nils-herrmann/clean_scirepeval huggingface dataset:
Hierarchical interpretations for neural network predictions

Deep neural networks (DNNs) have achieved impressive predictive performance due to their ability to learn complex, non-linear relationships between variables. However, the inability to effectively visualize these relationships has led to DNNs being characterized as black boxes and consequently limited their applications. To ameliorate this problem, we introduce the use of hierarchical interpretations to explain DNN predictions through our proposed method, agglomerative contextual decomposition (ACD). Given a prediction from a trained DNN, ACD produces a hierarchical clustering of the input features, along with the contribution of each cluster to the final prediction. This hierarchy is optimized to identify clusters of features that the DNN learned are predictive. Using examples from Stanford Sentiment Treebank and ImageNet, we show that ACD is effective at diagnosing incorrect predictions and identifying dataset bias. Through human experiments, we demonstrate that ACD enables users both to identify the more accurate of two DNNs and to better trust a DNN’s outputs. We also find that ACD’s hierarchy is largely robust to adversarial perturbations, implying that it captures fundamental aspects of the input and ignores spurious noise.
A Comparative Study of the Differences in Family Communication among Young Urban Adult Consumers and the Development of their Materialistic Values : A Discriminant Analysis

The study attempts to uncover the characteristics of materialism groups in Malaysia. It assesses the differences between materialism groups, i.e., low and high materialistic groups, using demographic and family communication dimensions. The data was collected through self-administered questionnaires. The sample consisted of 956 respondents. The majority of the respondents were Malays followed by Chinese and Indians. The proportion of female respondents was higher than the male respondents. Most of the respondents were single and in the age group of between 19-29 years old. Independent sample t-tests were used to compare mean scores for the study variables between ‘high’ materialism and ‘low’ materialism groups and significant mean differences were found between ‘high’ and ‘low’ materialism groups in terms of socio-oriented family communication construct. Specifically, it was found that the ‘high’ materialism group has considerably greater ratings on the constructs. Internal consistency reliability assessment using Cronbach coefficient alpha revealed that all the three dimensions had high reliability. A stepwise discriminant analysis performed on two family communication variables found that one variable was significant in differentiating the two materialism groups. The variable was socio-oriented family communication. The implications, significance and limitations of the study are discussed.
Sentiment classification for Turkish Twitter feeds using LDA

Today, people are able to express their opinions and ideas on any subject using personal blogs or a variety of social media environments. Contents shared by users with each passing day increases the data size on the web but this situation is a complication for meaningful information extraction. Therefore, sentiment analysis studies aimed at automatically obtaining meaningful information from social media content showed a significant increase in recent years. In these studies, automatically analyzing the content of messages and identifying sentiment such as positive, neutral, negative etc. are intended. In this study, a system has been proposed for analyzing sentiment of Turkish Twitter feeds. In addition, sentiment classification which is a leading problem for sentiment analysis studies has been performed using topic modeling and the effect of modeling on results are examined. The topic model has been created using LDA (Latent Dirichlet Allocation) algorithm. Experimental results obtained with different feature extraction model and classifiers. The results show that topic modeling is more successful compared to the methods used in the previous studies. Also, sentiment classification based on topic model knowledge has been found to increase sentiment analysis success by %26 compared to our previous work.
Comparison of NRZ and RZ modulations in laser intersatellite communication systems

Laser intersatellite communication (LIC) is a promising choice for intersatellite communication due to the high data rate that can be achieved with the advantages of light weight, smaller size and lower power consumption. In this paper, we compare the maximum transmission distance with non-return-to-zero (NRZ) and return-to-zero (RZ) modulations for two different LIC scenarios. Simulation results show that for the long-range LIC system with a saturated booster optical amplifier, the RZ modulation scheme can offer a longer transmission distance than the NRZ modulation scheme. For the short-range LIC system without an optical amplifier, the RZ modulation scheme performs almost the same as the NRZ modulation scheme.
Evolutionary Bayesian Rose Trees

We present an evolutionary multi-branch tree clustering method to model hierarchical topics and their evolutionary patterns overtime. The method builds evolutionary trees in a Bayesian online filtering framework. The tree construction is formulated as an online posterior estimation problem, which well balances both the fitness of the current tree and the smoothness between trees. The state-of-the-art multi-branch clustering method, Bayesian rose trees, is employed to generate a topic tree with a high fitness value. A constraint model is also introduced to preserve the smoothness between trees. A set of comprehensive experiments on real world news data demonstrates that the proposed method better incorporates historical tree information and is more efficient and effective than the traditional evolutionary hierarchical clustering algorithm. In contrast to our previous method , we implement two additional baseline algorithms to compare them with our algorithm. We also evaluate the performance of the clustering algorithm based on multiple constraint trees. Furthermore, two case studies are conducted to demonstrate the effectiveness and usefulness of our algorithm in helping users understand the major hierarchical topic evolutionary patterns in text data.
Multiuser Resource Allocation for Mobile-Edge Computation Offloading

Mobile-edge computation offloading (MECO) offloads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we consider resource allocation in a MECO system comprising multiple users that time share a single edge cloud and have different computation loads. The optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under constraint on computation latency and for both the cases of infinite and finite edge cloud computation capacities. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Computing the threshold requires iterative computation. To reduce the complexity, a sub-optimal resource-allocation algorithm is proposed and shown by simulation to have close-to-optimal performance.
REPAIRING PROGRAMS WITH SEMANTIC CODE SEARCH

Automated program repair can potentially reduce debugging costs and improve software quality but recent studies have drawn attention to shortcomings in the quality of automatically generated repairs. We propose a new kind of repair that uses the large body of existing open-source code to find potential fixes. The key challenges lie in efficiently finding code semantically similar (but not identical) to defective code and then appropriately integrating that code into a buggy program. We present SearchRepair, a repair technique that addresses these challenges by (1) encoding a large database of human-written code fragments as SMT constraints on input-output behavior, (2) localizing a given defect to likely buggy program fragments and deriving the desired input-output behavior for code to replace those fragments, (3) using state-of-the-art constraint solvers to search the database for fragments that satisfy that desired behavior and replacing the likely buggy code with these potential patches, and (4) validating that the patches repair the bug against program test suites. We find that SearchRepair repairs 150 (19%) of 778 benchmark C defects written by novice students, 20 of which are not repaired by GenProg, TrpAutoRepair, and AE. We compare the quality of the patches generated by the four techniques by measuring how many independent, not-used-during-repair tests they pass, and find that SearchRepair-repaired programs pass 97.3% of the tests, on average, whereas GenProg-, TrpAutoRepair-, and AE-repaired programs pass 68.7%, 72.1%, and 64.2% of the tests, respectively. We conclude that SearchRepair produces higher-quality repairs than GenProg, TrpAutoRepair, and AE, and repairs some defects those tools cannot.
The use of thermal ir array sensor for indoor fall detection

This paper presents new approach for unobtrusive indoor fall detection by an IR thermal array sensor. Unlike existing methods that run fall detection at server and require high communication and processing rates, we perform fall detection within the sensor node by a computationally inexpensive algorithm that signals the server only when a fall occurs. Experiments with prototype design show that such formulation provides robust and real-time fall detection even in a noisy environment.
Multivalued logics: a uniform approach to reasoning in artificial intelligence

This paper describes a uniform formalization of much of the current work in artificial intelligence on inference systems. We show that many of these systems, including first-order theorem provers, assumption-based truth maintenance systems (ATMSS), and unimplemented formal systems such as default logic or circumscription, can be subsumed under a single general framework. We begin by defining this framework, which is based on a mathematical structure known as a bilattice. We present a formal definition of inference using this structure and show that this definition generalizes work involving ATMSS and some simple nonmonotonic logics. Following the theoretical description, we describe a constructive approach to inference in this setting; the resulting generalization of both conventional inference and ATMSS is achieved without incurring any substantial computational overhead. We show that our approach can also be used to implement a default reasoner, and discuss a combination of default and ATMS methods that enables us to formally describe an “incremental” default reasoning system. This incremental system does not need to perform consistency checks before drawing tentative conclusions, but can instead adjust its beliefs when a default premise or conclusion is overturned in the face of convincing contradictory evidence. The system is therefore much more computationally viable than earlier approaches. Finally, we discuss the implementation of our ideas. We begin by considering general issues that need to be addressed when implementing a multivalued approach such as that we are proposing, and then turn to specific examples showing the results of an existing implementation. This single implementation is used to solve a digital simulation task using first-order logic, a diagnostic task using ATMSS as suggested by de Kleer and Williams, a problem in default reasoning as in Reiter’s default logic or McCarthy’s circumscription, and to solve the same problem more efficiently by combining default methods with justification information. All of these applications use the same general-purpose bilattice theorem prover and differ only in the choice of bilattice being considered.
Mining Social Entrepreneurship Strategies Using Topic Modeling

Despite the burgeoning research on social entrepreneurship (SE), SE strategies remain poorly understood. Drawing on extant research on the social activism and social change, empowerment and SE models, we explore, classify and validate the strategies used by 2,334 social entrepreneurs affiliated with the world's largest SE support organization, Ashoka. The results of the topic modeling of the social entrepreneurs' strategy profiles reveal that they employed a total of 39 change-making strategies that vary across resources (material versus symbolic strategies), specificity (general versus specific strategies), and mode of participation (mass versus elite participation strategies); they also vary across fields of practice and time. Finally, we identify six meta-SE strategies-a reduction from the 39 strategies-and identify four new meta-SE strategies (i.e., system reform, physical capital development, evidence-based practices, and prototyping) that have been overlooked in prior SE research. Our findings extend and deepen the research into SE strategies and offer a comprehensive model of SE strategies that advances theory, practice and policy making.
Modeling and Control of Brushless Doubly-Fed Induction Generators in Wind Energy Applications

In this paper, brushless doubly fed induction generator (BDFIG) used for the wind energy conversion system is proposed. The BDFIG employs two cascaded induction machines to eliminate the brushes and copper rings in the traditional DFIG. The dynamic model of BDFIG with two machines' rotor connected is presented and the control strategy for flexible power flow control is developed. The independent control of the active and reactive power flow is achieved by means of a four-quadrant power converter under the closed-loop stator flux oriented control scheme. The simulation results verified the control algorithm.
Investigating performance of students: a longitudinal study

This paper, investigates how academic performance of students evolves over the years in their studies during a study programme. To determine typical progression patterns over the years, students are described by a 4 tuple (e.g. x1, x2, x3, x4), these being the clusters' mean to which a student belongs to in each year of the degree. For this purpose, two consecutive cohorts have been analyzed using X-means clustering. Interestingly the patterns found in both cohorts show that a substantial number of students stay in the same kind of groups during their studies.
Combining classifiers: A theoretical framework

The problem of classifier combination is considered in the context of the two main fusion scenarios: fusion of opinions based on identical and on distinct representations. We develop a theoretical framework for classifier combination for these two scenarios. For multiple experts using distinct representations we argue that many existing schemes such as the product rule, sum rule, min rule, max rule, majority voting, and weighted combination, can be considered as special cases of compound classification. We then consider the effect of classifier combination in the case of multiple experts using a shared representation where the aim of fusion is to obtain a better estimate of the appropriatea posteriori class probabilities. We also show that the two theoretical frameworks can be used for devising fusion strategies when the individual experts use features some of which are shared and the remaining ones distinct. We show that in both cases (distinct and shared representations), the expert fusion involves the computation of a linear or nonlinear function of thea posteriori class probabilities estimated by the individual experts. Classifier combination can therefore be viewed as a multistage classification process whereby thea posteriori class probabilities generated by the individual classifiers are considered as features for a second stage classification scheme. Most importantly, when the linear or nonlinear combination functions are obtained by training, the distinctions between the two scenarios fade away, and one can view classifier fusion in a unified way.
Analysis of Different Clustering Techniques in Data and Text Mining

In recent days clustering becomes important in pattern detection, unsupervised learning process, data concept construction, information retrieval, text mining, web analysis, marketing and medical diagnostic. The purpose of this paper is an attempt to reconnoiter some of the important clustering techniques in the data mining literature and to compare some aspects of clustering algorithms which contains performance, order of input, accuracy, scalability, shapes discovered, dimensionality and dealing with noisy data. The algorithms are Partitional approach, hierarchical approach, seeded approach, ontology approach, concept based approach. KeywordsClustering; Semi Supervised; Ontology; Semantic; Constraint; Similarity measures;
The Impact of Nonlinear Junction Capacitance on Switching Transient and Its Modeling for SiC MOSFET

The nonlinear junction capacitances of power devices are critical for the switching transient, which should be fully considered in the modeling and transient analysis, especially for high-frequency applications. The silicon carbide (SiC) MOSFET combined with SiC Schottky Barrier Diode (SBD) is recognized as the proposed choice for high-power and high-frequency converters. However, in the existing SiC MOSFET models only the nonlinearity of gate-drain capacitance is considered meticulously, but the drain-source capacitance, which affects the switching commutation process significantly, is generally regarded as constant. In addition, the nonlinearity of diode junction capacitance is neglected in some simplified analysis. Experiments show that without full consideration of nonlinear junction capacitances, some significant deviations between simulated and measured results will emerge in the switching waveforms. In this paper, the nonlinear characteristics of drain-source capacitance in SiC MOSFET are studied in detail, and the simplified modeling methods for engineering applications are presented. On this basis, the SiC MOSFET model is improved and the simulation results with improved model correspond with the measured results much better than before, which verify the analysis and modeling.
HeNet: A Deep Learning Approach on Intel$^\circledR$ Processor Trace for Effective Exploit Detection

This paper presents HeNet, a hierarchical ensemble neural network, applied to classify hardware-generated control flow traces for malware detection. Deep learning-based malware detection has so far focused on analyzing executable files and runtime API calls. Static code analysis approaches face challenges due to obfuscated code and adversarial perturbations. Behavioral data collected during execution is more difficult to obfuscate but recent research has shown successful attacks against API call based malware classifiers. We investigate control flow based characterization of a program execution to build robust deep learning malware classifiers. HeNet consists of a low-level behavior model and a toplevel ensemble model. The low-level model is a per-application behavior model, trained via transfer learning on a time-series of images generated from control flow trace of an execution. We use Intel R © Processor Trace enabled processor for low overhead execution tracing and design a lightweight image conversion and segmentation of the control flow trace. The top-level ensemble model aggregates the behavior classification of all the trace segments and detects an attack. The use of hardware trace adds portability to our system and the use of deep learning eliminates the manual effort of feature engineering. We evaluate HeNet against real-world exploitations of PDF readers. HeNet achieves 100% accuracy and 0% false positive on test set, and higher classification accuracy compared to classical machine learning algorithms.
Formula Derivation and Verification of Characteristic Impedance for Offset Double-Sided Parallel Strip Line (DSPSL)

Double-sided parallel strip line (DSPSL) is a versatile microwave transmission line. With an offset, it allows for large variations of the characteristic impedance and therefore flexibility for the design of novel microwave devices. In this letter, a formula for the characteristic impedance of DSPSL is derived by the fuzzy EM method and verified by commercial software. Very good agreement is observed.
Analysing B2B electronic procurement benefits: information systems perspective

This paper aims to present electronic procurement benefits identified in four case companies from the information technology (IT), hi-tech sector.  Multi-case study design was applied. The benefits reported in the companies were analysed and classified according to taxonomies from the information systems discipline. Finally, a new benefits classification was proposed. The framework was developed based on information systems literature.  The research confirmed difficulties with benefits evaluation, as, apart from operational benefits, non-financial, intangible benefits at strategic level were also identified. Traditional evaluation methods are unable to capture all benefits categories, especially at strategic level. New taxonomy was created, which allows evaluation of the complex e-procurement impact. In the proposed taxonomy, e-procurement benefits are classified according to their level (operational, tactical, strategic), area of impact, applying scorecard dimensions (customer, process, financial, learning and growth). In addition the benefits characteristic is captured (tangible, intangible, financial and non-financial).  Research is based on four case studies only. Findings are specific to case companies and the environment in which they operate. The framework should be tested further in different contexts.  The new taxonomy allows evaluation of the complex e-procurement impact, demonstrating that benefits achieved do not concern merely the financial impact. The framework can be applied to preparing new systems implementation as well as to evaluating existing systems.  The paper applies information systems frameworks to the electronic procurement field, which allows one to look at e-procurement systems considering its complex impact. The framework can also be used to evaluate different systems, not simply e-procurement.
Brain—Computer Interface

Human-computer interfaces (HCIs) have become ubiquitous. Interfaces such as keyboards and mouses are used daily while interacting with computing devices (Ebrahimi et al., 2003). There is a developing need, however, for HCIs that can be used in situations where these typical interfaces are not viable. Direct brain-computer interfaces (BCI) is a developing field that has been adding this new dimension of functionality to HCI. BCI has created a novel communication channel, especially for those users who are unable to generate necessary muscular movements to use typical HCI devices.
Three Implementations of SquishQL, a Simple RDF Query Language

RDF provides a basic way to represent data for the Semantic Web. We have been experimenting with the query paradigm for working with RDF data in semantic web applications. Query of RDF data provides a declarative access mechanism that is suitable for application usage and remote access. We describe work on a conceptual model for querying RDF data that refines ideas first presented in at the W3C workshop on Query Languages  and the design of one possible syntax, derived from [7], that is suitable for application programmers. Further, we present experience gained in three implementations of the query language.