Kalman filter. Navigation is supported by a module that recognizes known objects in the environment. A distributed computation approach is adopted to address the computational requirements of the used algorithms. The capabilities of the system are validated in flight experiments, using a quadrotor.
Clinical research informatics and electronic health record data.

The goal of this survey is to discuss the impact of the growing availability of electronic health record (EHR) data on the evolving field of Clinical Research Informatics (CRI), which is the union of biomedical research and informatics.



Major challenges for the use of EHR-derived data for research include the lack of standard methods for ensuring that data quality, completeness, and provenance are sufficient to assess the appropriateness of its use for research. Areas that need continued emphasis include methods for integrating data from heterogeneous sources, guidelines (including explicit phenotype definitions) for using these data in both pragmatic clinical trials and observational investigations, strong data governance to better understand and control quality of enterprise data, and promotion of national standards for representing and using clinical data.



The use of EHR data has become a priority in CRI. Awareness of underlying clinical data collection processes will be essential in order to leverage these data for clinical research and patient care, and will require multi-disciplinary teams representing clinical research, informatics, and healthcare operations. Considerations for the use of EHR data provide a starting point for practical applications and a CRI research agenda, which will be facilitated by CRI's key role in the infrastructure of a learning healthcare system.
Automatic Video Captioning via Multi-channel Sequential Encoding

In this paper, we propose a novel two-stage video captioning framework composed of 1) a multi-channel video encoder and 2) a sentence-generating language decoder. Both of the encoder and decoder are based on recurrent neural networks with long-short-term-memory cells. Our system can take videos of arbitrary lengths as input. Compared with the previous sequence-to-sequence video captioning frameworks, the proposed model is able to handle multiple channels of video representations and jointly learn how to combine them. The proposed model is evaluated on two large-scale movie datasets (MPII Corpus and Montreal Video Description) and one YouTube dataset (Microsoft Video Description Corpus) and achieves the state-of-the-art performances. Furthermore, we extend the proposed model towards automatic American Sign Language recognition. To evaluate the performance of our model on this novel application, a new dataset for ASL video description is collected based on YouTube videos. Results on this dataset indicate that the proposed framework on ASL recognition is promising and will significantly benefit the independent communication between ASL users and
Wideband CPW-Fed Flexible Bow-Tie Slot Antenna for WLAN/WiMax Systems

A bow-tie antenna based on a slot configuration in a single metal sheet on top of a very thin flexible substrate is introduced. The antenna is constructed from two slotted right-angle triangles fed by a coplanar waveguide transmission line. The topology is very simple and extremely easy to tune in order to reach the proper characteristics after mounting on a supporting structure. A prototype is designed, fabricated, and characterized experimentally. The tunability is proven by considering both the version in free space and the version for use on a brick wall. Measurements demonstrate good agreement with simulations. Both versions cover the wireless local area network (2.4 and 3.65 GHz) and WiMax (2.3, 2.5, and 3.5 GHz) spectra, with an overall impedance bandwidth of 1.79 GHz (57.7%) and 1.46 GHz (49.7%), respectively. The radiation of the antenna is bidirectional with maximum gains of 6.30 and 5.09 dBi for the free space and brick wall versions, respectively.
Explainable artificial intelligence: A survey

In the last decade, with availability of large datasets and more computing power, machine learning systems have achieved (super)human performance in a wide variety of tasks. Examples of this rapid development can be seen in image recognition, speech analysis, strategic game planning and many more. The problem with many state-of-the-art models is a lack of transparency and interpretability. The lack of thereof is a major drawback in many applications, e.g. healthcare and finance, where rationale for model's decision is a requirement for trust. In the light of these issues, explainable artificial intelligence (XAI) has become an area of interest in research community. This paper summarizes recent developments in XAI in supervised learning, starts a discussion on its connection with artificial general intelligence, and gives proposals for further research directions.