nils-herrmann/clean_scirepeval huggingface dataset:
Sympathy Begins with a Smile, Intelligence Begins with a Word: Use of Multimodal Features in Spoken Human-Robot Interaction

Recognition of social signals, from human facial expressions or prosody of speech, is a popular research topic in human-robot interaction studies. There is also a long line of research in the spoken dialogue community that investigates user satisfaction in relation to dialogue characteristics. However, very little research relates a combination of multimodal social signals and language features detected during spoken face-to-face human-robot interaction to the resulting user perception of a robot. In this paper we show how different emotional facial expressions of human users, in combination with prosodic characteristics of human speech and features of human-robot dialogue, correlate with users’ impressions of the robot after a conversation. We find that happiness in the user’s recognised facial expression strongly correlates with likeability of a robot, while dialogue-related features (such as number of human turns or number of sentences per robot utterance) correlate with perceiving a robot as intelligent. In addition, we show that facial expression, emotional features, and prosody are better predictors of human ratings related to perceived robot likeability and anthropomorphism, while linguistic and non-linguistic features more often predict perceived robot intelligence and interpretability. As such, these characteristics may in future be used as an online reward signal for in-situ Reinforcement Learningbased adaptive human-robot dialogue systems.  Left: a live view of experimental setup showing a participant interacting with Pepper.  a diagram of experimental setup showing the participant (green) and the robot (white) positioned face to face. The scene was recorded by cameras (triangles C) from the robot’s perspective focusing on the face of the participant and from the side, showing the whole scene. The experimenter (red) was seated behind a divider.
Utilizing marginal net utility for recommendation in e-commerce

Traditional recommendation algorithms often select products with the highest predicted ratings to recommend. However, earlier research in economics and marketing indicates that a consumer usually makes purchase decision(s) based on the product's marginal net utility (i.e., the marginal utility minus the product price). Utility is defined as the satisfaction or pleasure user u gets when purchasing the corresponding product. A rational consumer chooses the product to purchase in order to maximize the total net utility. In contrast to the predicted rating, the marginal utility of a product depends on the user's purchase history and changes over time. According to the Law of Diminishing Marginal Utility, many products have the decreasing marginal utility with the increase of purchase count, such as cell phones, computers, and so on. Users are not likely to purchase the same or similar product again in a short time if they already purchased it before. On the other hand, some products, such as pet food, baby diapers, would be purchased again and again.
 To better match users' purchase decisions in the real world, this paper explores how to recommend products with the highest marginal net utility in e-commerce sites. Inspired by the Cobb-Douglas utility function in consumer behavior theory, we propose a novel utility-based recommendation framework. The framework can be utilized to revamp a family of existing recommendation algorithms. To demonstrate the idea, we use Singular Value Decomposition (SVD) as an example and revamp it with the framework. We evaluate the proposed algorithm on an e-commerce (shop.com) data set. The new algorithm significantly improves the base algorithm, largely due to its ability to recommend both products that are new to the user and products that the user is likely to re-purchase.
Molecular and morphological phylogeny of hooded shrimps, genera Betaeus and Betaeopsis (Decapoda, Alpheidae): testing the center of origin biogeographic model and evolution of life history traits.

The phylogenetic relationships of the alpheid shrimp genera Betaeus (Dana, 1852) (15 species) and Betaeopsis (Yaldwyn, 1971) (three species), collectively known as hooded shrimps, are analyzed with morphological, molecular (16S and H3) and combined "total evidence" (morphology+DNA) datasets. The tree topology resulting from morphological and combined analyses places Betaeus jucundus as sister to all the remaining species of Betaeus and Betaeopsis, rendering Betaeus paraphyletic. On the other hand, Betaeopsis is recovered as monophyletic. Betaeus australis is positioned as sister to the remaining species of Betaeus s. str. (excluding B. jucundus), which is composed of three well-supported and resolved clades. Mapping of biogeographic traits on the combined tree suggests at least two possible historic scenarios. In the first scenario, the North-East Pacific harboring the highest diversity of hooded shrimps (seven species of Betaeus), acted as the "center of origin", where species appeared, matured and eventually migrated toward peripheral regions. In the second scenario, Betaeus+Betaeopsis originated in the southern Indo-West Pacific and subsequently colonized the North-East Pacific, where a major radiation involving dispersal/vicariance events took place. The mapping of life history traits (symbiosis vs. free living and gregariousness vs. single/pair living) in the combined tree suggests (1) that different types of symbioses with dissimilar host organisms (sea urchins, abalones, other decapods, spoon worms) evolved independently more than once in the group (in B. jucundus and in various lineages of Betaeus s. str.), and (2) that gregariousness was ancestral in the Betaeus s. str. -Betaeopsis clade and later shifted toward single/pair living in several lineages.
Runtime Code Reuse Attacks: A Dynamic Framework Bypassing Fine-Grained Address Space Layout Randomization

Fine-grained address space layout randomization has recently been proposed as a method of efficiently mitigating ROP attacks. In this paper, we introduce a design and implementation of a framework based on a runtime strategy that undermines the benefits of fine-grained ASLR. Specifically, we abuse a memory disclosure to map an application’s memory layout on-the-fly, dynamically discover gadgets and construct the desired exploit payload, and finish our goals by using virtual function call mechanism—all with a script environment at the time an exploit is launched. We demonstrate the effectiveness of our framework by using it in conjunction with a real-world exploit against Internet Explorer and other applications protected by fine-grained ASLR. Moreover, we provide evaluations that demonstrate the practicality of run-time code reuse attacks. Our work shows that such a framework is effective and fine-grained ASLR may not be as promising as first thought. Keywords-code reuse; security; dynamic; fine-grained ASLR
Intrauterine fetal decapitation after a high-speed car crash.

Motor vehicle collisions are an important cause of blunt abdominal trauma in pregnant woman. Among the possible outcomes of blunt abdominal trauma, placental abruption, direct fetal trauma, and rupture of the gravid uterus are described. An interesting case of complete fetal decapitation with uterine rupture due to a high-velocity motor vehicle collision is described. The external examination of the fetus showed a disconnection between the cervical vertebrae C3 and C4. The autopsy examination showed hematic infiltration of the epicranic soft tissues, an overlap of the parietal bones, and a subarachnoid hemorrhage in the posterior part of interparietal area. Histological analysis was carried out showing a lack of epithelium and hemorrhages in the subcutaneous tissue, a hematic infiltration between the muscular fibers of the neck and between the collagen and deep muscular fibers of the tracheal wall. Specimens collected from the placenta and from the uterus showed a hematic infiltration with hypotrophy of the placental villi, fibrosis of the mesenchymal villi with ischemic phenomena of the membrane. The convergence of circumstantial data, autopsy results, and histological data led us to conclude that the neck lesion was vital and the cause of death was attributed to the motor vehicle collision.
Increased performance of single-chamber microbial fuel cells using an improved cathode structure

Maximum power densities by air-driven microbial fuel cells (MFCs) are considerably influenced by cathode performance. We show here that application of successive polytetrafluoroethylene (PTFE) layers (DLs), on a carbon/PTFE base layer, to the air-side of the cathode in a single chamber MFC significantly improved coulombic efficiencies (CEs), maximum power densities, and reduced water loss (through the cathode). Electrochemical tests using carbon cloth electrodes coated with different numbers of DLs indicated an optimum increase in the cathode potential of 117 mV with four-DLs, compared to a <10 mV increase due to the carbon base layer alone. In MFC tests, four-DLs was also found to be the optimum number of coatings, resulting in a 171% increase in the CE (from 19.1% to 32%), a 42% increase in the maximum power density (from 538 to 766 mW m ), and measurable water loss was prevented. The increase in CE due is believed to result from the increased power output and the increased operation time (due to a reduction in aerobic degradation of substrate sustained by oxygen diffusion through the cathode).
T-drive: driving directions based on taxi trajectories

GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge. In this paper, we mine smart driving directions from the historical GPS trajectories of a large number of taxis, and provide a user with the practically fastest route to a given destination at a given departure time. In our approach, we propose a time-dependent landmark graph, where a node (landmark) is a road segment frequently traversed by taxis, to model the intelligence of taxi drivers and the properties of dynamic road networks. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest route. We build our system based on a real-world trajectory dataset generated by over 33,000 taxis in a period of 3 months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70% of the routes suggested by our method are faster than the competing methods, and 20% of the routes share the same results. On average, 50% of our routes are at least 20% faster than the competing approaches.
Survey on social tagging techniques

Social tagging on online portals has become a trend now. It has emerged as one of the best ways of associating metadata with web objects. With the increase in the kinds of web objects becoming available, collaborative tagging of such objects is also developing along new dimensions. This popularity has led to a vast literature on social tagging. In this survey paper, we would like to summarize different techniques employed to study various aspects of tagging. Broadly, we would discuss about properties of tag streams, tagging models, tag semantics, generating recommendations using tags, visualizations of tags, applications of tags and problems associated with tagging usage. We would discuss topics like why people tag, what influences the choice of tags, how to model the tagging process, kinds of tags, different power laws observed in tagging domain, how tags are created, how to choose the right tags for recommendation, etc. We conclude with thoughts on future work in the area.
A comparison‐shopper's guide to connectivity metrics

lations, is a critical ecological process (Ims and Yoccoz 1997). It can maintain genetic diversity, rescue declining populations, and re-establish extirpated populations. Sufficient movement of individuals between isolated, extinction-prone populations can allow an entire network of populations to persist via metapopulation dynamics (Hanski 1991). As areas of natural habitat are reduced in size and continuity by human activities, the degree to which the remaining fragments are functionally linked by dispersal becomes increasingly important. The strength of those linkages is determined largely by a property known as “connectivity”, which, despite its intuitive appeal, is inconsistently defined. At one extreme, metapopulation ecologists argue for a habitat patch-level definition, while at the other, landscape ecologists insist that connectivity is a landscape-scale property (Merriam 1984; Taylor et al. 1993; Tischendorf and Fahrig 2000;  Tischendorf 2001a; ). Differences in perspective notwithstanding, theoreticians do agree that connectivity has undeniable effects on many population processes (Wiens 1997; ). It is therefore desirable to quantify connectivity and use these measurements as a basis for decision making. Currently, many reserve design algorithms factor in some measure of connectivity when weighing alternative plans (Siitonen et al. 2002, 2003; Singleton et al. 2002; Cabeza 2003). Consideration of connectivity during the reserve design process could highlight situations where it really matters. For example, alternative reserve designs that are similar in other factors such as area, habitat quality, and cost may differ greatly in connectivity (Siitonen et al. 2002). This matters because the low-connectivity scenarios may not be able to support viable populations of certain species over long periods of time. Analyses of this sort could also redirect some project resources towards improving the connectivity of a reserve network by building movement corridors or acquiring small, otherwise undesirable habitat patches that act as links between larger patches (Keitt et al. 1997). Reserve designs could therefore include the demographic and genetic benefits of increased connectivity without substantially increasing the cost of the project (eg Siitonen et al. 2002). If connectivity is to serve as a guide, at least in part, for conservation decision-making, it clearly matters how it is measured. Unfortunately, the ecological literature is awash with different connectivity metrics. How are land managers and decision makers to efficiently choose between these alternatives, when ecologists cannot even agree on a basic definition of connectivity, let alone how it is best measured? Aside from the theoretical perspectives to which they are tied, these metrics differ in two important regards: the type of data they require and the level of detail they provide. Here, we attempt to cut through some of the confusion surrounding connectivity by developing a classification scheme based on these key differences between metrics.
Multi-omics approaches to disease

High-throughput technologies have revolutionized medical research. The advent of genotyping arrays enabled large-scale genome-wide association studies and methods for examining global transcript levels, which gave rise to the field of “integrative genetics”. Other omics technologies, such as proteomics and metabolomics, are now often incorporated into the everyday methodology of biological researchers. In this review, we provide an overview of such omics technologies and focus on methods for their integration across multiple omics layers. As compared to studies of a single omics type, multi-omics offers the opportunity to understand the flow of information that underlies disease.
Graph-Based Network Analysis of Resting-State Functional MRI

In the past decade, resting-state functional MRI (R-fMRI) measures of brain activity have attracted considerable attention. Based on changes in the blood oxygen level-dependent signal, R-fMRI offers a novel way to assess the brain's spontaneous or intrinsic (i.e., task-free) activity with both high spatial and temporal resolutions. The properties of both the intra- and inter-regional connectivity of resting-state brain activity have been well documented, promoting our understanding of the brain as a complex network. Specifically, the topological organization of brain networks has been recently studied with graph theory. In this review, we will summarize the recent advances in graph-based brain network analyses of R-fMRI signals, both in typical and atypical populations. Application of these approaches to R-fMRI data has demonstrated non-trivial topological properties of functional networks in the human brain. Among these is the knowledge that the brain's intrinsic activity is organized as a small-world, highly efficient network, with significant modularity and highly connected hub regions. These network properties have also been found to change throughout normal development, aging, and in various pathological conditions. The literature reviewed here suggests that graph-based network analyses are capable of uncovering system-level changes associated with different processes in the resting brain, which could provide novel insights into the understanding of the underlying physiological mechanisms of brain function. We also highlight several potential research topics in the future.
Sparsity measure of a network graph: Gini index

This article examines the application of a popular measure of sparsity, Gini Index, on network graphs. A wide variety of network graphs happen to be sparse. But the index with which sparsity is commonly measured in network graphs is edge density, reflecting the proportion of the sum of the degrees of all nodes in the graph compared to the total possible degrees in the corresponding fully connected graph. Thus edge density is a simple ratio and carries limitations, primarily in terms of the amount of information it takes into account in its definition. In this paper, we have provided a formulation for defining sparsity of a network graph by generalizing the concept of Gini Index and call it sparsity index. A majority of the six properties (viz., Robin Hood, Scaling, Rising Tide, Cloning, Bill Gates and Babies) with which sparsity measures are commonly compared are seen to be satisfied by the proposed index. A comparison between edge density and the sparsity index has been drawn with appropriate examples to highlight the efficacy of the proposed index. It has also been shown theoretically that the two measures follow similar trend for a changing graph, i.e., as the edge density of a graph increases its sparsity index decreases. Additionally, the paper draws a relationship, analytically, between the sparsity index and the exponent term of a power law distribution, a distribution which is known to approximate the degree distribution of a wide variety of network graphs. Finally, the article highlights how the proposed index together with Gini index can reveal important properties of a network graph.
A multi-level clustering approach for forecasting taxi travel demand

In this paper, we use time-series modeling to forecast taxi travel demand, in the context of a mobile application-based taxi hailing service. In particular, we model the passenger demand density at various locations in the city of Bengaluru, India. Using the data, we first shortlist time-series models that suit our application. We then analyse the performance of these models by using Mean Absolute Percentage Error (MAPE) as the performance metric. In order to improve the model performance, we employ a multi-level clustering technique where we aggregate demand over neighboring cells/geohashes. We observe that the improved model based on clustering leads to a forecast accuracy of 80% per km2. In addition, our technique obtains an accuracy of 89% per km2 for the most frequently occurring use case.
Segmentation and semantic labelling of RGBD data with convolutional neural networks and surface fitting

This paper proposes an approach for segmentation and semantic labeling of RGBD data based on the joint usage of geometrical clues and deep learning techniques. An initial oversegmentation is performed using spectral clustering and a set of NURBS surfaces is then fitted on the extracted segments. The input data are then fed to a Convolutional Neural Network (CNN) together with surface fitting parameters. The network is made of nine convolutional stages followed by a softmax classifier and produces a per-pixel descriptor vector for each sample. An iterative merging procedure is then used to recombine the segments into the regions corresponding to the various objects and surfaces. The couples of adjacent segments with higher similarity according to the CNN features are considered for merging and the NURBS surface fitting accuracy is used in order to understand if the selected couples correspond to a single surface. By combining the obtained segmentation with the descriptors from the CNN a set of labeled segments is obtained. The comparison with state-of-the-art methods shows how the proposed method provides an accurate and reliable scene segmentation and labeling.
Skill Progression Demonstrated by Users in the Scratch Animation Environment

The Scratch environment exemplifies a tool+community approach to teaching elementary programming skills, as it includes a website where users can publish, discuss, and organize animations that are programs. To explore this environment’s effectiveness for helping people to develop programming skills, we performed a quantitative analysis of 250 randomly-selected users’ data, including over 1000 of their animations. We measured skill based on four models that had proven useful in prior empirical studies. Overall, we found mixed results about the environment’s effectiveness. Among users who do not drop out, we found an increasing progression in social skills. However, we also observed an extremely high drop-out rate. Moreover, we observed a flat or decreasing level of demonstrated skill on virtually every measure. These results call into question whether simply combining an animation tool and an online community is sufficient for keeping people engaged long enough to learn elementary programming skills.
Mental health and gender dysphoria: A review of the literature.

Studies investigating the prevalence of psychiatric disorders among trans individuals have identified elevated rates of psychopathology. Research has also provided conflicting psychiatric outcomes following gender-confirming medical interventions. This review identifies 38 cross-sectional and longitudinal studies describing prevalence rates of psychiatric disorders and psychiatric outcomes, pre- and post-gender-confirming medical interventions, for people with gender dysphoria. It indicates that, although the levels of psychopathology and psychiatric disorders in trans people attending services at the time of assessment are higher than in the cis population, they do improve following gender-confirming medical intervention, in many cases reaching normative values. The main Axis I psychiatric disorders were found to be depression and anxiety disorder. Other major psychiatric disorders, such as schizophrenia and bipolar disorder, were rare and were no more prevalent than in the general population. There was conflicting evidence regarding gender differences: some studies found higher psychopathology in trans women, while others found no differences between gender groups. Although many studies were methodologically weak, and included people at different stages of transition within the same cohort of patients, overall this review indicates that trans people attending transgender health-care services appear to have a higher risk of psychiatric morbidity (that improves following treatment), and thus confirms the vulnerability of this population.