nils-herrmann/clean_scirepeval huggingface dataset:
Recent Trends on Chemically Modified Chitosan for Biological Interest ABOUT THE AUTHORS

There is no doubt that, chitosan is considered as one of the most important biopolymers that can easily extracted from nature resources or synthesized in the chemical laboratories. Chitosan also display a suitable number of important properties in different fields of applications. Recently, chitosan has been reported as a perfect candidate as a trestle macromolecule for variable biological fields of study. This include, tissue engineering. cell culture and gene delivery, etc. Furthermore, chitosan has widely used in different types of industries which include: food, agriculture, fragrance, and even cosmetic industries. Besides that, chitosan derivatives is treated as excellent tool in waste water treatment. Therefore, the present work gives a simple selective overview for different modifications of Chitosan macromolecule with a special attention to its biological interest. Prior that, a closer look to its resources, chemical structure as well as general properties has been also determined which include its solubility character and its molecular weight. Furthermore, the chemistry of chitosan has been also mentioned with selected examples of each type of interaction. Finally a brief for sulfone based modified chitosan has been reported including classical methods of synthesis and its experimental variants.
Recognition of emotions in interactive voice response systems

This paper reports emotion recognition results from speech signals, with particular focus on extracting emotion features from the short utterances typical of Interactive Voice Response (IVR) applications. We focus on distinguishing anger versus neutral speech, which is salient to call center applications. We report on classification of other types of emotions such as sadness, boredom, happy, and cold anger. We compare results from using neural networks, Support Vector Machines (SVM), K-Nearest Neighbors, and decision trees. We use a database from the Linguistic Data Consortium at University of Pennsylvania, which is recorded by 8 actors expressing 15 emotions. Results indicate that hot anger and neutral utterances can be distinguished with over 90% accuracy. We show results from recognizing other emotions. We also illustrate which emotions can be clustered together using the selected prosodic features.
HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition

We present an algorithm for simultaneous face detection, landmarks localization, pose estimation and gender recognition using deep convolutional neural networks (CNN). The proposed method called, HyperFace, fuses the intermediate layers of a deep CNN using a separate CNN followed by a multi-task learning algorithm that operates on the fused features. It exploits the synergy among the tasks which boosts up their individual performances. Additionally, we propose two variants of HyperFace: (1) HyperFace-ResNet that builds on the ResNet-101 model and achieves significant improvement in performance, and (2) Fast-HyperFace that uses a high recall fast face detector for generating region proposals to improve the speed of the algorithm. Extensive experiments show that the proposed models are able to capture both global and local information in faces and performs significantly better than many competitive algorithms for each of these four tasks.
Heat Transfer and Pressure Drop Characteristics in Straight Microchannel of Printed Circuit Heat Exchangers

Performance tests were carried out for a microchannel printed circuit heat exchanger (PCHE), which was fabricated with micro photo-etching and diffusion bonding technologies. The microchannel PCHE was tested for Reynolds numbers in the range of 100‒850 varying the hot-side inlet temperature between 40 °C–50 °C while keeping the cold-side temperature fixed at 20 °C. It was found that the average heat transfer rate and heat transfer performance of the countercurrrent configuration were 6.8% and 10%‒15% higher, respectively, than those of the parallel flow. The average heat transfer rate, heat transfer performance and pressure drop increased with increasing Reynolds number in all experiments. Increasing inlet temperature did not affect the heat transfer performance while it slightly decreased the pressure drop in the experimental range considered. Empirical correlations have been developed for the heat transfer coefficient and pressure drop factor as functions of the Reynolds number.
Ventral and dorsal pathways for language.

Built on an analogy between the visual and auditory systems, the following dual stream model for language processing was suggested recently: a dorsal stream is involved in mapping sound to articulation, and a ventral stream in mapping sound to meaning. The goal of the study presented here was to test the neuroanatomical basis of this model. Combining functional magnetic resonance imaging (fMRI) with a novel diffusion tensor imaging (DTI)-based tractography method we were able to identify the most probable anatomical pathways connecting brain regions activated during two prototypical language tasks. Sublexical repetition of speech is subserved by a dorsal pathway, connecting the superior temporal lobe and premotor cortices in the frontal lobe via the arcuate and superior longitudinal fascicle. In contrast, higher-level language comprehension is mediated by a ventral pathway connecting the middle temporal lobe and the ventrolateral prefrontal cortex via the extreme capsule. Thus, according to our findings, the function of the dorsal route, traditionally considered to be the major language pathway, is mainly restricted to sensory-motor mapping of sound to articulation, whereas linguistic processing of sound to meaning requires temporofrontal interaction transmitted via the ventral route.
Applying Machine Learning to Reduce the Adaptation Space in Self-Adaptive Systems - an exploratory work

Self-adaptive systems are capable of autonomously adjusting their behavior at runtime to accomplish particular adaptation goals. The most common way to realize self-adaption is using a feedback loop(s) which contains four actions: collect runtime data from the system and its environment, analyze the collected data, decide if an adaptation plan is required, and act according to the adaptation plan for achieving the adaptation goals. Existing approaches achieve the adaptation goals by using formal methods, and exhaustively verify all the available adaptation options, i.e., adaptation space. However, verifying the entire adaptation space is often not feasible since it requires time and resources. In this thesis, we present an approach which uses machine learning to reduce the adaptation space in self-adaptive systems. The approach integrates with the feedback loop and selects a subset of the adaptation options that are valid in the current situation. The approach is applied on the simulator of a self-adaptive Internet of Things application which is deployed in KU Leuven, Belgium. We compare our results with a formal model based self-adaptation approach called ActivFORMS. The results show that on average the adaptation space is reduced by 81.2% and the adaptation time by 85% compared to ActivFORMS while achieving the same quality guarantees.
Designing Viral Promotional Campaigns: How Scarcity and Social Proof Affect Online Referrals

Online referrals have become an important mechanism in leveraging consumers’ social networks to spread firms’ promotional campaigns and thus attract new customers. However, despite a robust understanding of the benefits and drivers of consumer referrals, only minimal attention has been paid towards the potential of classical promotional tactics in influencing referral behavior. Therefore, this study examines scarcity and social proof, two promotional cues which are linked to extant referral literature and are of great practical relevance, in the context of a randomized online experiment with the German startup Blinkist. Our analysis reveals that scarcity cues affect consumers' referral propensity regardless of the presence of social proof cues, but that social proof cues amplify scarcity’s effect on consumer referral propensity. We demonstrate that consumers’ perceptions of offer value drive the impact of scarcity on referral likelihood and illuminate how social proof moderates this mediating effect.
Gigabit wireless LANs: an overview of IEEE 802.11ac and 802.11ad

This paper gives an overview of the upcoming IEEE Gigabit Wireless LAN amendments, i.e. IEEE 802.11ac and 802.11ad. Both standard amendments advance wireless networking throughput beyond gigabit rates. 802.11ac adds multi-user access techniques in the form of downlink multi-user (DL MU) multiple input multiple output (MIMO)and 80 and 160 MHz channels in the 5 GHz band for applications such as multiple simultaneous video streams throughout the home. 802.11ad takes advantage of the large swath of available spectrum in the 60 GHz band and defines protocols to enable throughput intensive applications such as wireless I/O or uncompressed video. New waveforms for 60 GHz include single carrier and orthogonal frequency division multiplex (OFDM). Enhancements beyond the new 60 GHz PHY include Personal Basic Service Set (PBSS) operation, directional medium access, and beamforming. We describe 802.11ac channelization, PHY design, MAC modifications, and DL MU MIMO. For 802.11ad, the new PHY layer, MAC enhancements, and beamforming are presented.
Deterministic Coreference Resolution Based on Entity-Centric, Precision-Ranked Rules

We propose a new deterministic approach to coreference resolution that combines the global information and precise features of modern machine-learning models with the transparency and modularity of deterministic, rule-based systems. Our sieve architecture applies a battery of deterministic coreference models one at a time from highest to lowest precision, where each model builds on the previous model's cluster output. The two stages of our sieve-based architecture, a mention detection stage that heavily favors recall, followed by coreference sieves that are precision-oriented, offer a powerful way to achieve both high precision and high recall. Further, our approach makes use of global information through an entity-centric model that encourages the sharing of features across all mentions that point to the same real-world entity. Despite its simplicity, our approach gives state-of-the-art performance on several corpora and genres, and has also been incorporated into hybrid state-of-the-art coreference systems for Chinese and Arabic. Our system thus offers a new paradigm for combining knowledge in rule-based systems that has implications throughout computational linguistics.
efining urban resilience : A review

Fostering resilience in the face of environmental, socioeconomic, and political uncertainty and risk has captured the attention of academics and decision makers across disciplines, sectors, and scales. Resilience has become an important goal for cities, particularly in the face of climate change. Urban areas house the majority of the world’s population, and, in addition to functioning as nodes of resource consumption and as sites for innovation, have become laboratories for resilience, both in theory and in practice. This paper reviews the scholarly literature on urban resilience and concludes that the term has not been well defined. Existing definitions are inconsistent and underdeveloped with respect to incorporation of crucial concepts found in both resilience theory and urban theory. Based on this literature review, and aided by bibliometric analysis, the paper identifies six conceptual tensions fundamental to urban resilience: (1) definition of ‘urban’; (2) understanding of system equilibrium; (3) positive vs. neutral (or negative) conceptualizations of resilience; (4) mechanisms for system change; (5) adaptation versus general adaptability; and (6) timescale of action. To advance this burgeoning field, more conceptual clarity is needed. This paper, therefore, proposes a new definition of urban resilience. This definition takes explicit positions on these tensions, but remains inclusive and flexible enough to enable uptake by, and collaboration among, varying disciplines. The paper concludes with a discussion of how the definition might serve as a boundary object, with the acknowledgement that applying resilience in different contexts requires answering: Resilience for whom and to what? When? Where? And why?
Deep Brain Stimulation for Treatment-Resistant Depression

Treatment-resistant depression is a severely disabling disorder with no proven treatment options once multiple medications, psychotherapy, and electroconvulsive therapy have failed. Based on our preliminary observation that the subgenual cingulate region (Brodmann area 25) is metabolically overactive in treatment-resistant depression, we studied whether the application of chronic deep brain stimulation to modulate BA25 could reduce this elevated activity and produce clinical benefit in six patients with refractory depression. Chronic stimulation of white matter tracts adjacent to the subgenual cingulate gyrus was associated with a striking and sustained remission of depression in four of six patients. Antidepressant effects were associated with a marked reduction in local cerebral blood flow as well as changes in downstream limbic and cortical sites, measured using positron emission tomography. These results suggest that disrupting focal pathological activity in limbic-cortical circuits using electrical stimulation of the subgenual cingulate white matter can effectively reverse symptoms in otherwise treatment-resistant depression.
Improving Optical Character Recognition Process for Low Resolution Images

Optical Character Recognition (OCR) systems often generate errors for images with noise or with low scanning resolution. In this paper, a novel approach that can be used to improve and restore the quality of any clean lower resolution images for easy recognition by OCR process. The method relies on the production of four copies of the original image so that each picture undergoes different restoration processes. These four copies of the images are then passed to a single OCR engine in parallel. In addition to that, the method does not need any traditional alignment between the four resulting texts, which is time consuming and needs complex calculation. It implements a new procedure to choose the best among them and can be applied without prior training on errors. The experimental results show improvement in word error rate for low resolution images by more than 67%.
Towards bipolar linguistic summaries: a novel fuzzy bipolar querying based approach

We study the possibility to extend the concept of linguistic data summaries employing the notion of bipolarity. Yager's linguistic summaries may be derived using a fuzzy linguistic querying interface. We look for a similar analogy between bipolar queries and the extended form of linguistic summaries. The general concept of bipolar query, and its special interpretation are recalled, which turns out to be applicable to accomplish our goal. Some preliminary results are presented and possible directions of further research are pointed out.
Engineering context-aware systems and applications: A survey

Context-awareness is an essential component of systems developed in areas like Intelligent Environments, Pervasive & Ubiquitous Computing and Ambient Intelligence. In these emerging fields, there is a need for computerized systems to have a higher understanding of the situations in which to provide services or functionalities, to adapt accordingly. The literature shows that researchers modify existing engineering methods in order to better fit the needs of context-aware computing. These efforts are typically disconnected from each other and generally focus on solving specific development issues. We encourage the creation of a more holistic and unified engineering process that is tailored for the demands of these systems. For this purpose, we study the state-of-the-art in the development of context-aware systems, focusing on: A) Methodologies for developing context-aware systems, analysing the reasons behind their lack of adoption and features that the community wish they can use; B) Context-aware system engineering challenges and techniques applied during the most common development stages; C) Context-aware systems conceptualization.
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks

Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance. However, little is published which parameters and design choices should be evaluated or selected making the correct hyperparameter optimization often a “black art that requires expert experiences” (Snoek et al., 2012). In this paper, we evaluate the importance of different network design choices and hyperparameters for five common linguistic sequence tagging tasks (POS, Chunking, NER, Entity Recognition, and Event Detection). We evaluated over 50.000 different setups and found, that some parameters, like the pre-trained word embeddings or the last layer of the network, have a large impact on the performance, while other parameters, for example the number of LSTM layers or the number of recurrent units, are of minor importance. We give a recommendation on a configuration that performs well among different tasks. The optimized implementation of our BiLSTM-CRF architecture is publicly  This publication explains in detail the experimental setup and discusses the results. A condensed version of this paper was presented at EMNLP 2017 (Reimers and Gurevych, 2017).2
Higher education student behaviors in spreading fake news on social media: A case of LINE group

LINE mobile application has group feature that can connect us with numerous other groups. With the recurring spread of fake news in recent time, it provides opportunities for higher education students to be able to receive and spread the fake news through LINE group. The purpose of this research: first, to show college students' activities and the intention of awareness to receive fake news by users. Second, to measure the number of college student groups with awareness of spread fake news. Third, to investigate whether Higher education students with many groups can make or modify fake news. There are 15 questions using a Likert scale and 5 multiple choice questions which are shown to higher education students and presented by using statistical descriptive explanatory analysis for this research. The result of this research is higher education students have many groups and this does not affect the number of fake news because Higher education students need to do advanced research on the news and see if the news has a reliable source. The number of groups in the LINE mobile application imposes no effect on the spread of fake news.
Social presence, trust, and social commerce purchase intention: An empirical research

Lacking the presence of human and social elements is claimed one major weakness that is hindering the growth of e-commerce. The emergence of social commerce (SC) might help ameliorate this situation. Social commerce is a new evolution of e-commerce that combines the commercial and social activities by deploying social technologies into e-commerce sites. Social commerce reintroduces the social aspect of shopping to e-commerce, increasing the degree of social presences in online environment. Drawing upon the social presence theory, this study theorizes the nature of social aspect in online SC marketplace by proposing a set of three social presence variables. These variables are then hypothesized to have positive impacts on trusting beliefs which in turn result in online purchase behaviors. The research model is examined via data collected from a typical ecommerce site in China. Our findings suggest that social presence factors grounded in social technologies contribute significantly to the building of the trustworthy online exchanging relationships. In doing so, this paper confirms the positive role of social aspect in shaping online purchase behaviors, providing a theoretical evidence for the fusion of social and commercial activities. Finally, this paper introduces a new perspective of e-commerce and calls more attention to this new phenomenon.
A study of the impact of social media on consumers

Social media have provided new opportunities to consumers to engage in social interaction on the internet. Consumers use social media, such as online communities, to generate content and to network with other users. The study of social media can also identify the advantages to be gained by business. A multidisciplinary model, building on the technology acceptance model and relevant literature on trust and social media, has been devised. The model has been validated by SEM-PLS, demonstrating the role of social media in the development of e-commerce into social commerce. The data emerging from a survey show how social media facilitate the social interaction of consumers, leading to increased trust and intention to buy. The results also show that trust has a significant direct effect on intention to buy. The perceived usefulness (PU) of a site is also identified as a contributory factor. At the end of the paper, the author discusses the results, along with implications, limitations and recommended future research directions.
Fixed Covering Polynomial for Friendship Graph and its Algorithm

The fixed Cover Polynomial of a graph G of order n has been already introduced in . It is defined as the polynomial C (G, x) = V C   c | | (G) i i= (G) (G, i)x , where C (G, i) is the number of fixed vertex covering sets of G of size i and (G) is the fixed covering number of G. In this paper, we found the fixed covering sets and fixed covering polynomial of the Friendship graphs Fn. Also we exhibited the fixed covering polynomial of the graph Kn K1 with an illustration. An introduction to obtain algorithm for the fixed covering polynomial is initiated.
The Business Perspective on Cloud Computing - A Literature Review of Research on Cloud Computing

This literature review synthesized the existing research on cloud computing from a business perspective by investigating 60 sources and integrates their results in order to offer an overview about the existing body of knowledge. Using an established framework our results are structured according to the four dimensions following: cloud computing characteristics, adoption determinants, governance mechanisms, and business impact. This work reveals a shifting focus from technological aspects to a broader understanding of cloud computing as a new IT delivery model. There is a growing consensus about its characteristics and design principles. Unfortunately, research on factors driving or inhibiting the adoption of cloud services, as well as research investigating its business impact empirically, is still limited. This may be attributed to cloud computing being a rather recent research topic. Research on structures, processes and employee qualification to govern cloud services is at an early stage as well.