nils-herrmann/clean_scirepeval huggingface dataset:
Distributed Optimal Energy Management in Microgrids

Energy management in microgrids is typically formulated as a nonlinear optimization problem. Solving it in a centralized manner does not only require high computational capabilities at the microgrid central controller (MGCC), but may also infringe customer privacy. Existing distributed approaches, on the other hand, assume that all generations and loads are connected to one bus, and ignore the underlying power distribution network and the associated power flow and system operational constraints. Consequently, the schedules produced by those algorithms may violate those constraints and thus are not feasible in practice. Therefore, the focus of this paper is on the design of a distributed energy management strategy (EMS) for the optimal operation of microgrids with consideration of the distribution network and the associated constraints. Specifically, we formulate microgrid energy management as an optimal power flow problem, and propose a distributed EMS where the MGCC and the local controllers jointly compute an optimal schedule. We also provide an implementation of the proposed distributed EMS based on IEC 61850. As one demonstration, we apply the proposed distributed EMS to a real microgrid in Guangdong Province, China, consisting of photovoltaics, wind turbines, diesel generators, and a battery energy storage system. The simulation results demonstrate the effectiveness and fast convergence of the proposed distributed EMS.
Automatic classification of archaeological pottery sherds

This article presents a novel technique for automatic archaeological sherd classification. Sherds that are found in the field usually have little to no visible textual information such as symbols, graphs, or marks on them. This makes manual classification an extremely difficult and time-consuming task for conservators and archaeologists. For a bunch of sherds found in the field, an expert identifies different classes and indicates at least one representative sherd for each class (training sample). The proposed technique uses the representative sherds in order to correctly classify the remaining sherds. For each sherd, local features based on color and texture information are extracted and are then transformed into a global vector that describes the whole sherd image, using a new bag of words technique. Finally, a feature selection algorithm is applied that locates features with high discriminative power. Extensive experiments were performed in order to verify the effectiveness of the proposed technique and show very promising results.
An analysis of social network-based Sybil defenses

Recently, there has been much excitement in the research community over using social networks to mitigate multiple identity, or Sybil, attacks. A number of schemes have been proposed, but they differ greatly in the algorithms they use and in the networks upon which they are evaluated. As a result, the research community lacks a clear understanding of how these schemes compare against each other, how well they would work on real-world social networks with different structural properties, or whether there exist other (potentially better) ways of Sybil defense.
 In this paper, we show that, despite their considerable differences, existing Sybil defense schemes work by detecting local communities (i.e., clusters of nodes more tightly knit than the rest of the graph) around a trusted node. Our finding has important implications for both existing and future designs of Sybil defense schemes. First, we show that there is an opportunity to leverage the substantial amount of prior work on general community detection algorithms in order to defend against Sybils. Second, our analysis reveals the fundamental limits of current social network-based Sybil defenses: We demonstrate that networks with well-defined community structure are inherently more vulnerable to Sybil attacks, and that, in such networks, Sybils can carefully target their links in order make their attacks more effective.
18 Critical Discourse Analysis

Critical discourse analysis (CDA) is a type of discourse analytical research that primarily studies the way social power abuse, dominance, and inequality are enacted, reproduced, and resisted by text and talk in the social and political context. With such dissident research, critical discourse analysts take explicit position, and thus want to understand, expose, and ultimately resist social inequality. Some of the tenets of CDA can already be found in the critical theory of the Frankfurt School before the Second World War (Agger 1992b; Rasmussen 1996). Its current focus on language and discourse was initiated with the "critical linguistics" that emerged (mostly in the UK and Australia) at the end of the 1970s (Fowler et al. 1979; see also Mey 1985). CDA has also counterparts in "critical" developments in sociolinguistics, psychology, and the social sciences, some already dating back to the As is the case in these neighboring disciplines, CDA may be seen as a reaction against the dominant formal (often "asocial" or "uncritical") paradigms of the 1960s and 1970s. CDA is not so much a direction, school, or specialization next to the many other "approaches" in discourse studies. Rather, it aims to offer a different "mode" or "perspective" of theorizing, analysis, and application throughout the whole field. We may find a more or less critical perspective in such diverse areas as pragmatics, conversation analysis, narrative analysis, rhetoric, stylistics, sociolinguistics, ethno-graphy, or media analysis, among others. Crucial for critical discourse analysts is the explicit awareness of their role in society. Continuing a tradition that rejects the possibility of a "value-free" science, they argue that science, and especially scholarly discourse, are inherently part of and influenced by social structure, and produced in social interaction. Instead of denying or ignoring such a relation between scholarship and society, they plead that such relations be studied and accounted for in their own right, and that scholarly practices
IOT INTEGRATED BIG DATA ANALYTICS FRAMEWORK FOR SMART BUILDING SYSTEMS

A smart building is created when it is engineered, delivered and operated smart. The Internet of Things (IoT) is advancing a new breed of smart buildings enables operational systems that deliver more accurate and useful information for improving operations and providing the best experiences for tenants. Big Data Analytics framework analyze building data to uncover new insight capable of driving real value and greater performance. Internet of Things technologies enhance the situational awareness or “smartness” of service providers and consumers alike. There is a need for an integrated IoT Big Data Analytics framework to fill the research gap in the Big Data Analytics domain. This paper also presents a novel approach for mobile phone centric observation applied to indoor localization for smart buildings. The applicability of the framework of this paper is demonstrated with the help of a scenario involving the analysis of real-time smart building data for automatically managing the oxygen level, luminosity and smoke/hazardous gases in different parts of the smart building. Lighting control in smart buildings and homes can be automated by having computer controlled lights and blinds along with illumination sensors that are distributed in the building. This paper gives an overview of an approach that algorithmically sets up the control system that can automate any building without custom programming. The resulting system controls blinds to ensure even lighting and also adds artificial illumination to ensure light coverage remains adequate at all times of the day, adjusting for weather and seasons. The key contribution of this paper is the complex integration of Big Data Analytics and IoT for addressing the large volume and velocity challenge of real-time data in the smart building domain.
Marriage and cohabitation in the United States: A statistical portrait based on cycle 6 (2002) of the National Survey of Family Growth.

This report provides an overview of marital and cohabiting relationships in the United States among men and women aged 15-44 in 2002, by a variety of characteristics. National estimates are provided that highlight formal and informal marital status, previous experience with marriage and cohabitation, the sequencing of marriage and cohabitation, and the stability of cohabitations and marriages.



The analyses presented in this report are based on a nationally representative sample of 12,571 men and women aged 15-44 living in households in the United States in 2002, based on the National Survey of Family Growth, Cycle 6.



Over 40% of men and women aged 15-44 were currently married at the date of interview, compared with about 9% who were currently cohabiting. Men and women were, however, likely to cohabit prior to becoming married. Marriages were longer lasting than cohabiting unions; about 78% of marriages lasted 5 years or more, compared with less than 30% of cohabitations. Cohabitations were shorter-lived than marriages in part because about half of cohabitations transitioned to marriage within 3 years. Variations--often large variations-in marital and cohabiting relationships and durations were found by race and Hispanic origin, education, family background, and other factors.
Desktop-to-Mobile Web Adaptation through Customizable Two-Dimensional Semantic Redesign

In this paper we present a novel method for desktop-to-mobile adaptation. The solution also supports end-users in customizing multi-device ubiquitous user interfaces. In particular, we describe an algorithm and the corresponding tool support to perform desktop-to-mobile adaptation by exploiting logical user interface descriptions able to capture interaction semantic information indicating the purpose of the interface elements. We also compare our solution with existing tools for similar goals.
Understanding and prediction of mobile application usage for smart phones

It is becoming harder to find an app on one's smart phone due to the increasing number of apps available and installed on smart phones today. We collect sensory data including app use from smart phones, to perform a comprehensive analysis of the context related to mobile app use, and build prediction models that calculate the probability of an app in the current context. Based on these models, we developed a dynamic home screen application that presents icons for the most probable apps on the main screen of the phone and highlights the most probable one. Our models outperformed other strategies, and, in particular, improved prediction accuracy by 8% over Most Frequently Used from 79.8% to 87.8% (for 9 candidate apps). Also, we found that the dynamic home screen improved accessibility to apps on the phone, compared to the conventional static home screen in terms of accuracy, required touch input and app selection time.
Personality and Information Behavior in Web Search

Participants had to find information for a specific subjects (Radio waves and brain tumours). They had to mark relevant web documents as favorites and store them in a “favorites” folder in their browser. For each task, participants had a time limit of 15 minutes. For the first task participants were supposed to find five general pages, PDFs etc. and store them. In the second task they had to explicitly look for scientific literature and mark three articles.  Tasks were recorded using screen capture software  With the help of a click-counter plugin and analysis of the videos quantitative data per Task was collected  Variables are e. g. duration, number of clicks, number of pages, number of queries (also per minute), average/median/max rank, browser interaction  Further data concerning the personal judgement of the subject’s search was collected with Post-TaskQuestionnaires  The Big 5 Traits were operationalized with the B5T, a German questionnaire, which was presented to the participants at the end 2
The safe zone for placement of chin implants.

Alloplastic chin augmentation requires the surgeon to predict the location of the mental foramen and the origin of the mentalis muscle to avoid the postoperative sequelae lower lip parasthesia, lower lip incompetence, or chin ptosis. The authors define a safe zone of dissection along the inferior border of the mandible for placement of alloplastic chin implants.



Fourteen fresh cadaveric hemifaces were dissected with the aid of loupe magnification. Previously described anatomic landmarks were used to identify the origin of the mentalis muscle and the location of the mental foramen along the alveolar ridge of the mandible. Vertical distances were then measured from the mandibular border to the inferior aspect of the mentalis muscle origin and the lower edge of the mental foramen to construct the zone of safe dissection.



The mentalis was identified as a fan-shaped muscle originating from the alveolar process below the incisors roots and inserting into the chin just below the labiomental sulcus. The mental foramen was located most commonly below the roots of the first and second premolars or in the space between the roots. The mentalis origin and the mental foramen were 1.8 ± 0.3 cm and 1.5 ± 0.2 cm cephalad to the inferior edge of the mandible, respectively. These distances define the borders of a safe zone above the mandibular border.



A safe zone of dissection for alloplastic chin augmentation is identified. This study is applicable to implant placement through a submental or an intraoral incision. This safe zone is also useful for reconstructive or orthognathic mandible procedures.
Investigation of a Ka-band Luneburg lens made of a glide-symmetric holey structure

A Ka-band 2D flat-profiled Luneburg lens antenna implemented with a glide-symmetric holey structure is presented. The required refractive index for the lens design has been investigated via an analysis of the hole depth and the gap between the two metallic layers constituting the lens. The final unit cell is described and applied to create the complete metasurface Luneburg lens showing that a plane wave is obtained when feeding at an opposite arbitrary point with a discrete source.
Auto Analysis of Customer Feedback using CNN and GRU Network

Analyzing customer feedback is the best way to channelize the data into new marketing strategies that benefit entrepreneurs as well as customers. Therefore an automated system which can analyze the customer behavior is in great demand. Users may write feedbacks in any language, and hence mining appropriate information often becomes intractable. Especially in a traditional feature-based supervised model, it is difficult to build a generic system as one has to understand the concerned language for finding the relevant features. In order to overcome this, we propose deep Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) based approaches that do not require handcrafting of features. We evaluate these techniques for analyzing customer feedback sentences on four languages, namely English, French, Japanese and Spanish. Our empirical analysis shows that our models perform well in all the four languages on the setups of IJCNLP Shared Task on Customer Feedback Analysis. Our model achieved the second rank in French, with an accuracy of 71.75% and third ranks for all the other languages.
Algorithm of chest wall keloid treatment

Keloids are common in the Asian population. Multiple or huge keloids can appear on the chest wall because of its tendency to develop acne, sebaceous cyst, etc. It is difficult to find an ideal treatment for keloids in this area due to the limit of local soft tissues and higher recurrence rate. This study aims at establishing an individualized protocol that could be easily applied according to the size and number of chest wall keloids.A total of 445 patients received various methods (4 protocols) of treatment in our department from September 2006 to September 2012 according to the size and number of their chest wall keloids. All of the patients received adjuvant radiotherapy in our hospital. Patient and Observer Scar Assessment Scale (POSAS) was used to assess the treatment effect by both doctors and patients. With mean follow-up time of 13 months (range: 6-18 months), 362 patients participated in the assessment of POSAS with doctors.Both the doctors and the patients themselves used POSAS to evaluate the treatment effect. The recurrence rate was 0.83%. There was an obvious significant difference (P < 0.001) between the before-surgery score and the after-surgery score from both doctors and patients, indicating that both doctors and patients were satisfied with the treatment effect.Our preliminary clinical result indicates that good clinical results could be achieved by choosing the proper method in this algorithm for Chinese patients with chest wall keloids. This algorithm could play a guiding role for surgeons when dealing with chest wall keloid treatment.
User-led Innovation and the video game industry

In spite of the recent acknowledgements of “open source development” type of user-led innovation, our understanding is still limited as to its applicability and benefit. Particularly, from the viewpoint of firms making consumer goods, it remains unclear whether they can take advantage of peer-to-peer interaction and collaboration among consumers in the Internet era. Through an analysis of the video game industry, this article highlights its potential and limitations. The current hype of user-led innovation entails the risk of the concept’s being over-applied in terms of its applicability across industries and assumption of benefits. We argue that user-led innovation emerged out of not only technology-specific environment, but also culturally-specific context of industries. Our analysis shows that user-led innovation in its applications is likely to be constrained by the cultural context among other obstacles, thereby limiting its usefulness to a set of industries with particular characteristics. .
A Regulation Scheme Based on the Ciphertext-Policy Hierarchical Attribute-Based Encryption in Bitcoin System

In Bitcoin financial system, a user’s privacy is supposed to be protected by means of anonymity. However, the anonymity makes illegal trades possible because nobody is able to reveal the real identities of the illegal users. In this paper, we propose a regulation scheme based on the ciphertext-policy hierarchical attribute-based encryption (CP-HABE). In the scheme, users’ identities are encrypted by using access policy and are contained in their transaction. A type of user is defined as the dependable regulation node, which is responsible for the regulation of transactions and encrypted identities. A new signature algorithm instead of the elliptic curve signature is adopted to generate wallet key pairs, this establishes a connection between wallet addresses and encrypted identities. When a transaction is doubted to involve illegal activities, the authorized regulation nodes are capable of revealing the users’ real identities and add the illegal identities to a public blacklist. Our system is based on a new CP-HABE scheme which is proved to be secure against chosen-plaintext attack in the standard model under the Bilinear Diffie–Hellman Exponent assumption. Finally, we give a performance analysis of our system. The proposed regulation system can reveal criminals’ identities undertaking illegal activities.
Impact of task switching and work interruptions on software development processes

Software developers often work on multiple projects and tasks throughout a work day, which may affect their productivity and quality of work. Knowing how working on several projects at a time affects productivity can improve cost and schedule estimations. It also can provide additional insights for better work scheduling and the development process. We want to achieve a better productivity without losing the benefits of work interruptions and multitasking for developers involved in the process. To understand how the development process can be improved, first, we identify work interruptions that mostly have a negative effect on productivity, second, we need to quantitatively evaluate impact of multitasking (task switching, work context switching) and work interruptions on productivity. In this research we study cross-project multitasking among the developers working on multiple projects in an educational setting. We propose a way to evaluate the number of cross-project interruptions among software developers using self-reported work logs. This paper describes the research that found: a) software developers involved in two or more projects on average spend 17% of their development effort on cross-project interruptions, b) the amount of effort spent on interruptions is overestimated by the G. Weinberg’s heuristic, c) the correlation between the number of projects and effort spent by developers on cross-project interruptions is relatively weak, and d) there is strong correlation between the number of projects and the number of interruptions developers reported.
Clifford Algebras and Possible Kinematics

We review Bacry and Lévy-Leblond’s work on possible kinematics as applied to 2-dimensional spacetimes, as well as the nine types of 2-dimensional Cayley–Klein geometries, illustrating how the Cayley–Klein geometries give homogeneous spacetimes for all but one of the kinematical groups. We then construct a two-parameter family of Clifford algebras that give a unified framework for representing both the Lie algebras as well as the kinematical groups, showing that these groups are true rotation groups. In addition we give conformal models for these spacetimes.
Short- versus long-term duration of dual-antiplatelet therapy after coronary stenting: a randomized multicenter trial.

The optimal duration of dual-antiplatelet therapy and the risk-benefit ratio for long-term dual-antiplatelet therapy after coronary stenting remain poorly defined. We evaluated the impact of up to 6 versus 24 months of dual-antiplatelet therapy in a broad all-comers patient population receiving a balanced proportion of Food and Drug Administration-approved drug-eluting or bare-metal stents.



We randomly assigned 2013 patients to receive bare-metal, zotarolimus-eluting, paclitaxel-eluting, or everolimus-eluting stent implantation. At 30 days, patients in each stent group were randomly allocated to receive up to 6 or 24 months of clopidogrel therapy in addition to aspirin. The primary end point was a composite of death of any cause, myocardial infarction, or cerebrovascular accident. The cumulative risk of the primary outcome at 2 years was 10.1% with 24-month dual-antiplatelet therapy compared with 10.0% with 6-month dual-antiplatelet therapy (hazard ratio, 0.98; 95% confidence interval, 0.74-1.29; P=0.91). The individual risks of death, myocardial infarction, cerebrovascular accident, or stent thrombosis did not differ between the study groups; however, there was a consistently greater risk of hemorrhage in the 24-month clopidogrel group according to all prespecified bleeding definitions, including the recently proposed Bleeding Academic Research Consortium classification.



A regimen of 24 months of clopidogrel therapy in patients who had received a balanced mixture of drug-eluting or bare-metal stents was not significantly more effective than a 6-month clopidogrel regimen in reducing the composite of death due to any cause, myocardial infarction, or cerebrovascular accident.