nils-herrmann/clean_scirepeval huggingface dataset:
Losses , Gains , and Hyperbolic Discounting : An Experimental Approach to Information Security Attitudes and Behavior

Surveys and experiments have uncovered a dichotomy between stated attitudes and actual behavior of individuals facing decisions affecting their privacy and their personal information security. Surveys report that most individuals are concerned about the security of their personal information and are willing to act to protect it. Experiments reveal that very few individuals actually take any action to protect their personal information, even when doing so involves limited costs. In this paper we analyze the causes of this dichotomy. We discuss which economic considerations are likely to affect individual choice and we advance testable hypotheses about why individuals’ information security attitudes seem inconsistent with their behavior. We then outline an experimental design to test our hypotheses. The experiment is designed to compare individuals’ characteristics as market agents to their information security attitudes and behavior.
A 63,000 Q-factor relaxation oscillator with switched-capacitor integrated error feedback

There is a growing interest in implementing on-chip reference clock generators for low-cost low-power area-efficient SoCs, such as implantable biomedical devices and microcomputers. Relaxation oscillators are suitable candidates to generate such reference clocks due to their compact size, low power consumption and wide frequency tuning range. However, the poor phase noise performance and large long-term variation are two major problems that limit their application.
The Implications and Impacts of Web Services to Electronic Commerce Research and Practices

Web services refer to a family of technologies that can universally standardize the communication of applications in order to connect systems, business partners, and customers cost-effectively through the World Wide Web. Major software vendors such as IBM, Microsoft, SAP, SUN, and Oracle are all embracing Web services standards and are releasing new products or tools that are Web services enabled. Web services will ease the constraints of time, cost, and space for discovering, negotiating, and conducting e-business transactions. As a result, Web services will change the way businesses design their applications as services, integrate with other business entities, manage business process workflows, and conduct e-business transactions. The early adopters of Web services are showing promising results such as greater development productivity gains and easier and faster integration with trading partners. However, there are many issues worth studying regarding Web services in the context of e-commerce. This special issue of the JECR aims to encourage awareness and discussion of important issues and applications of Web Services that are related to electronic commerce from the organizational, economics, and technical perspectives. Research opportunities of Web services and e-commerce area are fruitful and important for both academics and practitioners. We wish that this introductory article can shed some light for researchers and practitioners to better understand important issues and future trends of Web services and e-business.
The impact of website content dimension and e-trust on e-marketing effectiveness: The case of Iranian commercial saffron corporations

By considering the problems that commercial saffron companies have faced in international markets, the aim of this study is to investigate the impact of website content, including informational and design dimensions, on the effectiveness of e-marketing and e-trust as mediator variables. These aspects are examined with reference to sales and marketing division managers in a sample of 100 commercial saffron corporations in the Khorasan province. The findings support the ideas that website content has an effect on e-marketing and e-trust and that e-trust plays a mediating role in the relationship between etrust and e-marketing effectiveness.
A Novel Artificial Bee Colony Algorithm

Artificial bee colony algorithm is a new population-based evolutionary method based on the intelligent behavior of honey bee swarm. It has shown more effective than other biological-inspired algorithms. However, there are still insufficiencies in ABC algorithm, which is good at exploration but poor at exploitation and its convergence speed is also an issue in some cases. For these insufficiencies, we propose a novel artificial bee colony algorithm (NABC) for numerical optimization problems in this paper to improve the exploitation capability by incorporating the current best solution into the search procedure. Experiments are conducted on a set of unimodal/multimodal benchmark functions. The experiments results of NABC have been compared with Gbest-guided artificial bee colony algorithm (G-ABC), improved artificial bee colony algorithm (I-ABC), Elitist artificial bee colony algorithm (E-ABC). The results show that NABC is superior to those algorithms in most of the tested functions.
1.5MVA grid-connected interleaved inverters using coupled inductors for wind power generation system

In this paper, grid-connected interleaved voltage source inverters for PMSG wind power generation system with coupled inductors is introduced. In parallel operation, the undesirable circulating current flows between inverters. There are some differences in circulating currents according to source configuration. It is mathematically analyzed and simulated for the verification of analysis. In case of interleaved inverters, the uncontrollable circulating current in switching frequency level flows between inverter modules regardless of source configuration such as common or separated if filter inductance which is placed between inverters is significantly low. In order to suppress high frequency circulating current, the coupled inductors are employed in each phase. A case of 1.5MVA grid-connected interleaved inverters using coupled inductors prototype for the PMSG wind power generation Back To Back converters in parallel are introduced and experimentally verified the proposed topology.
SuperedgeRank algorithm and its application in identifying opinion leader of online public opinion supernetwork

Opinion leaders on the internet are very important figures in online communities, which play an important role in promoting the formation of public opinions. Many theories have been introduced to identify opinion leaders by social network analysis, text mining and PageRank-based algorithm in different fields, but few has addressed the issue of opinion leader identification by combining the methods above, and there is no research using supernetwork analysis to identify opinion leaders. This paper proposed an SuperedgeRank algorithm for opinion leader identification based on supernetwork theory, which combined the network topology analysis and text mining. First, the study established a supernetwork model with multidimensional subnetworks, which are social, psychological, environmental and viewpoint subnetworks. Then, the study proposed four supernetwork indexes: node superdegree, superedge degree, superedge–superedge distance and superedge overlap. The later two indexes are developed by us to help evaluate the identified opinion leaders. Based on them, our study applied SuperedgeRank algorithm to rank superedges, and used the ranking result to identify opinion leaders in opinion supernetwork model. Finally, the feasibility and innovativeness of this method were verified by a case study.
A crucial role for adipose tissue p53 in the regulation of insulin resistance

Various stimuli, such as telomere dysfunction and oxidative stress, can induce irreversible cell growth arrest, which is termed 'cellular senescence'. This response is controlled by tumor suppressor proteins such as p53 and pRb. There is also evidence that senescent cells promote changes related to aging or age-related diseases. Here we show that p53 expression in adipose tissue is crucially involved in the development of insulin resistance, which underlies age-related cardiovascular and metabolic disorders. We found that excessive calorie intake led to the accumulation of oxidative stress in the adipose tissue of mice with type 2 diabetes–like disease and promoted senescence-like changes, such as increased activity of senescence-associated β-galactosidase, increased expression of p53 and increased production of proinflammatory cytokines. Inhibition of p53 activity in adipose tissue markedly ameliorated these senescence-like changes, decreased the expression of proinflammatory cytokines and improved insulin resistance in mice with type 2 diabetes–like disease. Conversely, upregulation of p53 in adipose tissue caused an inflammatory response that led to insulin resistance. Adipose tissue from individuals with diabetes also showed senescence-like features. Our results show a previously unappreciated role of adipose tissue p53 expression in the regulation of insulin resistance and suggest that cellular aging signals in adipose tissue could be a new target for the treatment of diabetes (pages 996–967).
Reconstruction of hidden 3D shapes using diffuse reflections

We analyze multi-bounce propagation of light in an unknown hidden volume and demonstrate that the reflected light contains sufficient information to recover the 3D structure of the hidden scene. We formulate the forward and inverse theory of secondary scattering using ideas from energy front propagation and tomography. We show that using Fresnel approximation greatly simplifies this problem and the inversion can be achieved via a backpropagation process. We study the invertibility, uniqueness and choices of space-time-angle dimensions using synthetic examples. We show that a 2D streak camera can be used to discover and reconstruct hidden geometry. Using a 1D high speed time of flight camera, we show that our method can be used recover 3D shapes of objects "around the corner".
Classification of watermelon leaf diseases using neural network analysis

This paper mainly discussed the process to classify Anthracnose and Downey Mildew, watermelon leaf diseases using neural network analysis. A few of infected leaf samples were collected and they were captured using a digital camera with specific calibration procedure under controlled environment. The classification on the watermelon's leaf diseases is based on color feature extraction from RGB color model where the RGB pixel color indices have been extracted from the identified Regions of Interest (ROI). The proposed automated classification model involved the process of diseases classification using Statistical Package for the Social Sciences (SPSS) and Neural Network Pattern Recognition Toolbox in MATLAB. Determinations in this work have shown that the type of leaf diseases achieved 75.9% of accuracy based on its RGB mean color component.
Training Testing ( in Use ) ( . ) User Side

There is growing concern about how personal data are used when users grant applications direct access to the sensors of their mobile devices. In fact, high resolution temporal data generated by motion sensors reflect directly the activities of a user and indirectly physical and demographic attributes. In this paper, we propose a feature learning architecture for mobile devices that provides flexible and negotiable privacypreserving sensor data transmission by appropriately transforming raw sensor data. The objective is to move from the current binary setting of granting or not permission to an application, toward a model that allows users to grant each application permission over a limited range of inferences according to the provided services. The internal structure of each component of the proposed architecture can be flexibly changed and the trade-off between privacy and utility can be negotiated between the constraints of the user and the underlying application. We validated the proposed architecture in an activity recognition application using two realworld datasets, with the objective of recognizing an activity without disclosing gender as an example of private information. Results show that the proposed framework maintains the usefulness of the transformed data for activity recognition, with an average loss of only around three percentage points, while reducing the possibility of gender classification to around 50%, the target random guess, from more than 90% when using raw sensor data. We also present and distribute MotionSense, a new dataset for activity and attribute recognition collected from motion sensors.
Rumor Detection and Classification for Twitter Data

With the pervasiveness of online media data as a source of information, verifying the validity of this information is becoming even more important yet quite challenging. Rumors spread a large quantity of misinformation on microblogs. In this study we address two common issues within the context of microblog social media. First, we detect rumors as a type of misinformation propagation, and next, we go beyond detection to perform the task of rumor classification (RDC). We explore the problem using a standard data set. We devise novel features and study their impact on the task. We experiment with various levels of preprocessing as a precursor to the classification as well as grouping of features. We achieve an F-Measure of over 0.82 in the RDC task in a mixed rumors data set and 84% in a single rumor data set using a two step classification
Locality preserving indexing for document representation

Document representation and indexing is a key problem for document analysis and processing, such as clustering, classification and retrieval. Conventionally, Latent Semantic Indexing (LSI) is considered effective in deriving such an indexing. LSI essentially detects the most representative features for document representation rather than the most discriminative features. Therefore, LSI might not be optimal in discriminating documents with different semantics. In this paper, a novel algorithm called Locality Preserving Indexing (LPI) is proposed for document indexing. Each document is represented by a vector with low dimensionality. In contrast to LSI which discovers the global structure of the document space, LPI discovers the local structure and obtains a compact document representation subspace that best detects the essential semantic structure. We compare the proposed LPI approach with LSI on two standard databases. Experimental results show that LPI provides better representation in the sense of semantic structure.
Comparison Analysis of CPU Scheduling : FCFS, SJF and Round Robin

Task scheduling is needed to maintain every process that comes with a processor in parallel processing. In several conditions, not every algorithm works better on the significant problem. Sometimes FCFS algorithm is better than the other in short burst time while Round Robin is better for multiple processes in every single time. However, it cannot be predicted what process will come after. Average Waiting Time is a standard measure for giving credit to the scheduling algorithm. Several techniques have been applied to maintain the process to make the CPU performance in normal. The objective of this paper is to compare three algorithms, FCFS, SJF, and Round Robin. The target is to know which algorithm is more suitable for the certain process. Index Term FCFS, SJF, Round Robin, Schedule, Operating System.
An Adaptive Current-Boosting Voltage Buffer for Low-Power Low Dropout Regulators

A voltage buffer to solve the slew-rate limit problem at the gate of the power transistor of LDOs is proposed in this paper. The proposed idea, based on the capacitive-coupling method to provide an instantaneous boost of the transient current to charge/discharge the gate capacitance of the power transistor, provides power saving of the voltage buffer and enables fast load transient responses of the LDOs simultaneously. The proposed idea is applied to a LDO design and simulated with BSIM models of a commercial 0.35-mum CMOS technology. The quiescent current is about 3.5 muA, and the setting time of the load transient response is greatly improved, when comparing to the LDO without the proposed idea.
Intelligent Condition Based Monitoring Using Acoustic Signals for Air Compressors

Intelligent fault diagnosis of machines for early recognition of faults saves industry from heavy losses occurring due to machine breakdowns. This paper proposes a process with a generic data mining model that can be used for developing acoustic signal-based fault diagnosis systems for reciprocating air compressors. The process includes details of data acquisition, sensitive position analysis for deciding suitable sensor locations, signal pre-processing, feature extraction, feature selection, and a classification approach. This process was validated by developing a real time fault diagnosis system on a reciprocating type air compressor having 8 designated states, including one healthy state, and 7 faulty states. The system was able to accurately detect all the faults by analyzing acoustic recordings taken from just a single position. Additionally, thorough analysis has been presented where performance of the system is compared while varying feature selection techniques, the number of selected features, and multiclass decomposition algorithms meant for binary classifiers.
Embedding a Mathematical OCR Module into OCRopus

This paper describes embedding a mathematical formula recognition module into the OCR system OCRopus aiming at developing a OCR system for scientific and technical documents which include mathematical formulas. OCRopus is a open source OCR system emphasizing modularity, easy extensibility, and reuse. This system has several basic components such as preprocessing, layout analysis, and text line recognition, so it is a challenging project to embed the mathematical formula recognition module into the OCRopus system. We have developed the math OCR module, then report how to embed our module into the OCRopus system in order to realize a math OCR which can deal with wide variety of documents including mathematical formulas.
An international qualitative study of ability and disability in ADHD using the WHO-ICF framework

This is the third in a series of four cross-cultural empirical studies designed to develop International Classification of Functioning, Disability and Health (ICF, and Children and Youth version, ICF(-CY) Core Sets for Attention-Deficit Hyperactivity Disorder (ADHD). To explore the perspectives of individuals diagnosed with ADHD, self-advocates, immediate family members and professional caregivers on relevant areas of impairment and functional abilities typical for ADHD across the lifespan as operationalized by the ICF(-CY). A qualitative study using focus group discussions or semi-structured interviews of 76 participants, divided into 16 stakeholder groups. Participants from five countries (Brazil, India, Saudi Arabia, South Africa and Sweden) were included. A deductive qualitative content analysis was conducted to extract meaningful functioning and disability concepts from verbatim material. Extracted concepts were then linked to ICF(-CY) categories by independent researchers using a standardized linking procedure. In total, 82 ICF(-CY) categories were identified, of which 32 were related to activities and participation, 25 to environmental factors, 23 to body functions and 2 to body structures. Participants also provided opinions on experienced positive sides to ADHD. A high level of energy and drive, creativity, hyper-focus, agreeableness, empathy, and willingness to assist others were the most consistently reported strengths associated with ADHD. Stakeholder perspectives highlighted the need to appraise ADHD in a broader context, extending beyond diagnostic criteria into many areas of ability and disability as well as environmental facilitators and barriers. This qualitative study, along with three other studies (comprehensive scoping review, expert survey and clinical study), will provide the scientific basis to define ICF(-CY) Core Sets for ADHD, from which assessment tools can be derived for use in clinical and research setting, as well as in health care administration.
68 Implementing Dubins Airplane Pathson Fixed-Wing UAVs !

A well-known path-planning technique for mobile robots or planar aerial vehicles is to use Dubins paths, which are minimum-distance paths between two configurations subject to the constraints of the Dubins car model. An extension of this method to a three-dimensional Dubins airplane model has recently been proposed. This chapter builds on that work showing a complete architecture for implementing Dubins airplane paths on small fixed-wing UAVs. The existing Dubins airplane model is modified to be more consistent with the kinematics of a fixed-wing aircraft. The chapter then shows how a recently proposed vector-field method can be used to design a guidance law that causes the Dubins airplane model to follow straight-line and helical paths. Dubins airplane paths are more complicated than Dubins car paths because of the altitude component. Based on the difference between the altitude of the start and end configurations, Dubins airplane paths can be classified as low, medium, or high altitude gain. While for medium and high altitude gain there are many different Dubins airplane paths, this chapter proposes selecting the path that maximizes the average altitude throughout the maneuver. The proposed architecture is implemented on a six degree-of-freedom Matlab/Simulink simulation of an Aerosonde UAV, and results from this simulation demonstrate the effectiveness of the technique. 68.
Compositing digital images

Most computer graphics pictures have been computed all at once, so that the rendering program takes care of all computations relating to the overlap of objects. There are several applications, however, where elements must be rendered separately, relying on compositing techniques for the anti-aliased accumulation of the full image. This paper presents the case for four-channel pictures, demonstrating that a matte component can be computed similarly to the color channels. The paper discusses guidelines for the generation of elements and the arithmetic for their arbitrary compositing.
Proactive avoidance of moving obstacles for a service robot utilizing a behavior-based control

A main challenge in the application of service robotics is safe and reliable navigation of robots in human everyday environments. Supermarkets, which are chosen here as an example, pose a challenging scenario because they usually have a cluttered and nested character. The robot has to avoid collisions with static and even with moving obstacles while interacting with nearby humans or a dedicated user respectively. This paper presents a hierarchical approach for the proactive avoidance of moving objects as it is used on the robot shopping trolley InBOT. The behavior-based control (bbc) of InBOT is extended by a reflex and a reactive behavior to ensure adequate reaction times when confronted with a possible collision. On top of the bbc a spatio-temporal planner is situated which is able to predict environmental changes and therefore can generate a safe movement sequence accordingly.