nils-herrmann/clean_scirepeval huggingface dataset:
Photovoltaic DC-Building-Module-Based BIPV System—Concept and Design Considerations

The photovoltaic (PV) modules used in the building-integrated PV (BIPV) system, generally, can be installed in different orientations and angles. Moreover, performance of the PV modules is easy to be affected by partial shadows and mismatch of their electrical parameters. Consequently, the conventional power configurations are difficult to obtain higher energy efficiency and reliability. Some improved power configurations of BIPV system have been presented to solve these problems. The objective of the paper is to give an overview of the existing solutions, and then, proposes an efficient and cost-effective power configuration of BIPV system from the energy conversion and control point of view. The proposed power configuration consists of plenty of PV dc-building module (PV-DCBM) and a centralized inverter. Each PV-DCBM includes a high step-up dc-dc converter integrated with a PV module into an individual electrical device. The PV-DCBMs are parallel connected, and then, connected to a common dc bus. The centralized inverter is connected to the grid. The design criterions, optimum design considerations, and design procedure of the proposed PV-DCBM-based BIPV system are proposed in this paper. The experimental results are presented to verify the validity and feasibility of the novel concept.
The BellKor 2008 Solution to the Netflix Prize

Our RMSE=0.8643 solution is a linear blend of over 100 results. Some of them are new to this year, whereas many others belong to the set that was reported a year ago in our 2007 Progress Prize report . This report is structured accordingly. In Section 2 we detail methods new to this year. In general, our view is that those newer methods deliver a superior performance compared to the methods we used a year ago. Throughout the description of the methods, we highlight the specific predictors that participated in the final blended solution. Nonetheless, the older methods still play a role in the blend, and thus in Section 3 we list those methods repeated from a year ago. Finally, we conclude with general thoughts in Section 4.
Topology management techniques for tolerating node failures in wireless sensor networks: A survey

In wireless sensor networks (WSNs) nodes often operate unattended in a collaborative manner to perform some tasks. In many applications, the network is deployed in harsh environments such as battlefield where the nodes are susceptible to damage. In addition, nodes may fail due to energy depletion and breakdown in the onboard electronics. The failure of nodes may leave some areas uncovered and degrade the fidelity of the collected data. However, the most serious consequence is when the network gets partitioned into disjoint segments. Losing network connectivity has a very negative effect on the applications since it prevents data exchange and hinders coordination among some nodes. Therefore, restoring the overall network connectivity is very crucial. Given the resource-constrained setup, the recovery should impose the least overhead and performance impact. This paper focuses on network topology management techniques for tolerating/handling node failures in WSNs. Two broad categories based on reactive and proactive methods have been identified for classifying the existing techniques. Considering these categories, a thorough analysis and comparison of all the recent works have been provided. Finally, the paper is concluded by outlining open issues that warrant additional research.
Optical proximity correction (OPC)-friendly maze routing

As the technology migrates into the deep submicron manufacturing(DSM) era, the critical dimension of the circuits is getting smaller than the lithographic wavelength. The unavoidable light diffraction phenomena in the sub-wavelength technologies have become one of the major factors in the yield rate. Optical proximity correction (OPC) is one of the methods adopted to compensate for the light diffraction effect as a post layout process.However, the process is time-consuming and the results are still limited by the original layout quality. In this paper, we propose a maze routing method that considers the optical effect in the routing algorithm. By utilizing the symmetrical property of the optical system, the light diffraction is efficiently calculated and stored in tables. The costs that guide the router to minimize the optical interferences are obtained from these look-up tables. The problem is first formulated as a constrained maze routing problem, then it is shown to be a multiple constrained shortest path problem. Based on the Lagrangian relaxation method, an effective algorithm is designed to solve the problem.
Threats of Online Social Networks

In the recent years, we have witnessed a dramatic rise in popularity of online social networking services, with several Social Network Sites (SNSs) such as Myspace, Facebook, Blogger, You Tube, Yahoo! Groups etc are now among the most visited websites globally. However, since such forums are relatively easy to access and the users are often not aware of the size and the nature of the audience accessing their profiles, they often reveal more information which is not appropriate to a public forum. As a result, such commercial and social site may often generate a number of privacy and security related threats for the members. This paper highlights the commercial and social benefits of safe and well-informed use of SNSs and emphasizes the most important threats to users of SNSs as well as illustrates the fundamental factors behind these threats. It also presents policy and technical recommendations to improve privacy and security without compromising the benefits of information sharing through SNSs.
Automatic Fall Detection System Based on the Combined Use of a Smartphone and a Smartwatch

Due to their widespread popularity, decreasing costs, built-in sensors, computing power and communication capabilities, Android-based personal devices are being seen as an appealing technology for the deployment of wearable fall detection systems. In contrast with previous solutions in the existing literature, which are based on the performance of a single element (a smartphone), this paper proposes and evaluates a fall detection system that benefits from the detection performed by two popular personal devices: a smartphone and a smartwatch (both provided with an embedded accelerometer and a gyroscope). In the proposed architecture, a specific application in each component permanently tracks and analyses the patient's movements. Diverse fall detection algorithms (commonly employed in the literature) were implemented in the developed Android apps to discriminate falls from the conventional activities of daily living of the patient. As a novelty, a fall is only assumed to have occurred if it is simultaneously and independently detected by the two Android devices (which can interact via Bluetooth communication). The system was systematically evaluated in an experimental testbed with actual test subjects simulating a set of falls and conventional movements associated with activities of daily living. The tests were repeated by varying the detection algorithm as well as the pre-defined mobility patterns executed by the subjects (i.e., the typology of the falls and non-fall movements). The proposed system was compared with the cases where only one device (the smartphone or the smartwatch) is considered to recognize and discriminate the falls. The obtained results show that the joint use of the two detection devices clearly increases the system's capability to avoid false alarms or 'false positives' (those conventional movements misidentified as falls) while maintaining the effectiveness of the detection decisions (that is to say, without increasing the ratio of 'false negatives' or actual falls that remain undetected).
Integrating Agile and User-Centered Design: A Systematic Mapping and Review of Evaluation and Validation Studies of Agile-UX

Agile software engineering and user-centered design are two important development processes for ensuring that an application has good user experience. However, integrating these two different processes into a single Agile-UX approach remains difficult. We performed a systematic mapping study to identify relevant research and understand what the field of Agile-UX looks like at present. This mapping discovered that there were only a few evaluation and validation papers published to date, so we performed a review of these papers to better understand the recommendations of these types of papers. Based on this, we are able both to provide a discussion of common trends in these papers that should be of interest to practitioners and to identify gaps in existing literature that indicate strong opportunities for future work.
A continuous linear optimal transport approach for pattern analysis in image datasets

We present a new approach to facilitate the application of the optimal transport metric to pattern recognition on image databases. The method is based on a linearized version of the optimal transport metric, which provides a linear embedding for the images. Hence, it enables shape and appearance modeling using linear geometric analysis techniques in the embedded space. In contrast to previous work, we use Monge's formulation of the optimal transport problem, which allows for reasonably fast computation of the linearized optimal transport embedding for large images. We demonstrate the application of the method to recover and visualize meaningful variations in a supervised-learning setting on several image datasets, including chromatin distribution in the nuclei of cells, galaxy morphologies, facial expressions, and bird species identification. We show that the new approach allows for high-resolution construction of modes of variations and discrimination and can enhance classification accuracy in a variety of image discrimination problems.
Social Commerce : A Contingency Framework for Assessing Marketing Potential

A key issue for marketers resulting from the dramatic rise of social media is how it can be leveraged to generate value for firms. Whereas the importance of social media for brand management and customer relationship management is widely recognized, it is unclear whether social media can also help companies market and sell products. Extant discussions of social commerce present a variety of perspectives, but the core issue remains unresolved. This paper aims to make two contributions. First, to address the lack of clarity in the literature regarding the meaning and domain of social commerce, the paper offers a definition stemming from important research streams in marketing. This definition allows for both a broad (covering all steps of the consumer decision process) and a narrow (focusing on the purchase act itself) construal of social commerce. Second, we build on this definition and develop a contingency framework for assessing the marketing potential that social commerce has to offer to firms. Implications for researchers and managers, based on the proposed definition and framework, are also discussed.
Trajectory privacy in location-based services and data publication

The ubiquity of mobile devices with global positioning functionality (e.g., GPS and AGPS) and Internet connectivity (e.g., 3G andWi-Fi) has resulted in widespread development of location-based services (LBS). Typical examples of LBS include local business search, e-marketing, social networking, and automotive traffic monitoring. Although LBS provide valuable services for mobile users, revealing their private locations to potentially untrusted LBS service providers pose privacy concerns. In general, there are two types of LBS, namely, snapshot and continuous LBS. For snapshot LBS, a mobile user only needs to report its current location to a service provider once to get its desired information. On the other hand, a mobile user has to report its location to a service provider in a periodic or on-demand manner to obtain its desired continuous LBS. Protecting user location privacy for continuous LBS is more challenging than snapshot LBS because adversaries may use the spatial and temporal correlations in the user's location samples to infer the user's location information with higher certainty. Such user location trajectories are also very important for many applications, e.g., business analysis, city planning, and intelligent transportation. However, publishing such location trajectories to the public or a third party for data analysis could pose serious privacy concerns. Privacy protection in continuous LBS and trajectory data publication has increasingly drawn attention from the research community and industry. In this survey, we give an overview of the state-of-the-art privacy-preserving techniques in these two problems.
Patterns of connections and movements in dual-map overlays: A new method of publication portfolio analysis

Portfolio analysis of the publication profile of a unit of interest, ranging from individuals, organizations, to a scientific field or interdisciplinary programs, aims to inform analysts and decision makers about the position of the unit, where it has been, and where it may go in a complex adaptive environment. A portfolio analysis may aim to identify the gap between the current position of an organization and a goal that it intends to achieve or identify competencies of multiple institutions. We introduce a new visual analytic method for analyzing, comparing, and contrasting characteristics of publication portfolios. The new method introduces a novel design of dual-map thematic overlays on global maps of science. Each publication portfolio can be added as one layer of dual-map overlays over two related but distinct global maps of science, one for citing journals and the other for cited journals. We demonstrate how the new design facilitates a portfolio analysis in terms of patterns emerging from the distributions of citation threads and the dynamics of trajectories as a function of space and time. We first demonstrate the analysis of portfolios defined on a single source article. Then we contrast publication portfolios of multiple comparable units of interest, namely, colleges in universities, corporate research organizations. We also include examples of overlays of scientific fields. We expect the new method will provide new insights to portfolio analysis.
Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media

History of mental illness is a major factor behind suicide risk and ideation. However research efforts toward characterizing and forecasting this risk is limited due to the paucity of information regarding suicide ideation, exacerbated by the stigma of mental illness. This paper fills gaps in the literature by developing a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation. We utilize semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts. We develop language and interactional measures for this purpose, as well as a propensity score matching based statistical approach. Our approach allows us to derive distinct markers of shifts to suicidal ideation. These markers can be modeled in a prediction framework to identify individuals likely to engage in suicidal ideation in the future. We discuss societal and ethical implications of this research.
THE MYSTERY OF THE MOZART EFFECT : Failure to Replicate

The Mozart effect is the purported increase in spatialreasoning performance immediately after exposure to a Mozart piano sonata. Several laboratories have been unable to confirm the existence of the effect despite two positive reports from the original laboratory. The authors of the original studies have provided a list of key procedural components to produce the effect. This experiment attempted to produce a Mozart effect by following those procedural instructions and replicating the procedure of one of the original positive reports. The experiment failed to produce either a statistically significant Mozart effect or an effect size suggesting practical significance. This general lack of effect is consistent with previous work by other investigators. We conclude that there is little evidence to support basing intellectual intervention programs on the existence of the Mozart
A Philosophy for Developing Trust in Self ‐ Driving Cars

For decades, our lives have depended on the safe operation of automated mechanisms around and inside us. The autonomy and complexity of these mechanisms is increasing dramatically. Autonomous systems such as self-driving cars rely heavily on inductive inference and complex software, both of which confound traditional software-safety techniques that are focused on amassing sufficient confirmatory evidence to support safety claims. In this paper we survey existing methods and tools that, taken together, can enable a new and more productive philosophy for software safety that is based on Karl Popper’s idea of falsificationism. 1 Trusting self-driving cars For decades, our lives have depended on the safe operation of automated mechanisms around and inside us. However, the autonomy of these mechanisms is increasing dramatically, going from comparatively simple drive-by-wire control to fully autonomous self-driving cars. The safety risks posed by autonomous systems cannot be mitigated through mechanical interlocks or similar tried-and-true techniques. Furthermore these autonomous systems will operate in unstructured environments (including highways not designed for self-driving cars and unpredictable weather conditions) that will present a myriad of unexpected situations. Without question, automation holds the promise of reducing the rates of accidents; for example, self-driving cars have the potential to virtually eliminate accidents due to inattentive drivers. However, when he pays attention, a human driver has tremendous capacity for reacting responsibly to circumstances for which he has not been explicitly trained. With the human out of the loop, the autonomous car is far less capable of handling unforeseen circumstances. By definition, an “unstructured environment” such as a real-world road network includes plenty of unforeseen con-
Leaf Disease Grading by Machine Vision and Fuzzy Logic

present paper introduces an innovative approach to automatically grade the disease on plant leaves. The system effectively inculcates Information and Communication Technology (ICT) in agriculture and hence contributes to Precision Agriculture. Presently, plant pathologists mainly rely on naked eye prediction and a disease scoring scale to grade the disease. This manual grading is not only time consuming but also not feasible. Hence the current paper proposes an image processing based approach to automatically grade the disease spread on plant leaves by employing Fuzzy Logic. The results are proved to be accurate and satisfactory in contrast with manual grading. Keywordscolor image segmentation, disease spot extraction, percent-infection, fuzzy logic, disease grade.  The sole area that serves the food needs of the entire human race is the Agriculture sector. It has played a key role in the development of human civilization. Plants exist everywhere we live, as well as places without us. Plant disease is one of the crucial causes that reduces quantity and degrades quality of the agricultural products. Plant Pathology is the scientific study of plant diseases caused by pathogens (infectious diseases) and environmental conditions (physiological factors). It involves the study of pathogen identification, disease etiology, disease cycles, economic impact, plant disease epidemiology, plant disease resistance, pathosystem genetics and management of plant diseases. Disease is impairment to the normal state of the plant that modifies or interrupts its vital functions such as photosynthesis, transpiration, pollination, fertilization, germination etc. Plant diseases have turned into a nightmare as it can cause significant reduction in both quality and quantity of agricultural products [2]. Information and Communication Technology (ICT) application is going to be implemented as a solution in improving the status of the agriculture sector . Due to the manifestation and developments in the fields of sensor networks, robotics, GPS technology, communication systems etc, precision agriculture started emerging [10]. The objectives of precision agriculture are profit maximization, agricultural input rationalization and environmental damage reduction by adjusting the agricultural practices to the site demands. In the area of disease management, grade of the disease is determined to provide an accurate and precision treatment advisory.  Presently the plant pathologists mainly rely on the naked eye prediction and a disease scoring scale to grade the disease on leaves. There are some problems associated with this manual grading. Diseases are inevitable in plants. When a plant gets affected by the disease, a treatment advisory is required to cure the Arun Kumar R et al,  1709-1716 IJCTA | SEPT-OCT 2011 Available online@www.ijcta.com 1709 ISSN:2229-6093
DRETOM: developer recommendation based on topic models for bug resolution

In most cases, bug resolution is a collaborative activity among developers in software development where each developer contributes his or her ideas on how to resolve the bug. Although only one developer is recorded as the actual fixer for the bug, the contribution of the developers who participated in the collaboration cannot be neglected.
  This paper proposes a new approach, called DRETOM (Developer REcommendation based on TOpic Models), to recommending developers for bug resolution in collaborative behavior.
 Method: The proposed approach models developers' interest in and expertise on bug resolving activities based on topic models that are built from their historical bug resolving records. Given a new bug report, DRETOM recommends a ranked list of developers who are potential to participate in and contribute to resolving the new bug according to these developers' interest in and expertise on resolving it.
  Experimental results on Eclipse JDT and Mozilla Firefox projects show that DRETOM can achieve high recall up to 82% and 50% with top 5 and top 7 recommendations respectively.
  Developers' interest in bug resolving activities should be taken into consideration. On condition that the parameter θ of DRETOM is set properly with trials, the proposed approach is practically useful in terms of recall.
Pasting Small Votes for Classification in Large Databases and On-Line

Many databases have grown to the point where they cannot fit into the fast memory of even large memory machines, to say nothing of current workstations. If what we want to do is to use these data bases to construct predictions of various characteristics, then since the usual methods require that all data be held in fast memory, various work-arounds have to be used. This paper studies one such class of methods which give accuracy comparable to that which could have been obtained if all data could have been held in core and which are computationally fast. The procedure takes small pieces of the data, grows a predictor on each small piece and then pastes these predictors together. A version is given that scales up to terabyte data sets. The methods are also applicable to on-line learning.