nils-herrmann/clean_scirepeval huggingface dataset:
Modeling of financial supply chain

The research work on supply-chain management has primarily focused on the study of materials flow and very little work has been done on the study of upstream flow of money. In this paper we study the flow of money in a supply chain from the viewpoint of a supply chain partner who receives money from the downstream partners and makes payments to the upstream partners. The objective is to schedule all payments within the constraints of the receipt of the money. A penalty is to be paid if payments are not made within the specified time. Any unused money in a given period can be invested to earn an interest. The problem is computationally complex and non-intuitive because of its dynamic nature. The incoming and outgoing monetary flows never stop and are sometimes unpredictable. For tractability purposes we first develop an integer programming model to represent the static problem, where monetary in-flows and out-flows are known before hand. We demonstrate that even the static problem is NP-Complete. First we develop a heuristic to solve this static problem. Next, the insights derived from the static problem analysis are used to develop two heuristics to solve the various level of dynamism of the problem. The performances of all these heuristics are measured and presented.
Implemental Control Strategy for Grid Stabilization of Grid-Connected PV System Based on German Grid Code in Symmetrical Low-to-Medium Voltage Network

In the last couple of years, the increasing penetration of renewable energy resulted in the development of grid-connected large-scale power plants. However, a high penetration harbors the risk of grid instability if the generating power plants are not able to support the grid. Therefore, grid stabilization, which depends on the system-type or grid of each country, plays an important role and has been strengthened by different grid codes. With this background, VDE-AR-N 4105 for photovoltaic (PV) systems connected to the low-voltage grid and the German Association of Energy and Water Industries (BDEW) introduced the medium-voltage grid code for connecting power plants to the grid and they are the most stringent certifications. In this paper, the control strategy of generating system is enhanced with VDE-AR-N 4105 and BDEW grid code, where both active/reactive powers are controlled. Simulation and experimental results of 100-kW PV inverter are shown to verify the effectiveness of the proposed implemental control strategy.
A Data Mining Approach for Prediction of Heart Disease Using Neural Networks

Heart disease diagnosis is a complex task which requires much experience and knowledge. Traditional way of predicting Heart disease is doctor’s examination or number of medical tests such as ECG, Stress Test, and Heart MRI etc. Nowadays, Health care industry contains huge amount of heath care data, which contains hidden information. This hidden information is useful for making effective decisions. Computer based information along with advanced Data mining techniques are used for appropriate results. Neural network is widely used tool for predicting Heart disease diagnosis. In this research paper, a Heart Disease Prediction system (HDPS) is developed using Neural network. The HDPS system predicts the likelihood of patient getting a Heart disease. For prediction, the system uses sex, blood pressure, cholesterol like 13 medical parameters. Here two more parameters are added i.e. obesity and smoking for better accuracy. From the results, it has been seen that neural network predict heart disease with nearly 100% accuracy.
A game plan: Gamification design principles in mHealth applications for chronic disease management

Effective chronic disease management is essential to improve positive health outcomes, and incentive strategies are useful in promoting self-care with longevity. Gamification, applied with mHealth (mobile health) applications, has the potential to better facilitate patient self-management. This review article addresses a knowledge gap around the effective use of gamification design principles, or mechanics, in developing mHealth applications. Badges, leaderboards, points and levels, challenges and quests, social engagement loops, and onboarding are mechanics that comprise gamification. These mechanics are defined and explained from a design and development perspective. Health and fitness applications with gamification mechanics include: bant which uses points, levels, and social engagement, mySugr which uses challenges and quests, RunKeeper which uses leaderboards as well as social engagement loops and onboarding, Fitocracy which uses badges, and Mango Health, which uses points and levels. Specific design considerations are explored, an example of the efficacy of a gamified mHealth implementation in facilitating improved self-management is provided, limitations to this work are discussed, a link between the principles of gaming and gamification in health and wellness technologies is provided, and suggestions for future work are made. We conclude that gamification could be leveraged in developing applications with the potential to better facilitate self-management in persons with chronic conditions.
Time to Give Up the Dogmas of Attribution : An Alternative Theory of Behavior Explanation

Attribution research has held a prominent place in social psychology for 50 years, and the dominant theory of attribution has been the same for all this time. Unfortunately, this theory (a version of attribution as covariation detection) cannot account for people’s ordinary explanations of behavior. The goal here is to present a theory that can. The theory is grounded in the framework of folk concepts children and adults use to make sense of human behavior, a framework that was already anticipated by Fritz Heider. To introduce the theory, I first map out this folk-conceptual framework, provide evidence for its core elements, and develop the cognitive and social features of behavior explanations, with a focus on the unique properties of intentional action explanation. I then apply the theory to a core attributional phenomenon—actor– observer asymmetries in explanation—and chart two additional applications. In light of these results, I summarize the theoretical and empirical reasons to give up these three dogmas of attribution theory: that behaviors are like all other events, that explaining behavior is choosing between person and situation causes, and that such choices are driven by covariation detection.
Intelligence-Augmented Rat Cyborgs in Maze Solving

Cyborg intelligence is an emerging kind of intelligence paradigm. It aims to deeply integrate machine intelligence with biological intelligence by connecting machines and living beings via neural interfaces, enhancing strength by combining the biological cognition capability with the machine computational capability. Cyborg intelligence is considered to be a new way to augment living beings with machine intelligence. In this paper, we build rat cyborgs to demonstrate how they can expedite the maze escape task with integration of machine intelligence. We compare the performance of maze solving by computer, by individual rats, and by computer-aided rats (i.e. rat cyborgs). They were asked to find their way from a constant entrance to a constant exit in fourteen diverse mazes. Performance of maze solving was measured by steps, coverage rates, and time spent. The experimental results with six rats and their intelligence-augmented rat cyborgs show that rat cyborgs have the best performance in escaping from mazes. These results provide a proof-of-principle demonstration for cyborg intelligence. In addition, our novel cyborg intelligent system (rat cyborg) has great potential in various applications, such as search and rescue in complex terrains.
Text Pre-processing and Text Segmentation for OCR

Optical Character Recognition (OCR) systems have been effectively developed for the recognition of printed script. The accuracy of OCR system mainly depends on the text preprocessing and segmentation algorithm being used. When the document is scanned it can be placed in any arbitrary angle which would appear on the computer monitor at the same angle. This paper addresses the algorithm for correction of skew angle generated in scanning of the text document and a novel profile based method for segmentation of printed text which separates the text in document image into lines, words and characters. Skew correction, Segmentation, Text preprocessing, Horizontal Profile, Vertical Profile.
Using Kullback-Leibler Distance for Text Categorization

A system that performs text categorization aims to assign appropriate categories from a predefined classification scheme to incoming documents. These assignments might be used for varied purposes such as filtering, or retrieval. This paper introduces a new effective model for text categorization with great corpus (more or less 1 million documents). Text categorization is performed using the Kullback-Leibler distance between the probability distribution of the document to classify and the probability distribution of each category. Using the same representation of categories, experiments show a significant improvement when the above mentioned method is used. KLD method achieve substantial improvements over the tfidf performing method.
THE FLEXIBLE CORRECTION MODEL : THE ROLE OF NAIVE THEORIES OF BIAS IN BIAS CORRECTION

Life often presents us with situations in which it is important to assess the “true” qualities of a person or object, but in which some factor(s) might have affected (or might yet affect) our initial perceptions in an undesired way. For example, in the Reginald Denny case following the 1993 Los Angeles riots, jurors were asked to determine the guilt or innocence of two African-American defendants who were charged with violently assaulting a Caucasion truck driver. Some of the jurors in this case might have been likely to realize that in their culture many of the popular media portrayals of African-Americans are violent in nature. Yet, these jurors ideally would not want those portrayals to influence their perceptions of the particular defendants in the case. In fact, the justice system is based on the assumption that such portrayals will not influence jury verdicts. In our work on bias correction, we have been struck by the variety of potentially biasing factors that can be identified-including situational influences such as media, social norms, and general culture, and personal influences such as transient mood states, motives (e.g., to manage impressions or agree with liked others), and salient beliefs-and we have been impressed by the apparent ubiquity of correction phenomena (which appear to span many areas of psychological inquiry). Yet, systematic investigations of bias correction are in their early stages. Although various researchers have discussed the notion of effortful cognitive processes overcoming initial (sometimes “automatic”) biases in a variety of settings (e.g., Brewer, 1988; Chaiken, Liberman, & Eagly, 1989; Devine, 1989; Kruglanski & Freund, 1983; Neuberg & Fiske, 1987; Petty & Cacioppo, 1986), little attention has been given, until recently, to the specific processes by which biases are overcome when effort is targeted toward “correction of bias.” That is, when
Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network

Hyperspectral data classification is a hot topic in remote sensing community. In recent years, significant effort has been focused on this issue. However, most of the methods extract the features of original data in a shallow manner. In this paper, we introduce a deep learning approach into hyperspectral image classification. A new feature extraction (FE) and image classification framework are proposed for hyperspectral data analysis based on deep belief network (DBN). First, we verify the eligibility of restricted Boltzmann machine (RBM) and DBN by the following spectral information-based classification. Then, we propose a novel deep architecture, which combines the spectral-spatial FE and classification together to get high classification accuracy. The framework is a hybrid of principal component analysis (PCA), hierarchical learning-based FE, and logistic regression (LR). Experimental results with hyperspectral data indicate that the classifier provide competitive solution with the state-of-the-art methods. In addition, this paper reveals that deep learning system has huge potential for hyperspectral data classification.
Technology addiction's contribution to mental wellbeing: The positive effect of online social capital

This research examines the effect of online social capital and Internet use on the normally negative effects of technology addiction, especially for individuals prone to self-concealment. Self-concealment is a personality trait that describes individuals who are more likely to withhold personal and private information, inhibiting catharsis and wellbeing. Addiction, in any context, is also typically associated with negative outcomes. However, we investigate the hypothesis that communication technology addiction may positively affect wellbeing for self-concealing individuals when online interaction is positive, builds relationships, or fosters a sense of community. Within these parameters, increased communication through mediated channels (and even addiction) may reverse the otherwise negative effects of self-concealment on wellbeing. Overall, the proposed model offers qualified support for the continued analysis of mediated communication as a potential source for improving the wellbeing for particular individuals. This study is important because we know that healthy communication in relationships, including disclosure, is important to wellbeing. This study recognizes that not all people are comfortable communicating in face-to-face settings. Our findings offer evidence that the presence of computers in human behaviors (e.g., mediated channels of communication and NCTs) enables some individuals to communicate and fos ter beneficial interpersonal relationships, and improve their wellbeing.
Day-to-Day Travel Time Trends and Travel Time Prediction from Loop Detector Data

This paper presents an approach to estimate future travel times on a freeway using flow and occupancy data from single loop detectors and historical travel time information. The work uses linear regression with stepwise variable selection method and more advanced tree based methods. The analysis considers forecasts ranging from a few minutes into the future up to an hour ahead. Leave-a-day-out cross-validation was used to evaluate the prediction errors without under-estimation. The current traffic state proved to be a good predictor for the near future, up to 20 minutes, while historical data is more informative for longerrange predictions. Tree based methods and linear regression both performed satisfactorily, showing slightly different qualitative behaviors for each condition examined in this analysis. Unlike preceding works that rely on simulation, this study uses real traffic data. Although the current implementation uses measured travel times from probe vehicles, the ultimate goal of this research is an autonomous system that relies strictly on detector data. In the course of presenting the prediction system, the paper examines how travel times change from day-to-day and develops several metrics to quantify these changes. The metrics can be used as input for travel time prediction, but they should be also beneficial for other applications such as calibrating traffic models and planning models.
Statistical mapping between articulatory movements and acoustic spectrum using a Gaussian mixture model

In this paper, we describe a statistical approach to both an articulatory-to-acoustic mapping and an acoustic-to-articulatory inversion mapping without using phonetic information. The joint probability density of an articulatory parameter and an acoustic parameter is modeled using a Gaussian mixture model (GMM) based on a parallel acoustic-articulatory speech database. We apply the GMM-based mapping using the minimum mean-square error (MMSE) criterion, which has been proposed for voice conversion, to the two mappings. Moreover, to improve the mapping performance, we apply maximum likelihood estimation (MLE) to the GMM-based mapping method. The determination of a target parameter trajectory having appropriate static and dynamic properties is obtained by imposing an explicit relationship between static and dynamic features in the MLE-based mapping. Experimental results demonstrate that the MLE-based mapping with dynamic features can significantly improve the mapping performance compared with the MMSE-based mapping in both the articulatory-to-acoustic mapping and the inversion mapping.
Social influence effects on automatic racial prejudice.

Although most research on the control of automatic prejudice has focused on the efficacy of deliberate attempts to suppress or correct for stereotyping, the reported experiments tested the hypothesis that automatic racial prejudice is subject to common social influence. In experiments involving actual interethnic contact, both tacit and expressed social influence reduced the expression of automatic prejudice, as assessed by two different measures of automatic attitudes. Moreover, the automatic social tuning effect depended on participant ethnicity. European Americans (but not Asian Americans) exhibited less automatic prejudice in the presence of a Black experimenter than a White experimenter (Experiments 2 and 4), although both groups exhibited reduced automatic prejudice when instructed to avoid prejudice (Experiment 3). Results are consistent with shared reality theory, which postulates that social regulation is central to social cognition.
Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis

The rapid increase in the number of Android malware poses great challenges to anti-malware systems, because the sheer number of malware samples overwhelms malware analysis systems. The classification of malware samples into families, such that the common features shared by malware samples in the same family can be exploited in malware detection and inspection, is a promising approach for accelerating malware analysis. Furthermore, the selection of representative malware samples in each family can drastically decrease the number of malware to be analyzed. However, the existing classification solutions are limited because of the following reasons. First, the legitimate part of the malware may misguide the classification algorithms because the majority of Android malware are constructed by inserting malicious components into popular apps. Second, the polymorphic variants of Android malware can evade detection by employing transformation attacks. In this paper, we propose a novel approach that constructs frequent subgraphs (fregraphs) to represent the common behaviors of malware samples that belong to the same family. Moreover, we propose and develop FalDroid, a novel system that automatically classifies Android malware and selects representative malware samples in accordance with fregraphs. We apply it to 8407 malware samples from 36 families. Experimental results show that FalDroid can correctly classify 94.2% of malware samples into their families using approximately 4.6 sec per app. FalDroid can also dramatically reduce the cost of malware investigation by selecting only 8.5% to 22% representative samples that exhibit the most common malicious behavior among all samples.
Semantic Web Meets Internet of Things and Web of Things: [2nd Edition]

An ever growing interest and wide adoption of Internet of Things (IoT) and Web technologies are unleashing a true potential of designing a broad range of high-quality consumer applications. Smart cities, smart buildings, and e-health are among various application domains which are currently benefiting and will continue to benefit from IoT and Web technologies in a foreseeable future. Similarly, semantic technologies have proven their effectiveness in various domains and a few among multiple challenges which semantic Web technologies are addressing are to (i) mitigate heterogeneity by providing semantic inter-operability, (ii) facilitate easy integration of data application, (iii) deduce and extract new knowledge to build applications providing smart solutions, and (iv) facilitate inter-operability among various data processes including representation, management and storage of data. In this tutorial, our focus will be on the combination of Web technologies, Semantic Web, and IoT technologies and we will present to our audience that how a merger of these technologies is leading towards an evolution from IoT to Web of Things (WoT) to Semantic Web of Things. This tutorial will introduce the basics of Internet of Things, Web of Things and Semantic Web and will demonstrate tools and techniques designed to enable the rapid development of semantics-based Web of Things applications. One key aspect of this tutorial is to familiarize its audience with the open source tools designed by different semantic Web, IoT and WoT based projects and provide the audience a rich hands-on experience to use these tools and build smart applications with minimal efforts. Thus, reducing the learning curve to its maximum. We will showcase real-world use case scenarios which are designed using semantically-enabled WoT frameworks (e.g. CityPulse, FIESTA-IoT and M3).
A center fed multi-band antenna for simultaneous satellite communication at C and Ku bands

As the Navy requires increased data throughput via satellite communications at sea, the need for a vessel based SATCOM multi-band antenna operating at both C and Ku-bands is on the rise. The design of a compact, high performance, simultaneous C and Ku-band antenna and feed presents many technical challenges. Such challenges include mechanical packaging of the feed components with short focal length optics, broadband axial ratio that is less than 0.75 dB, and broadband high efficiency. This paper describes the design and testing of a multi-band coaxial antenna subsystem as part of the Navy Commercial Broadband Satellite Program (CBSP). The subsystem includes capability to switch among linear and circular polarization at C-band as well as adjustable linear polarization at Ku-band. Both software simulations and test data are presented for the antenna performance.
Towards a Framework for Evaluating Mobile Mental Health Apps.

Mobile phones are ubiquitous in society and owned by a majority of psychiatric patients, including those with severe mental illness. Their versatility as a platform can extend mental health services in the areas of communication, self-monitoring, self-management, diagnosis, and treatment. However, the efficacy and reliability of publicly available applications (apps) have yet to be demonstrated. Numerous articles have noted the need for rigorous evaluation of the efficacy and clinical utility of smartphone apps, which are largely unregulated. Professional clinical organizations do not provide guidelines for evaluating mobile apps.



Guidelines and frameworks are needed to evaluate medical apps. Numerous frameworks and evaluation criteria exist from the engineering and informatics literature, as well as interdisciplinary organizations in similar fields such as telemedicine and healthcare informatics.



We propose criteria for both patients and providers to use in assessing not just smartphone apps, but also wearable devices and smartwatch apps for mental health. Apps can be evaluated by their usefulness, usability, and integration and infrastructure. Apps can be categorized by their usability in one or more stages of a mental health provider's workflow.



Ultimately, leadership is needed to develop a framework for describing apps, and guidelines are needed for both patients and mental health providers.