nils-herrmann/clean_scirepeval huggingface dataset:
Elastic textures for additive fabrication

We introduce elastic textures: a set of parametric, tileable, printable, cubic patterns achieving a broad range of isotropic elastic material properties: the softest pattern is over a thousand times softer than the stiffest, and the Poisson's ratios range from below zero to nearly 0.5. Using a combinatorial search over topologies followed by shape optimization, we explore a wide space of truss-like, symmetric 3D patterns to obtain a small family. This pattern family can be printed without internal support structure on a single-material 3D printer and can be used to fabricate objects with prescribed mechanical behavior. The family can be extended easily to create anisotropic patterns with target orthotropic properties. We demonstrate that our elastic textures are able to achieve a user-supplied varying material property distribution. We also present a material optimization algorithm to choose material properties at each point within an object to best fit a target deformation under a prescribed scenario. We show that, by fabricating these spatially varying materials with elastic textures, the desired behavior is achieved.
Combining RCC5 Relations with Betweenness Information

RCC5 is an important and well-known calculus for representing and reasoning about mereological relations. Among many other applications, it is pivotal in the formalization of commonsense reasoning about natural categories. In particular, it allows for a qualitative representation of conceptual spaces in the sense of Gärdenfors. To further the role of RCC5 as a vehicle for conceptual reasoning, in this paper we combine RCC5 relations with information about betweenness of regions. The resulting calculus allows us to express, for instance, that some part (but not all) of region B is between regions A and C. We show how consistency can be decided in polynomial time for atomic networks, even when regions are required to be convex. From an application perspective, the ability to express betweenness information allows us to use RCC5 as a basis for interpolative reasoning, while the restriction to convex regions ensures that all consistent networks can be faithfully represented as a conceptual space.
A methodology for power characterization of associative memories

Content Addressable Memories (CAM) have become increasingly more important in applications requiring high speed memory search due to their inherent massively parallel processing architecture. We present a complete power analysis methodology for CAM systems to aid the exploration of their power-performance trade-offs in future systems. Our proposed methodology uses detailed transistor level circuit simulation of power behavior and a handful of input data types to simulate full chip power consumption. Furthermore, we applied our power analysis methodology on a custom designed associative memory test chip. This chip was developed by Fermilab for the purpose of developing high performance real-time pattern recognition on high volume data produced by a future large-scale scientific experiment. We applied our methodology to configure a power model for this test chip. Our model is capable of predicting the total average power within 4% of actual power measurements. Our power analysis methodology can be generalized and applied to other CAM-like memory systems and accurately characterize their power behavior.
Generic SPWM technique for multilevel inverter

Multilevel inverters generate stepped AC signals and thus overcome many of the limitations with the classical inverter circuit. Multilevel inverters use many number of switches and generate staircase type output through controlled switching of various switches. The sequence of switching and the duration of each step are very vital in minimizing the THD. The methods of generating gating signals are not unique. Many methods that are proposed in literatures make use of SPWM technique. Sinusoidal Pulse Width Modulation (SPWM) is a popular control method widely used in power electronic inverter circuit. It has advantages like low switching losses, the output has fewer harmonic and method is easy to implement. The different SPWM methods are: (A) Phase Disposition PWM (PDPWM), (B) Phase Opposition Disposition PWM (PODPWM), (C) Alternative Opposition and Disposition PWM (APODPWM), (D) Phase Shift PWM (PSPWM), (E) Carrier Overlapping PWM (COPWM) and (F) Multi Carrier Sinusoidal Pulse Width Modulation with Variable Frequency (MCSPWMVF). All the above methods make use of many triangular signals, that are level shifted or phase shifted, and compare with a single sine wave to generate gating signals for the respective switches. The intersection of the sine signal with the various triangular signals will generate the gating signals for the respective switches. In the present investigation a generalized gating signal generation method is proposed using only one modulating and one carrier signal. The signal so generated is steered into various switches through pulse steering circuit developed in the present work.
Toward Abstractive Summarization Using Semantic Representations

We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR). In this framework, the source text is parsed to a set of AMR graphs, the graphs are transformed into a summary graph, and then text is generated from the summary graph. We focus on the graph-tograph transformation that reduces the source semantic graph into a summary graph, making use of an existing AMR parser and assuming the eventual availability of an AMR-totext generator. The framework is data-driven, trainable, and not specifically designed for a particular domain. Experiments on goldstandard AMR annotations and system parses show promising results. Code is available at: https://github.com/summarization
Autotagging Facebook: Social network context improves photo annotation

Most personal photos that are shared online are embedded in some form of social network, and these social networks are a potent source of contextual information that can be leveraged for automatic image understanding. In this paper, we investigate the utility of social network context for the task of automatic face recognition in personal photographs. We combine face recognition scores with social context in a conditional random field (CRF) model and apply this model to label faces in photos from the popular online social network Facebook, which is now the top photo-sharing site on the Web with billions of photos in total. We demonstrate that our simple method of enhancing face recognition with social network context substantially increases recognition performance beyond that of a baseline face recognition system.
An Extended Model of Cybercrime Investigations

A comprehensive model of cybercrime investigations is important for standardising terminology, defining requirements, and supporting the development of new techniques and tools for investigators. In this paper a model of investigations is presented which combines the existing models, generalises them, and extends them by explicitly addressing certain activities not included in them. Unlike previous models, this model explicitly represents the information flows in an investigation and captures the full scope of an investigation, rather than only the processing of evidence. The results of an evaluation of the model by practicing cybercrime investigators are presented. This new model is compared to some important existing models and applied to a real investigation.
Effects of Presowing Pulsed Electromagnetic Treatment of Tomato Seed on Growth, Yield, and Lycopene Content

The use of magnetic field as a presowing treatment has been adopted by researchers as a new environmental friendly technique. The aim of this study was to determine the effect of magnetic field exposure on tomato seeds covering a range of parameters such as transplanting percentage, plant height, shoot diameter, number of leaves per plant, fresh weight, dry weight, number of flowers, yield, and lycopene content. Pulsed electromagnetic field was used for 0, 5, 10, and 15 minutes as a presowing treatment of tomato seeds in a field experiment for two years. Papimi device (amplitude on the order of 12.5 mT) has been used. The use of pulsed electromagnetic field as a presowing treatment was found to enhance plant growth in tomato plants at certain duration of exposure. Magnetic field treatments and especially the exposure of 10 and 15 minutes gave the best results in all measurements, except plant height and lycopene content. Yield per plant was higher in magnetic field treatments, compared to control. MF-15 treatment yield was 80.93% higher than control treatment. Lycopene content was higher in magnetic field treatments, although values showed no statistically significant differences.
Smart Home Mobile RFID-Based Internet-of-Things Systems and Services

Smart homes can apply new Internet-Of-Things concepts along with RFID technologies for creating ubiquitous services. This paper introduces a novel read-out method for a hierarchical wireless master-slave RFID reader architecture of multi standard NFC (Near Field Communication) and UHF (Ultra High Frequency) technologies to build a smart home service system that benefits in terms of cost, energy consumption and complexity. Various smart home service use cases such as washing programs, cooking, shopping and elderly health care are described as examples that make use of this system.
A Study of Translation Edit Rate with Targeted Human Annotation

We examine a new, intuitive measure for evaluating machine-translation output that avoids the knowledge intensiveness of more meaning-based approaches, and the labor-intensiveness of human judgments. Translation Edit Rate (TER) measures the amount of editing that a human would have to perform to change a system output so it exactly matches a reference translation. We show that the single-reference variant of TER correlates as well with human judgments of MT quality as the four-reference variant of BLEU. We also define a human-targeted TER (or HTER) and show that it yields higher correlations with human judgments than BLEU—even when BLEU is given human-targeted references. Our results indicate that HTER correlates with human judgments better than HMETEOR and that the four-reference variants of TER and HTER correlate with human judgments as well as—or better than—a second human judgment does.
Metadata Embeddings for User and Item Cold-start Recommendations

I present a hybrid matrix factorisation model representing users and items as linear combinations of their content features’ latent factors. The model outperforms both collaborative and content-based models in cold-start or sparse interaction data scenarios (using both user and item metadata), and performs at least as well as a pure collaborative matrix factorisation model where interaction data is abundant. Additionally, feature embeddings produced by the model encode semantic information in a way reminiscent of word embedding approaches, making them useful for a range of related tasks such as tag recommendations.
Etiology of the Psychopathic Serial Killer : An Analysis of Antisocial Personality Disorder , Psychopathy , and Serial Killer Personality and Crime Scene Characteristics

The purpose of this article is tomake the distinction between antisocial personality disorder and psychopathy, discuss possible etiologies of psychopathy, and analyze the crimes, personality characteristics, and historical aspects of psychopathic serial killers. The research indicates that both environmental and biological factors affect the development of psychopathy. Several different serial killers were compared to assess the similarities and differences between their histories, crimes, and personalities. Though there were marked differences between their crimes, startling historical and personality similarities were clearly identified. Based on these findings, the validity and reliability of offender profiling is also discussed.
Predicting users' first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness

Users make lasting judgments about a website's appeal within a split second of seeing it for the first time. This first impression is influential enough to later affect their opinions of a site's usability and trustworthiness. In this paper, we demonstrate a means to predict the initial impression of aesthetics based on perceptual models of a website's colorfulness and visual complexity. In an online study, we collected ratings of colorfulness, visual complexity, and visual appeal of a set of 450 websites from 548 volunteers. Based on these data, we developed computational models that accurately measure the perceived visual complexity and colorfulness of website screenshots. In combination with demographic variables such as a user's education level and age, these models explain approximately half of the variance in the ratings of aesthetic appeal given after viewing a website for 500ms only.
Attitudes of adult 46, XY intersex persons to clinical management policies.

We surveyed a clinic sample of adult 46,XY intersex patients regarding attitudes to clinical management policies.



All adult former patients of 1 pediatric endocrine clinic in the eastern United States whose addresses could be obtained and who consented to participation were surveyed by a comprehensive written followup questionnaire. Three questions on attitudes concerning the desirability of a third gender category and the age at which genital surgery should be done were presented in the context of ratings of satisfaction with gender, genital status and sexual functioning.



A total of 72 English speaking patients with 46,XY, including 32 men and 40 women 18 to 60 years old, completed the questionnaire. The majority of respondents stated that they were mainly satisfied with being the assigned gender, did not have a time in life when they felt unsure about gender, did not agree to a third gender policy, did not think that the genitals looked unusual (although the majority of men rated their penis as too small), were somewhat or mainly satisfied with sexual functioning, did not agree that corrective genital surgery should be postponed to adulthood and stated that their genital surgeries should have been performed before adulthood, although there were some significant and important differences among subgroups.



The majority of adult patients with intersexuality appeared to be satisfied with gender and genital status, and did not support major changes in the prevailing policy. However, a significant minority was dissatisfied and endorsed policy changes.
WiDir: walking direction estimation using wireless signals

Despite its importance, walking direction is still a key context lacking a cost-effective and continuous solution that people can access in indoor environments. Recently, device-free sensing has attracted great attention because these techniques do not require the user to carry any device and hence could enable many applications in smart homes and offices. In this paper, we present WiDir, the first system that leverages WiFi wireless signals to estimate a human's walking direction, in a device-free manner. Human motion changes the multipath distribution and thus WiFi Channel State Information at the receiver end. WiDir analyzes the phase change dynamics from multiple WiFi subcarriers based on Fresnel zone model and infers the walking direction. We implement a proof-of-concept prototype using commercial WiFi devices and evaluate it in both home and office environments. Experimental results show that WiDir can estimate human walking direction with a median error of less than 10 degrees.
Structured light in scattering media

Virtually all structured light methods assume that the scene and the sources are immersed in pure air and that light is neither scattered nor absorbed. Recently, however, structured lighting has found growing application in underwater and aerial imaging, where scattering effects cannot be ignored. In this paper, we present a comprehensive analysis of two representative methods - light stripe range scanning and photometric stereo - in the presence of scattering. For both methods, we derive physical models for the appearances of a surface immersed in a scattering medium. Based on these models, we present results on (a) the condition for object detectability in light striping and (b) the number of sources required for photometric stereo. In both cases, we demonstrate that while traditional methods fail when scattering is significant, our methods accurately recover the scene (depths, normals, albedos) as well as the properties of the medium. These results are in turn used to restore the appearances of scenes as if they were captured in clear air. Although we have focused on light striping and photometric stereo, our approach can also be extended to other methods such as grid coding, gated and active polarization imaging.
Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning

End-to-end neural models show great promise towards building conversational agents that are trained from data and on-line experience using supervised and reinforcement learning. However, these models require a large corpus of dialogues to learn effectively. For goal-oriented dialogues, such datasets are expensive to collect and annotate, since each task involves a separate schema and database of entities. Further, the Wizard-of-Oz approach commonly used for dialogue collection does not provide sufficient coverage of salient dialogue flows, which is critical for guaranteeing an acceptable task completion rate in consumer-facing conversational agents. In this paper, we study a recently proposed approach for building an agent for arbitrary tasks by combining dialogue self-play and crowd-sourcing to generate fully-annotated dialogues with diverse and natural utterances. We discuss the advantages of this approach for industry applications of conversational agents, wherein an agent can be rapidly bootstrapped to deploy in front of users and further optimized via interactive learning from actual users of the system.
Data Sparsity Issues in the Collaborative Filtering Framework

With the amount of available information on the Web growing rapidly with each day, the need to automatically filter the information in order to ensure greater user efficiency has emerged. Within the fields of user profiling and Web personalization several popular content filtering techniques have been developed. In this chapter we present one of such techniques – collaborative filtering. Apart from giving an overview of collaborative filtering approaches, we present the experimental results of confronting the k-Nearest Neighbor (kNN) algorithm with Support Vector Machine (SVM) in the collaborative filtering framework using datasets with different properties. While the k-Nearest Neighbor algorithm is usually used for collaborative filtering tasks, Support Vector Machine is considered a state-of-the-art classification algorithm. Since collaborative filtering can also be interpreted as a classification/regression task, virtually any supervised learning algorithm (such as SVM) can also be applied. Experiments were performed on two standard, publicly available datasets and, on the other hand, on a real-life corporate dataset that does not fit the profile of ideal data for collaborative filtering. We conclude that the quality of collaborative filtering recommendations is highly dependent on the sparsity of available data. Furthermore, we show that kNN is dominant on datasets with relatively low sparsity while SVMbased approaches may perform better on highly sparse data.
Regional homogeneity approach to fMRI data analysis

Kendall's coefficient concordance (KCC) can measure the similarity of a number of time series. It has been used for purifying a given cluster in functional MRI (fMRI). In the present study, a new method was developed based on the regional homogeneity (ReHo), in which KCC was used to measure the similarity of the time series of a given voxel to those of its nearest neighbors in a voxel-wise way. Six healthy subjects performed left and right finger movement tasks in event-related design; five of them were additionally scanned in a rest condition. KCC was compared among the three conditions (left finger movement, right finger movement, and the rest). Results show that bilateral primary motor cortex (M1) had higher KCC in either left or right finger movement condition than in rest condition. Contrary to prediction and to activation pattern, KCC of ipsilateral M1 is significantly higher than contralateral M1 in unilateral finger movement conditions. These results support the previous electrophysiologic findings of increasing ipsilateral M1 excitation during unilateral movement. ReHo can consider as a complementary method to model-driven method, and it could help reveal the complexity of the human brain function. More work is needed to understand the neural mechanism underlying ReHo.
Predicting Students’ Performance using Modified ID3 Algorithm

The ability to predict performance of students is very crucial in our present education system. We can use data mining concepts for this purpose. ID3 algorithm is one of the famous algorithms present today to generate decision trees. But this algorithm has a shortcoming that it is inclined to attributes with many values. So , this research aims to overcome this shortcoming of the algorithm by using gain ratio(instead of information gain) as well as by giving weights to each attribute at every decision making point. Several other algorithms like J48 and Naive Bayes classification algorithm are also applied on the dataset. The WEKA tool was used for the analysis of J48 and Naive Bayes algorithms. The results are compared and presented. The dataset used in our study is taken from the School of Computing Sciences and Engineering (SCSE), VIT University. Keyworddata mining, educational data mining (EDM), decision tree, gain ratio, weighted ID3
Everyday Life Information Seeking : Approaching Information Seeking in the Context of “ Way of Life ”

The study offers a framework for the study of everyday life information seeking (ELIS) in the context of way of and mastery of life.Way of life is defined as the “order of things,” manifesting itself, for example, in the relationship between work and leisure time, models of consumption, and nature of hobbies. Mastery of life is interpreted as “keeping things in order; ” four ideal types of mastery of life with their implications for ELIS, namely optimistic-cognitive, pessimistic-cognitive, defensiveaffective and pessimistic-affective mastery of life are outlined. The article reviews two major dimensions of ELIS, there are. the seeking of orienting and practical information. The research framework was tested in an empirical study based on interviews with teachers and industrial workers, eleven of both. The main features of seeking orienting and practical information are reviewed, followed by suggestions for refinement of the research framework.
Image Generation from Captions Using Dual-Loss Generative Adversarial Networks

Deep Convolutional Generative Adversarial Networks (DCGANs) have become popular in recent months for their ability to effectively capture image distributions and generate realistic images. Recent work has also shown that conditional information provided to Generative Adversarial Networks (GANs) allows for deterministic control of generated images simply through the regulation of the conditional vector. Although many have hinted that language models could be used as conditional information for generating images from words, there are very few attempts using GANs, much less DCGANs. In this project we explore and analyze the results of image generation by encoding captions as conditional information to our DCCGAN. We use a subset of the MSCOCO dataset to evaluate our results.