nils-herrmann/clean_scirepeval huggingface dataset:
On seeing human: a three-factor theory of anthropomorphism.

Anthropomorphism describes the tendency to imbue the real or imagined behavior of nonhuman agents with humanlike characteristics, motivations, intentions, or emotions. Although surprisingly common, anthropomorphism is not invariant. This article describes a theory to explain when people are likely to anthropomorphize and when they are not, focused on three psychological determinants--the accessibility and applicability of anthropocentric knowledge (elicited agent knowledge), the motivation to explain and understand the behavior of other agents (effectance motivation), and the desire for social contact and affiliation (sociality motivation). This theory predicts that people are more likely to anthropomorphize when anthropocentric knowledge is accessible and applicable, when motivated to be effective social agents, and when lacking a sense of social connection to other humans. These factors help to explain why anthropomorphism is so variable; organize diverse research; and offer testable predictions about dispositional, situational, developmental, and cultural influences on anthropomorphism. Discussion addresses extensions of this theory into the specific psychological processes underlying anthropomorphism, applications of this theory into robotics and human-computer interaction, and the insights offered by this theory into the inverse process of dehumanization.
Variability in findings of anogenital injury in consensual and non-consensual fisting intercourse: A systematic review.

Fisting is an uncommon and potentially dangerous sexual practice. This is usually a homosexual activity, but can also be a heterosexual or an autoerotic practice. A systematic review of the forensic literature yielded 14 published studies from 8 countries between 1968 and 2016 that met the inclusion/exclusion criteria, illustrating that external anogenital (anal and/or genital) trauma due to fisting is observed in 22.2% and 88.8% (reported consensual and non-consensual intercourse, respectively) of the subjects, while internal injuries are observed in the totality of the patients. Establishing the reliability of the conclusions of these studies is difficult due to a lack of uniformity in methodology used to detect and define injuries. Taking this limit into account, the aim of this article is to give a description of the external and internal injuries subsequent to reported consensual and non-consensual fisting practice, and try to find a relation between this sexual practice, the morphology of the injuries, the correlation with the use of drugs, and the relationship with assailant, where possible. The findings reported in this paper could be useful, especially when concerns of sexual assault arise.
SimApp: A Framework for Detecting Similar Mobile Applications by Online Kernel Learning

With the popularity of smart phones and mobile devices, the number of mobile applications (a.k.a. "apps") has been growing rapidly. Detecting semantically similar apps from a large pool of apps is a basic and important problem, as it is beneficial for various applications, such as app recommendation, app search, etc. However, there is no systematic and comprehensive work so far that focuses on addressing this problem. In order to fill this gap, in this paper, we explore multi-modal heterogeneous data in app markets (e.g., description text, images, user reviews, etc.), and present "SimApp" -- a novel framework for detecting similar apps using machine learning. Specifically, it consists of two stages: (i) a variety of kernel functions are constructed to measure app similarity for each modality of data; and (ii) an online kernel learning algorithm is proposed to learn the optimal combination of similarity functions of multiple modalities. We conduct an extensive set of experiments on a real-world dataset crawled from Google Play to evaluate SimApp, from which the encouraging results demonstrate that SimApp is effective and promising.
Performance Analysis of Reduced Common-Mode Voltage PWM Methods and Comparison With Standard PWM Methods for Three-Phase Voltage-Source Inverters

This paper surveys the reduced common-mode voltage pulsewidth modulation (RCMV-PWM) methods for three-phase voltage-source inverters, investigates their performance characteristics, and provides a comparison with the standard PWM methods. PWM methods are reviewed, and their pulse patterns and common-mode voltage (CMV) patterns are illustrated. The inverter input and output current ripple characteristics and output voltage linearity characteristics of each PWM method are thoroughly investigated by analytical methods, simulations, and experiments. The research results illustrate the advantages and disadvantages of the considered methods, and suggest the utilization of the near-state PWM and active zero state PWM1 methods as overall superior methods. The paper aids in the selection and application of appropriate PWM methods in inverter drives with low CMV requirements.
Compositional Vector Space Models for Knowledge Base Inference

Traditional approaches to knowledge base completion have been based on symbolic representations. Lowdimensional vector embedding models proposed recently for this task are attractive since they generalize to possibly unlimited sets of relations. A significant drawback of previous embedding models for KB completion is that they merely support reasoning on individual relations (e.g., bornIn(X,Y )⇒ nationality(X,Y )). In this work, we develop models for KB completion that support chains of reasoning on paths of any length using compositional vector space models. We construct compositional vector representations for the paths in the KB graph from the semantic vector representations of the binary relations in that path and perform inference directly in the vector space. Unlike previous methods, our approach can generalize to paths that are unseen in training and, in a zero-shot setting, predict target relations without supervised training data for that relation.
Neural Semantic Parsing with Type Constraints for Semi-Structured Tables

We present a new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables. Our parser is an encoder-decoder neural network with two key technical innovations: (1) a grammar for the decoder that only generates well-typed logical forms; and (2) an entity embedding and linking module that identifies entity mentions while generalizing across tables. We also introduce a novel method for training our neural model with question-answer supervision. On the WIKITABLEQUESTIONS data set, our parser achieves a state-of-theart accuracy of 43.3% for a single model and 45.9% for a 5-model ensemble, improving on the best prior score of 38.7% set by a 15-model ensemble. These results suggest that type constraints and entity linking are valuable components to incorporate in neural semantic parsers.
Sense of Relatedness as a Factor in Children ’ s Academic Engagement and Performance

Children’s sense of relatedness is vital to their academic motivation from 3rd to 6th grade. Children’s (n 641) reports of relatedness predicted changes in classroom engagement over the school year and contributed over and above the effects of perceived control. Regression and cumulative risk analyses revealed that relatedness to parents, teachers, and peers each uniquely contributed to students’ engagement, especially emotional engagement. Girls reported higher relatedness than boys, but relatedness to teachers was a more salient predictor of engagement for boys. Feelings of relatedness to teachers dropped from 5th to 6th grade, but the effects of relatedness on engagement were stronger for 6th graders. Discussion examines theoretical, empirical, and practical implications of relatedness as a key predictor of children’s academic motivation and performance.
Face Matching Between Near Infrared and Visible Light Images

In many applications, such as E-Passport and driver’s license, the enrollment of face templates is done using visible light (VIS) face images. Such images are normally acquired in controlled environment where the lighting is approximately frontal. However, Authentication is done in variable lighting conditions. Matching of faces in VIS images taken in different lighting conditions is still a big challenge. A recent development in near infrared (NIR) image based face recognition  has well overcome the difficulty arising from lighting changes. However, it requires that enrollment face images be acquired using NIR as well. In this paper, we present a new problem, that of matching a face in an NIR image against one in a VIS images, and propose a solution to it. The work is aimed to develop a new solution for meeting the accuracy requirement of face-based biometric recognition, by taking advantages of the recent NIR face technology while allowing the use of existing VIS face photos as gallery templates. Face recognition is done by matching an NIR probe face against a VIS gallery face. Based on an analysis of properties of NIR and VIS face images, we propose a learningbased approach for the different modality matching. A mechanism of correlation between NIR and VIS faces is learned from NIR→VIS face pairs, and the learned correlation is used to evaluate similarity between an NIR face and a VIS face. We provide preliminary results of NIR→VIS face matching for recognition under different illumination conditions. The results demonstrate advantages of NIR→VIS matching over VIS→VIS matching.
Architecture exploration for ambient energy harvesting nonvolatile processors

Energy harvesting has been widely investigated as a promising method of providing power for ultra-low-power applications. Such energy sources include solar energy, radio-frequency (RF) radiation, piezoelectricity, thermal gradients, etc. However, the power supplied by these sources is highly unreliable and dependent upon ambient environment factors. Hence, it is necessary to develop specialized systems that are tolerant to this power variation, and also capable of making forward progress on the computation tasks. The simulation platform in this paper is calibrated using measured results from a fabricated nonvolatile processor and used to explore the design space for a nonvolatile processor with different architectures, different input power sources, and policies for maximizing forward progress.
Synthesizing highly expressive SQL queries from input-output examples

SQL is the de facto language for manipulating relational data. Though powerful, many users find it difficult to write SQL queries due to highly expressive constructs. 
 While using the programming-by-example paradigm to help users write SQL queries is an attractive proposition, as evidenced by online help forums such as Stack Overflow, developing techniques for synthesizing SQL queries from given input-output (I/O) examples has been difficult, due to the large space of SQL queries as a result of its rich set of operators. 
 
 In this paper, we present a new scalable and efficient algorithm for synthesizing SQL queries based on I/O examples. The key innovation of our algorithm is development of a language for abstract queries, i.e., queries with uninstantiated operators, that can be used to express a large space of SQL queries efficiently. Using abstract queries to represent the search space nicely decomposes the synthesis problem into two tasks: 1) searching for abstract queries that can potentially satisfy the given I/O examples, and 2) instantiating the found abstract queries and ranking the results. 
 
 We have implemented this algorithm in a new tool called Scythe and evaluated it using 193 benchmarks collected from Stack Overflow. Our evaluation shows that Scythe can efficiently solve 74% of the benchmarks, most in just a few seconds, and the queries range from simple ones involving a single selection to complex queries with 6 nested subqueires.
Toward the Human-Robot Co-Existence Society: On Safety Intelligence for Next Generation Robots

Technocrats from many developed countries, especially Japan and South Korea, are preparing for the human–robot co-existence society that they believe will emerge by 2030. Regulators are assuming that within the next two decades, robots will be capable of adapting to complex, unstructured environments and interacting with humans to assist with the performance of daily life tasks. Unlike heavily regulated industrial robots that toil in isolated settings, Next Generation Robots will have relative autonomy, which raises a number of safety issues that are the focus of this article. Our purpose is to describe a framework for a legal system focused on Next Generation Robots safety issues, including a Safety Intelligence concept that addresses robot Open-Texture Risk. We express doubt that a model based on Isaac Asimov’s Three Laws of Robotics can ever be a suitable foundation for creating an artificial moral agency ensuring robot safety. Finally, we make predictions about the most significant Next Generation Robots safety issues that will arise as the human–robot co-existence society
FINAL: Fast Attributed Network Alignment

Multiple networks naturally appear in numerous high-impact applications. Network alignment (i.e., finding the node correspondence across different networks) is often the very first step for many data mining tasks. Most, if not all, of the existing alignment methods are solely based on the topology of the underlying networks. Nonetheless, many real networks often have rich attribute information on nodes and/or edges. In this paper, we propose a family of algorithms FINAL to align attributed networks. The key idea is to leverage the node/edge attribute information to guide (topology-based) alignment process. We formulate this problem from an optimization perspective based on the alignment consistency principle, and develop effective and scalable algorithms to solve it. Our experiments on real networks show that (1) by leveraging the attribute information, our algorithms can significantly improve the alignment accuracy (i.e., up to a 30% improvement over the existing methods); (2) compared with the exact solution, our proposed fast alignment algorithm leads to a more than 10 times speed-up, while preserving a 95% accuracy; and (3) our on-query alignment method scales linearly, with an around 90% ranking accuracy compared with our exact full alignment method and a near real-time response time.
Memory-based language processing: psycholinguistic research in the 1990s.

There are two main domains of research in psycholinguistics: sentence processing, concerned with how the syntactic structures of sentences are computed, and text processing, concerned with how the meanings of larger units of text are understood. In recent sentence processing research, a new and controversial theme is that syntactic computations may rely heavily on statistical information about the relative frequencies with which different syntactic structures occur in the language. In text processing, recent research has focused on what information the words and ideas of a text evoke from long-term memory quickly, passively, and at low processing cost. Research in both domains has begun to use the information that can be obtained from large corpora of naturally occurring texts.
Reporting results of cancer treatment.

On the initiative of the World Health Organization, two meetings on the Standardization of Reporting Results of Cancer Treatment have been held with representatives and members of several organizations. Recommendations have been developed for standardized approaches to the recording of baseline data relating to the patient, the tumor, laboratory and radiologic data, the reporting of treatment, grading of acute and subacute toxicity, reporting of response, recurrence and disease-free interval, and reporting results of therapy. These recommendations, already endorsed by a number of organizations, are proposed for international acceptance and use to make it possible for investigators to compare validly their results with those of others.
Integrating BIM and GIS for large scale ( building ) asset management : a critical review

Widely-dispersed assets such as buildings, equipment, roads and bridges present local governments as well as big organizations with a number of asset management challenges. Recently financial sustainability and competitiveness have put more pressures on owner/managers to minimize asset total cost of ownership and streamline their asset management operations. In general asset management operation is the systematic process of maintaining, upgrading and operating physical assets.
Location-aware access to hospital information and services

Hospital workers are highly mobile; they are constantly changing location to perform their daily work, which includes visiting patients, locating resources, such as medical records, or consulting with other specialists. The information required by these specialists is highly dependent on their location. Access to a patient's laboratory results might be more relevant when the physician is near the patient's bed and not elsewhere. We describe a location-aware medical information system that was developed to provide access to resources such as patient's records or the location of a medical specialist, based on the user's location. The system is based on a handheld computer which includes a trained backpropagation neural-network used to estimate the user's location and a client to access information from the hospital information system that is relevant to the user's current location.
From Collision To Exploitation: Unleashing Use-After-Free Vulnerabilities in Linux Kernel

Since vulnerabilities in Linux kernel are on the increase, attackers have turned their interests into related exploitation techniques. However, compared with numerous researches on exploiting use-after-free vulnerabilities in the user applications, few efforts studied how to exploit use-after-free vulnerabilities in Linux kernel due to the difficulties that mainly come from the uncertainty of the kernel memory layout. Without specific information leakage, attackers could only conduct a blind memory overwriting strategy trying to corrupt the critical part of the kernel, for which the success rate is negligible.
 In this work, we present a novel memory collision strategy to exploit the use-after-free vulnerabilities in Linux kernel reliably. The insight of our exploit strategy is that a probabilistic memory collision can be constructed according to the widely deployed kernel memory reuse mechanisms, which significantly increases the success rate of the attack. Based on this insight, we present two practical memory collision attacks: An object-based attack that leverages the memory recycling mechanism of the kernel allocator to achieve freed vulnerable object covering, and a physmap-based attack that takes advantage of the overlap between the physmap and the SLAB caches to achieve a more flexible memory manipulation. Our proposed attacks are universal for various Linux kernels of different architectures and could successfully exploit systems with use-after-free vulnerabilities in kernel. Particularly, we achieve privilege escalation on various popular Android devices (kernel version>=4.3) including those with 64-bit processors by exploiting the CVE-2015-3636 use-after-free vulnerability in Linux kernel. To our knowledge, this is the first generic kernel exploit for the latest version of Android. Finally, to defend this kind of memory collision, we propose two corresponding mitigation schemes.
Simulation and Hardware Implementation of Incremental Conductance MPPT With Direct Control Method Using Cuk Converter

This paper presents simulation and hardware implementation of incremental conductance (IncCond) maximum power point tracking (MPPT) used in solar array power systems with direct control method. The main difference of the proposed system to existing MPPT systems includes elimination of the proportional-integral control loop and investigation of the effect of simplifying the control circuit. Contributions are made in several aspects of the whole system, including converter design, system simulation, controller programming, and experimental setup. The resultant system is capable of tracking MPPs accurately and rapidly without steady-state oscillation, and also, its dynamic performance is satisfactory. The IncCond algorithm is used to track MPPs because it performs precise control under rapidly changing atmospheric conditions. MATLAB and Simulink were employed for simulation studies, and Code Composer Studio v3.1 was used to program a TMS320F2812 digital signal processor. The proposed system was developed and tested successfully on a photovoltaic solar panel in the laboratory. Experimental results indicate the feasibility and improved functionality of the system.
Zero-current-switching switched-capacitor bidirectional DC – DC converter

The proposed zero-current-switching switched-capacitor quasi-resonant DC–DC converter is a new type of bidirectional power flow control conversion scheme. It possesses the conventional features of resonant switched-capacitor converters: low weight, small volume, high efficiency, low EMI emission and current stress. A zero-current-switching switched-capacitor stepup/step-down bidirectional converter is presented that can improve the current stress problem during bidirectional power flow control processing. It can provide a high voltage conversion ratio using four power MOSFET main switches, a set of switched capacitors and a small resonant inductor. The converter operating principle of the proposed bidirectional power conversion scheme is described in detail with circuit model analysis. Simulation and experiment are carried out to verify the concept and performance of the proposed bidirectional DC–DC converter.
Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks

Retinal vessel segmentation is an indispensable step for automatic detection of retinal diseases with fundoscopic images. Though many approaches have been proposed, existing methods tend to miss fine vessels or allow false positives at terminal branches. Let alone undersegmentation, over-segmentation is also problematic when quantitative studies need to measure the precise width of vessels. In this paper, we present a method that generates the precise map of retinal vessels using generative adversarial training. Our methods achieve dice coefficient of 0.829 on DRIVE dataset and 0.834 on STARE dataset which is the state-of-the-art performance on both datasets.