nils-herrmann/clean_scirepeval huggingface dataset:
The Ties that Blog : Examining the Relationship Between Social Ties and Continued Participation in the Wallop Weblogging System

Are people who remain active as webloggers more socially connected to other users? How are the number and nature of social ties related to people's willingness to continue contributing content to a weblog? This study uses longitudinal data taken from Wallop, a weblogging system developed by Microsoft Research, to explore patterns of user activity. In its year long operation Wallop hosted a naturally occurring opportunity for cultural comparison, as it developed a majority Chinese language using population (despite the English language focus of the system). This allows us to consider whether or not language communities have different social network characteristics that vary along different activity levels. Logistic regression models and network visualizations reveal two key findings. The first is that not all ties are equal. Although a count of incoming comments appears to be a significant predictor of retention, it loses its predictive strength when strong ties created by repeated, reciprocal interaction and ties from other dedicated webloggers are considered. Second, the higher rate of retention among the Chinese language users is partly explained by that population’s greater ability to draw in participants with pre-existing social ties. We conclude with considerations for weblogs and directions for future research.
Novel Radiation-Hardened-by-Design (RHBD) 12T Memory Cell for Aerospace Applications in Nanoscale CMOS Technology

In this paper, a novel radiation-hardened-by-design (RHBD) 12T memory cell is proposed to tolerate single node upset and multiple-node upset based on upset physical mechanism behind soft errors together with reasonable layout-topology. The verification results obtained confirm that the proposed 12T cell can provide a good radiation robustness. Compared with 13T cell, the increased area, power, read/write access time overheads of the proposed 12T cell are −18.9%, −23.8%, and 171.6%/−50.0%, respectively. Moreover, its hold static noise margin is 986.2 mV which is higher than that of 13T cell. This means that the proposed 12T cell also has higher stability when it provides fault tolerance capability.
Effectiveness of Fitting Pressure Garments for Minor and Moderate Burn Patients: A Literature Review

Burn reconstruction and rehabilitation are difficult and time-consuming processes; a burn patient must endure a long rehabilitation process to treat burn scars. The continued proliferation of scars and anxiety engender considerable difficulties for patients. A burn scar contracture is like a burned rubber band; the skin is never restored to its original flexibility.
 Wearing pressure garments (PGs) is uncomfortable and challenging for patients, but constant adherence is necessary. To improve adherence, precise knowledge about the advantages and disadvantages of PG therapy (PGT) is necessary.
 Methods: This review examined experimental studies that have applied PGT on patients with varicose veins and burns, as well as healthy human controls. The participants answered questionnaires to determine the best or "ideal" pressure. All scars were treated under a scar management program and followed up by the treating hospital and scanned by ultrasound. Scars were assessed using the Tissue Ultrasound Palpation System (TUPS), as well as the Vancouver Scar Scale (VSS) for rating scar thickness, pliability, pigmentation, and vascularity. Scar thickness and color were objectively measured using the Pliance X system and Laser Doppler Imaging (LDI).
  In general, the application of 15-25 mmHg pressure is most commonly used in clinical practice. Minor and moderate burn patients could wear PGs in the very beginning of the recovery process. According to guidelines, closed scars should always be reevaluated 6 months after the burn to determine whether additional scar management interventions are required or whether preventive therapy can be terminated.
Dangerous Skills : Understanding and Mitigating Security Risks of Voice-Controlled Third-Party Functions on Virtual Personal Assistant Systems

Virtual personal assistants (VPA) (e.g., Amazon Alexa and Google Assistant) today mostly rely on the voice channel to communicate with their users, which however is known to be vulnerable, lacking proper authentication (from the user to the VPA). A new authentication challenge, from the VPA service to the user, has emerged with the rapid growth of the VPA ecosystem, which allows a third party to publish a function (called skill) for the service and therefore can be exploited to spread malicious skills to a large audience during their interactions with smart speakers like Amazon Echo and Google Home. In this paper, we report a study that concludes such remote, largescale attacks are indeed realistic. We discovered two new attacks: voice squatting in which the adversary exploits the way a skill is invoked (e.g., “open capital one”), using a malicious skill with a similarly pronounced name (e.g., “capital won”) or a paraphrased name (e.g., “capital one please”) to hijack the voice command meant for a legitimate skill (e.g., “capital one”), and voice masquerading in which a malicious skill impersonates the VPA service or a legitimate skill during the user’s conversation with the service to steal her personal information. These attacks aim at the way VPAs work or the user’s misconceptions about their functionalities, and are found to pose a realistic threat by our experiments (including user studies and real-world deployments) on Amazon Echo and Google Home. The significance of our findings has already been acknowledged by Amazon and Google, and further evidenced by the risky skills found on Alexa and Google markets by the new squatting detector we built. We further developed a technique that automatically captures an ongoing masquerading attack and demonstrated its efficacy.
Unhealthy region of citrus leaf detection using image processing techniques

Producing agricultural products are difficult task as the plant comes to an attack from various micro-organisms, pests and bacterial diseases. The symptoms of the attacks are generally distinguished through the leaves, steams or fruit inspection. The present paper discusses the image processing techniques used in performing early detection of plant diseases through leaf features inspection. The objective of this work is to implement image analysis and classification techniques for extraction and classification of leaf diseases. Leaf image is captured and then processed to determine the status of each plant. Proposed framework is model into four parts image preprocessing including RGB to different color space conversion, image enhancement; segment the region of interest using K-mean clustering for statistical usage to determine the defect and severity areas of plant leaves, feature extraction and classification. texture feature extraction using statistical GLCM and color feature by means of mean values. Finally classification achieved using SVM. This technique will ensure that chemicals only applied when plant leaves are detected to be effected with the disease.
Wireless Sensor Network for Precise Agriculture Monitoring

Precision Agriculture Monitor System (PAMS) is an intelligent system which can monitor the agricultural environments of crops and provides service to farmers. PAMS based on the wireless sensor network (WSN) technique attracts increasing attention in recent years. The purpose of such systems is to improve the outputs of crops by means of managing and monitoring the growth period. This paper presents the design of a WSN for PAMS, shares our real-world experience, and discusses the research and engineering challenges in implementation and deployments.
Discipline and practices of TDD: (test driven development)

This panel brings together practitioners with experience in Agile and XP methodologies to discuss the approaches and benefits of applying Test Driven Development (TDD). The goal of TDD is clean code that works. The mantra of TDD is: write a test; make it run; and make it right. Open questions to be addressed by the panel include: - How are TDD approaches to be applied to databases, GUIs, and distributed systems? What are the quantitative benchmarks that can demonstrate the value of TDD, and what are the best approaches to solve the ubiquitous issue of scalability.
GIS-Based Planning and Modeling for Renewable Energy: Challenges and Future Research Avenues

In the face of the broad political call for an ―energy turnaround‖, we are currently witnessing three essential trends with regard to energy infrastructure planning, energy generation and storage: from planned production towards fluctuating production on the basis of renewable energy sources, from centralized generation towards decentralized generation and from expensive energy carriers towards cost-free energy carriers. These changes necessitate considerable modifications of the energy infrastructure. Even though most of these modifications are inherently motivated by geospatial questions and challenges, the integration of energy system models and Geographic Information Systems (GIS) is still in its infancy. This paper analyzes the shortcomings of previous approaches in using GIS in renewable energy-related projects, extracts distinct challenges from these previous efforts and, finally, defines a set of core future research avenues for GIS-based energy infrastructure planning with a focus on the use of renewable energy. These future OPEN ACCESS ISPRS Int.  2014, 3 663 research avenues comprise the availability base data and their ―geospatial awareness‖, the development of a generic and unified data model, the usage of volunteered geographic information (VGI) and crowdsourced data in analysis processes, the integration of 3D building models and 3D data analysis, the incorporation of network topologies into GIS, the harmonization of the heterogeneous views on aggregation issues in the fields of energy and GIS, fine-grained energy demand estimation from freely-available data sources, decentralized storage facility planning, the investigation of GIS-based public participation mechanisms, the transition from purely structural to operational planning, data privacy aspects and, finally, the development of a new dynamic power market design.
Real User Evaluation of Spoken Dialogue Systems Using Amazon Mechanical Turk

This paper describes a framework for evaluation of spoken dialogue systems. Typically, evaluation of dialogue systems is performed in a controlled test environment with carefully selected and instructed users. However, this approach is very demanding. An alternative is to recruit a large group of users who evaluate the dialogue systems in a remote setting under virtually no supervision. Crowdsourcing technology, for example Amazon Mechanical Turk (AMT), provides an efficient way of recruiting subjects. This paper describes an evaluation framework for spoken dialogue systems using AMT users and compares the obtained results with a recent trial in which the systems were tested by locally recruited users. The results suggest that the use of crowdsourcing technology is feasible and it can provide reliable results.
Multiple Operating Points Model-Based Control of a Two-Wheeled Inverted Pendulum Mobile Robot

Literature review shows that, most of the linear controllers proposed for control of a Two-Wheeled Inverted Pendulum (TWIP) mobile robot are based on dynamic models that are linearized around a single operating point. Thus, this paper presents the controller design for a TWIP mobile robot based on its multiple operating point linearized model. The nonlinear dynamical equations of motion of the TWIP mobile robot were firstly obtained. The nonlinear equations were then linearized around different operating points resulting in a number of linear models with each represented a local region of the original nonlinear system. Based on the multiple operating points linearized models a state feedback control scheme was proposed using pole placement technique for balancing and tracking control of the TWIP mobile robot. The performance of the proposed controller investigated in terms of reference command tracking, parameter variation and external disturbance rejection via simulations. The simulation result shows the effectiveness of the proposed control scheme.
Steerable interfaces for pervasive computing spaces

This paper introduces a new class of interactive interfaces that can be moved around to appear on ordinary objects and surfaces anywhere in a space. By dynamically adapting the form, function, and location of an interface to suit the context of the user, such steerable interfaces have the potential to offer radically new and powerful styles of interaction in intelligent pervasive computing spaces. We propose defining characteristics of steerable interfaces and present the first steerable interface system that combines projection, gesture recognition, user tracking, environment modeling and geometric reasoning components within a system architecture. Our work suggests that there is great promise and rich potential for further research on steerable interfaces.
Linking online news and social media

Much of what is discussed in social media is inspired by events in the news and, vice versa, social media provide us with a handle on the impact of news events. We address the following linking task: given a news article, find social media utterances that implicitly reference it. We follow a three-step approach: we derive multiple query models from a given source news article, which are then used to retrieve utterances from a target social media index, resulting in multiple ranked lists that we then merge using data fusion techniques. Query models are created by exploiting the structure of the source article and by using explicitly linked social media utterances that discuss the source article. To combat query drift resulting from the large volume of text, either in the source news article itself or in social media utterances explicitly linked to it, we introduce a graph-based method for selecting discriminative terms.
 For our experimental evaluation, we use data from Twitter, Digg, Delicious, the New York Times Community, Wikipedia, and the blogosphere to generate query models. We show that different query models, based on different data sources, provide complementary information and manage to retrieve different social media utterances from our target index. As a consequence, data fusion methods manage to significantly boost retrieval performance over individual approaches. Our graph-based term selection method is shown to help improve both effectiveness and efficiency.
Continuity Editing for 3D Animation

We describe an optimization-based approach for automatically creating well-edited movies from a 3D animation. While previous work has mostly focused on the problem of placing cameras to produce nice-looking views of the action, the problem of cutting and pasting shots from all available cameras has never been addressed extensively. In this paper, we review the main causes of editing errors in literature and propose an editing model relying on a minimization of such errors. We make a plausible semi-Markov assumption, resulting in a dynamic programming solution which is computationally efficient. We also show that our method can generate movies with different editing rhythms and validate the results through a user study. Combined with state-of-the-art cinematography, our approach therefore promises to significantly extend the expressiveness and naturalness of virtual movie-making.
A personalized adaptive e-learning approach based on semantic web technology

Recent developments in semantic web technologies heightened the need for online adaptive learning environment. Adaptive learning is an important research topic in the field of webbased systems as there are no fixed learning paths which are appropriate for all learners. However, most studies in this field have only focused on learning styles and habits of learners. Far too little attention has been paid on understanding the ability of learners. Therefore, it is becoming increasingly difficult to ignore adaptation in the field of e-learning systems. Many researchers are adopting semantic web technologies to find new ways for designing adaptive learning systems based on describing knowledge using ontological models. Ontologies have the potential to design content and learner models required to create adaptive e-learning systems based on various characteristics of learners. The aim of this paper is to present an ontology-based approach to develop adaptive e-learning system based on the design of semantic content, learner and domain models to tailor the teaching process for individual learner’s needs. The proposed new adaptive e-learning has the ability to support personalization based on learner’s ability, learning style, preferences and levels of knowledge. In our approach the ontological user profile is updated based on achieved learner’s abilities.
Hybrid Taguchi-genetic algorithm for global numerical optimization

In this paper, a hybrid Taguchi-genetic algorithm (HTGA) is proposed to solve global numerical optimization problems with continuous variables. The HTGA combines the traditional genetic algorithm (TGA), which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimum offspring. The Taguchi method is inserted between crossover and mutation operations of a TGA. Then, the systematic reasoning ability of the Taguchi method is incorporated in the crossover operations to select the better genes to achieve crossover, and consequently, enhance the genetic algorithm. Therefore, the HTGA can be more robust, statistically sound, and quickly convergent. The proposed HTGA is effectively applied to solve 15 benchmark problems of global optimization with 30 or 100 dimensions and very large numbers of local minima. The computational experiments show that the proposed HTGA not only can find optimal or close-to-optimal solutions but also can obtain both better and more robust results than the existing algorithm reported recently in the literature.
Gain enhancement of dual-band antenna using square loop FSS

This paper presents a high gain dual-band microstrip antenna. The antenna operates in 9.25 and 11 GHz (X-band). High gain of antenna is achieved by employing a FSS as superstrate which is formed by the 5 × 5 array of square loops. The square loop FSS resonance is varied and its influence on the antenna behavior is analyzed. Analyze of the simulated antenna gain and return loss are shown. The gain improvement depends on the resonance of the FSS superstrate. The higher gain obtained is 6 dB at 11 GHz while maintaining a good matching level for the first resonance.
What are the Gaps in Mobile Patient Portal? Mining Users Feedback Using Topic Modeling

Patient portals are positioned as a central component of patient engagement through the potential to change the physician-patient relationship and enable chronic disease self-management. In this article, we extend the existing literature by discovering design gaps for patient portals from a systematic analysis of negative users’ feedback from the actual use of patient portals. Specifically, we adopt topic modeling approach, LDA algorithm, to discover design gaps from online low rating user reviews of a common mobile patient portal, EPIC’s mychart. To validate the extracted gaps, we compared the results of LDA analysis with that of human analysis. Overall, the results revealed opportunities to improve collaboration and to enhance the design of portals intended for patientcentered care.
Long-range GPS-denied aerial inertial navigation with LIDAR localization

Despite significant progress in GPS-denied autonomous flight, long-distance traversals (> 100 km) in the absence of GPS remain elusive. This paper demonstrates a method capable of accurately estimating the aircraft state over a 218 km flight with a final position error of 27 m, 0.012% of the distance traveled. Our technique efficiently captures the full state dynamics of the air vehicle with semi-intermittent global corrections using LIDAR measurements matched against an a priori Digital Elevation Model (DEM). Using an error-state Kalman filter with IMU bias estimation, we are able to maintain a high-certainty state estimate, reducing the computation time to search over a global elevation map. A sub region of the DEM is scanned with the latest LIDAR projection providing a correlation map of landscape symmetry. The optimal position is extracted from the correlation map to produce a position correction that is applied to the state estimate in the filter. This method provides a GPS-denied state estimate for long range drift-free navigation. We demonstrate this method on two flight data sets from a full-sized helicopter, showing significantly longer flight distances over the current state of the art.
A Survey on Online Judge Systems and Their Applications

Online judges are systems designed for the reliable evaluation of algorithm source code submitted by users, which is next compiled and tested in a homogeneous environment. Online judges are becoming popular in various applications. Thus, we would like to review the state of the art for these systems. We classify them according to their principal objectives into systems supporting organization of competitive programming contests, enhancing education and recruitment processes, facilitating the solving of data mining challenges, online compilers and development platforms integrated as components of other custom systems. Moreover, we introduce a formal definition of an online judge system and summarize the common evaluation methodology supported by such systems. Finally, we briefly discuss an Optil.io platform as an example of an online judge system, which has been proposed for the solving of complex optimization problems. We also analyze the competition results conducted using this platform. The competition proved that online judge systems, strengthened by crowdsourcing concepts, can be successfully applied to accurately and efficiently solve complex industrial- and science-driven challenges.
Phytochemical screening and Extraction: A Review

Prashant Tiwari*, Bimlesh Kumar, Mandeep Kaur, Gurpreet Kaur, Harleen Kaur Plants are a source of large amount of drugs comprising to different groups such as antispasmodics, emetics, anti-cancer, antimicrobials etc. A large number of the plants are claimed to possess the antibiotic properties in the traditional system and are also used extensively by the tribal people worldwide. It is now believed that nature has given the cure of every disease in one way or another. Plants have been known to relieve various diseases in Ayurveda. Therefore, the researchers today are emphasizing on evaluation and characterization of various plants and plant constituents against a number of diseases based on their traditional claims of the plants given in Ayurveda. Extraction of the bioactive plant constituents has always been a challenging task for the researchers. In this present review, an attempt has been made to give an overview of certain extractants and extraction processes with their advantages and disadvantages. Department of Pharmaceutical Sciences, Lovely School of Pharmaceutical Sciences, Phagwara, Punjab Date of Submission: 12-01-2011 Date of Acceptance: 22-02-2011 Conflict of interest: Nil Source of support: None