nils-herrmann/clean_scirepeval huggingface dataset:
Development of Enteric Coated Pantoprazole Tablets with an Aqueous Based Polymer

The advent of new technical solutions has offered a vast scope to encounter the existing challenges in tablet coating technology. One such outcome is the usage of innovative aqueous coating compositions to meet the limitations of organic based coating. The present study aimed at development of delayed release pantoprazole sodium tablets by coating with aqueous acrylic system belonging to methacrylic acid copolymer and to investigate the ability of the dosage form to protect the drug from acid milieu and to release rapidly in the duodenal pH. The core tablets were produced by direct compression using different disintegrants in variable concentrations. The physicochemical properties of all the tablets were consistent and satisfactory. Crosspovidone at 7.5% proved to be a better disintegrant with rapid disintegration with a minute, owing to its wicking properties. The optimized formulations were seal coated using HPMC dispersion to act as a barrier between the acid liable drug and enteric film coatings. The subcoating process was followed by enteric coating of tablets by the application of acryl-Eze at different theoretical weight gains. Enteric coated formulations were subjected to disintegration and dissolution tests by placing them in 0.1 N HCl for 2 h and then in pH 6.8 phosphate buffer for 1 h. The coated tablets remained static without peeling or cracking in the acid media, however instantly disintegrated in the intestinal pH. In the in vitro release studies, the optimized tablets released 0.16% in the acid media and 96% in the basic media which are well within the selected criteria. Results of the stability tests were satisfactory with the dissolution rate and assays were within acceptable limits. The results ascertained the acceptability of the aqueous based enteric coating composition for the successful development of delayed release, duodenal specific dosage forms for proton pump inhibitors.
On Improving a Microblog Ranking

Microblog ranking is a hot research topic in recent years. Most of the related works apply TF-IDF metric for calculating content similarity while neglecting their semantic similarity. And most existing search engines which retrieve the microblog list by string matching the search keywords is not competent to provide a reliable list for users when dealing with polysemy and synonym. Besides, treating all the users with same authority for all topics is intuitively not ideal. In this paper, a comprehensive strategy for microblog ranking is proposed. First, we extend the conventional TF-IDF based content similarity with exploiting knowledge from WordNet. Then, we further incorporate a new feature for microblog ranking that is the topical relation between search keyword and its retrieval. Author topical authority is also incorporated into the ranking framework as an important feature for microblog ranking. Gradient Boosting Decision Tree(GBDT), then is employed to train the ranking model with multiple features involved. We conduct thorough experiments on a large-scale real-world Twitter dataset and demonstrate that our proposed approach outperform a number of existing approaches in discovering higher quality and more related microblogs.
Does perpetrating violence damage mental health? Differences between forcibly recruited and voluntary combatants in DR Congo.

As a consequence of the ongoing conflict in the Democratic Republic of the Congo (DRC), combatants are constantly involved in various forms of violence. Findings concerning the impact of perpetrating violence on mental health are contradictory, ranging from increasing to buffering the risk for mental ill health. The present study investigated the impact of perpetrating violence on mental health. In total, 204 forcibly recruited and voluntary male combatants (mean age = 24.61 years) from different armed groups in the eastern DRC took part in the study. In a semistructured interview, respondents were questioned about appetitive aggression and posttraumatic stress disorder (PTSD) as well as self-experienced violence and self-perpetrated violent offending. A multivariate analysis of variance (η(2) = .23) revealed that voluntary combatants perpetrated more violent acts (η(2) = .06) and showed higher appetitive aggression η(2) = .03). A moderated multiple regression analysis (R(2) = .20) showed that perpetrating violence was positively related to PTSD in forcibly recruited combatants, but not in voluntary combatants. Thus, perpetrating violence may not necessarily qualify as a traumatic stressor. Further studies might consider assessing the combatant's perception of committing violent acts.
A comparison of immersive HMD, fish tank VR and fish tank with haptics displays for volume visualization

Although a wide range of virtual reality (VR) systems are in use, there are few guidelines to help system and application developers select the components most appropriate for the domain problem they are investigating. Using the results of an empirical study, we developed such guidelines for the choice of display environment for four specific, but common, volume visualization problems: identification and judgment of the size, shape, density, and connectivity of objects present in a volume. These tasks are derived from questions being asked by collaborators studying Cystic Fibrosis (CF). We compared user performance in three different stereo VR systems: (1) head-mounted display (HMD); (2) fish tank VR (fish tank); and (3) fish tank VR augmented with a haptic device (haptic). HMD participants were placed "inside" the volume and walked within it to explore its structure. Fish tank and haptic participants saw the entire volume on-screen and rotated it to view it from different perspectives. Response time and accuracy were used to measure performance. Results showed that the fish tank and haptic groups were significantly more accurate at judging the shape, density, and connectivity of objects and completed the tasks significantly faster than the HMD group. Although the fish tank group was itself significantly faster than the haptic group, there were no statistical differences in accuracy between the two. Participants classified the HMD system as an "inside-out" display (looking outwards from inside the volume), and the fish tank and haptic systems as "outside-in" displays (looking inwards from outside the volume). Including haptics added an inside-out capability to the fish tank system through the use of touch. We recommend an outside-in system because it offers both overview and context, two visual properties that are important for the volume visualization tasks we studied. In addition, based on the haptic group's opinion (80% positive) that haptic feedback aided comprehension, we recommend supplementing the outside-in visual display with inside-out haptics when possible.
From the Semantic Web to social machines: A research challenge for AI on the World Wide Web

Article history: Received 24 September 2009 Received in revised form 1 October 2009 Accepted 1 October 2009 Available online 17 November 2009 The advent of social computing on the Web has led to a new generation of Web applications that are powerful and world-changing. However, we argue that we are just at the beginning of this age of “social machines” and that their continued evolution and growth requires the cooperation of Web and AI researchers. In this paper, we show how the growing Semantic Web provides necessary support for these technologies, outline the challenges we see in bringing the technology to the next level, and propose some starting places for the research.
Predicting Dengue Using Bayes Net Classifier

Dengue is a mosquito-borne fever in the southernmost part of India. It is caused by female mosquitoes, grown in stagnant water .The major symptoms for dengue are fever, bleeding, pain behind eyes, abdominal pain, fatigue, loss of appetite etc., Early diagnosis is the most important, in order to save the human from this deadly disease. Classification techniques helps to predict the disease at an early stage. In this research, Bayes belief network is classification technique is used to predict the probability for various disease occurrence using the probability distribution.  Prediction, Diagnosis, Bayes belief network, Probability distribution,
Collocation and Thai Word Segmentation

This paper presents another approach of Thai word segmentation, which is composed of two processes : syllable segmentation and syllable merging. Syllable segmentation is done on the basis of trigram statistics. Syllable merging is done on the basis of collocation between syllables. We argue that many of word segmentation ambiguities can be resolved at the level of syllable segmentation. Since a syllable is a more well-defined unit and more consistent in analysis than a word, this approach is more reliable than other approaches that use a wordsegmented corpus. This approach can perform well at the level of accuracy 81-98% depending on the dictionary used in the segmentation.
Segmentation and Characterization of Chewing Bouts by Monitoring Temporalis Muscle Using Smart Glasses With Piezoelectric Sensor

Several methods have been proposed for automatic and objective monitoring of food intake, but their performance suffers in the presence of speech and motion artifacts. This paper presents a novel sensor system and algorithms for detection and characterization of chewing bouts from a piezoelectric strain sensor placed on the temporalis muscle. The proposed data acquisition device was incorporated into the temple of eyeglasses. The system was tested by ten participants in two part experiments, one under controlled laboratory conditions and the other in unrestricted free-living. The proposed food intake recognition method first performed an energy-based segmentation to isolate candidate chewing segments (instead of using epochs of fixed duration commonly reported in research literature), with the subsequent classification of the segments by linear support vector machine models. On participant level (combining data from both laboratory and free-living experiments), with ten-fold leave-one-out cross-validation, chewing were recognized with average F-score of 96.28% and the resultant area under the curve was 0.97, which are higher than any of the previously reported results. A multivariate regression model was used to estimate chew counts from segments classified as chewing with an average mean absolute error of 3.83% on participant level. These results suggest that the proposed system is able to identify chewing segments in the presence of speech and motion artifacts, as well as automatically and accurately quantify chewing behavior, both under controlled laboratory conditions and unrestricted free-living.
A Text Detection System for Natural Scenes with Convolutional Feature Learning and Cascaded Classification

We propose a system that finds text in natural scenes using a variety of cues. Our novel data-driven method incorporates coarse-to-fine detection of character pixels using convolutional features (Text-Conv), followed by extracting connected components (CCs) from characters using edge and color features, and finally performing a graph-based segmentation of CCs into words (Word-Graph). For Text-Conv, the initial detection is based on convolutional feature maps similar to those used in Convolutional Neural Networks (CNNs), but learned using Convolutional k-means. Convolution masks defined by local and neighboring patch features are used to improve detection accuracy. The Word-Graph algorithm uses contextual information to both improve word segmentation and prune false character/word detections. Different definitions for foreground (text) regions are used to train the detection stages, some based on bounding box intersection, and others on bounding box and pixel intersection. Our system obtains pixel, character, and word detection f-measures of 93.14%, 90.26%, and 86.77% respectively for the ICDAR 2015 Robust Reading Focused Scene Text dataset, out-performing state-of-the-art systems. This approach may work for other detection targets with homogenous color in natural scenes.
Ontology Based Information Extraction for Disease Intelligence

Disease Intelligence (DI) is based on the acquisition and aggregation of fragmented knowledge of diseases at multiple sources all over the world to provide valuable information to doctors, researchers and information seeking community. Some diseases have their own characteristics changed rapidly at different places of the world and are reported on documents as unrelated and heterogeneous information which may be going unnoticed and may not be quickly available. This research presents an Ontology based theoretical framework in the context of medical intelligence and country/region. Ontology is designed for storing information about rapidly spreading and changing diseases with incorporating existing disease taxonomies to genetic information of both humans and infectious organisms. It further maps disease symptoms to diseases and drug effects to disease symptoms. The machine understandable disease ontology represented as a website thus allows the drug effects to be evaluated on disease symptoms and exposes genetic involvements in the human diseases. Infectious agents which have no known place in an existing classification but have data on genetics would still be identified as organisms through the intelligence of this system. It will further facilitate researchers on the subject to try out different solutions for curing diseases.
Diagnostic applications of nail clippings.

"Nail clipping is a simple technique for diagnosis of several nail unit dermatoses. This article summarizes the practical approach, utility, and histologic findings of a nail clipping in evaluation of onychomycosis, nail unit psoriasis, onychomatricoma, subungual hematoma, melanonychia, and nail cosmetics, and the forensic applications of this easily obtained specimen. It reviews important considerations in optimizing specimen collection, processing methods, and efficacy of special stains in several clinical contexts. Readers will develop a greater understanding and ease of application of this indispensable procedure in assessing nail unit dermatoses."
SNDlib 1.0 - Survivable Network Design Library

We provide information on the Survivable Network Design Library (SNDlib), a data library for fixed telecommunication network design that can be accessed at http://sndlib.zib.de. In version 1.0, the library contains data related to 22 networks which, combined with a set of selected planning parameters, leads to 830 network planning problem instances. In this paper, we provide a mathematical model for each planning problem considered in the library and describe the data concepts of the SNDlib. Furthermore, we provide statistical information and details about the origin of the data sets.
Modeling and Learning Context-Aware Recommendation Scenarios Using Tensor Decomposition

The task of recommending items, like movies, to users is a core feature of many social networks. Standard approaches either use item or user similarity to suggest the next items users might be interested in. Recently, multivariate models like matrix factorization have become popular to combine the advantages of both perspectives. In addition, extensions have been proposed to capture the dynamics of user interests over time, like trends or recurrent user needs. While offering good predictive performance, so far those models do not exploit possibly available rich semantic context. Typically, only one implicit feature, like user ratings, is tracked to give personalized recommendations. However, with semantic data sources, like linked data, wealthy background knowledge becomes available that could be leveraged to improve predictive performance. We argue, that a more flexible framework is needed to model and learn a greater class of recommendation scenarios where rich context is available. Thus, we propose a generic approach which generalizes state-of-the-art methods based on pair wise interaction tensor factorization by leveraging arbitrary background knowledge related to the recommendation situation. Our experiments on streamed semantic data from a social network show that by adding varying sets of context - like user information, sequential information or time information - the ranking of potential items can be personalized and the predictive performance can be improved.
Sensing as a Service and Big Data

Internet of Things (IoT) will comprise billions of devices that can sense, communicate, compute and potentially actuate. Data streams coming from these devices will challenge the traditional approaches to data management and contribute to the emerging paradigm of big data. This paper discusses emerging Internet of Things (IoT) architecture, large scale sensor network applications, federating sensor networks, sensor data and related context capturing techniques, challenges in cloud-based management, storing, archiving and processing of
Blockchain: A path to grid modernization and cyber resiliency

Blockchain may help solve several complex problems related to integrity and trustworthiness of rapid, distributed, complex energy transactions and data exchanges. In a move towards resilience, blockchain commoditizes trust and enables automated smart contracts to support auditable multiparty transactions based on predefined rules between distributed energy providers and customers. Blockchain based smart contracts also help remove the need to interact with third-parties, facilitating the adoption and monetization of distributed energy transactions and exchanges, both energy flows as well as financial transactions. This may help reduce transactive energy costs and increase the security and sustainability of distributed energy resource (DER) integration, helping to remove barriers to a more decentralized and resilient power grid. This paper explores the application of blockchain and smart contracts to improve smart grid cyber resiliency and secure transactive energy applications.
ShapeCodes: Self-supervised Feature Learning by Lifting Views to Viewgrids

We introduce an unsupervised feature learning approach that embeds 3D shape information into a single-view image representation. The main idea is a self-supervised training objective that, given only a single 2D image, requires all unseen views of the object to be predictable from learned features. We implement this idea as an encoderdecoder convolutional neural network. The network maps an input image of an unknown category and unknown viewpoint to a latent space, from which a deconvolutional decoder can best “lift” the image to its complete viewgrid showing the object from all viewing angles. Our class-agnostic training procedure encourages the representation to capture fundamental shape primitives and semantic regularities in a data-driven manner— without manual semantic labels. Our results on two widely-used shape datasets show 1) our approach successfully learns to perform “mental rotation” even for objects unseen during training, and 2) the learned latent space is a powerful representation for object recognition, outperforming several existing unsupervised feature learning methods.
Radiation Queue: Meeting Patient Waiting Time Targets

This article proposes a queueing and patient pooling approach for radiotherapy capacity allocation with heterogeneous treatment machines called linear accelerators (LINACs), different waiting time targets (WTTs), and treatment protocols. We first propose a novel queueing framework with LINAC time slots as servers. This framework leads to simple single-class queues for the evaluation of WTT satisfaction that would, otherwise, require an analysis of complicated multiclass queues with reentrance. Mixed-integer programming models are proposed for capacity allocation and case-mix optimization under WTT constraints. We then extend the queueing framework by pooling the basic patient types into groups sharing the same slot servers. A mathematical programming model and a pairwise merging heuristic are proposed for patient pooling optimization to minimize the overall LINAC capacity needed to meet all WTT requirements. Extended numerical experiments are conducted to assess the efficiency of our approach and to show the properties of optimal capacity allocation and patient pooling.
Small-world brain networks.

Many complex networks have a small-world topology characterized by dense local clustering or cliquishness of connections between neighboring nodes yet a short path length between any (distant) pair of nodes due to the existence of relatively few long-range connections. This is an attractive model for the organization of brain anatomical and functional networks because a small-world topology can support both segregated/specialized and distributed/integrated information processing. Moreover, small-world networks are economical, tending to minimize wiring costs while supporting high dynamical complexity. The authors introduce some of the key mathematical concepts in graph theory required for small-world analysis and review how these methods have been applied to quantification of cortical connectivity matrices derived from anatomical tract-tracing studies in the macaque monkey and the cat. The evolution of small-world networks is discussed in terms of a selection pressure to deliver cost-effective information-processing systems. The authors illustrate how these techniques and concepts are increasingly being applied to the analysis of human brain functional networks derived from electroencephalography/magnetoencephalography and fMRI experiments. Finally, the authors consider the relevance of small-world models for understanding the emergence of complex behaviors and the resilience of brain systems to pathological attack by disease or aberrant development. They conclude that small-world models provide a powerful and versatile approach to understanding the structure and function of human brain systems.
The Impact of Humanoid Affect Expression on Human Behavior in a Game-Theoretic Setting

With the rapid development of robot and other intelligent and autonomous agents, how a human could be influenced by a robot’s expressed mood when making decisions becomes a crucial question in human-robot interaction. In this pilot study, we investigate (1) in what way a robot can express a certain mood to influence a human’s decision making behavioral model; (2) how and to what extent the human will be influenced in a game theoretic setting. More specifically, we create an NLP model to generate sentences that adhere to a specific affective expression profile. We use these sentences for a humanoid robot as it plays a Stackelberg security game against a human. We investigate the behavioral model of the human player.
Measurements of heart motion using accelerometers

We have used acceleration sensors to monitor the heart motion during surgery. A three-axis accelerometer was made from two commercially available two-axis sensors, and was used to measure the heart motion in anesthetized pigs. The heart moves due to both respiration and heart beating. The heart beating was isolated from respiration by high-pass filtering at 1.0 Hz, and heart wall velocity and position were calculated by numerically integrating the filtered acceleration traces. The resulting curves reproduced the heart motion in great detail, noise was hardly visible. Events that occurred during the measurements, e.g. arrhythmias and fibrillation, were recognized in the curves, and confirmed by comparison with synchronously recorded ECG data. We conclude that acceleration sensors are able to measure heart motion with good resolution, and that such measurements can reveal patterns that may be an indication of heart circulation failure.