Miracle-dz/UniFAR-FTUs huggingface dataset:
Why do We Follow Robots? An Experimental Investigation of Conformity with Robot, Human, and Hybrid Majorities

Individuals tend to conform to a majority for reasons of peer pressure (normative conformity) and insecurity (informational conformity). It is important to investigate the reasons for social phenomena such as conformity in order to better understand processes in hybrid teams (i.e., teams which consist of humans and robots). Research has yielded conflicting results on conformity with robot and hybrid majorities, and the reasons for conformity remain unclear. We conducted a within-subject online experiment $(\mathrm{n}=103)$ to compare the reasons for conformity under three conditions: human, robot, and hybrid majorities. Results indicate that subjects conformed most often with hybrid majorities, while they conformed least often with robot majorities. Normative conformity influenced conformity with human majorities, but informational conformity did not. Informational conformity influenced conformity with robot majorities, but normative conformity did not. Both types of conformity affected conformity with hybrid majorities. Our results provide a possible explanation for the heterogeneous findings on conformity in HRI.
ANew VR Kitchen Environment for Recording Well Annotated Object Interaction Tasks

This paper presents the Virtual Annotated Cooking Environment (VACE), a new open-source virtual reality dataset (https://sites.google.com/view/vacedataset) and simulator (https://github.com/michaelkoller/vacesimulator) for object inter-action tasks in a rich kitchen environment. We use the Unity-based VR simulator to create thoroughly annotated video se-quences of a virtual human avatar performing food preparation activities. Based on the MPII Cooking 2 dataset, it enables the recreation of recipes for meals such as sandwiches, pizzas, fruit salads and smaller activity sequences such as cutting vegetables. For complex recipes, multiple samples are present, following different orderings of valid partially ordered plans. The dataset includes an RGB and depth camera view, bounding boxes, object masks segmentation, human joint poses and object poses, as well as ground truth interaction data in the form of temporally labeled semantic predicates (holding, on, in, colliding, moving, cutting). In our effort to make the simulator accessible as an open-source tool, researchers are able to expand the setting and annotation to create additional data samples.
Cohesiveness of Robots in Groups Affects the Perception of Social Rejection by Human Observers

As robots become increasingly part of human social systems, how humans are psychologically affected by machine behaviors in groups such as ostracism and prejudice, becomes criteria for design. Parameters of Robot group can have an effect on the dynamics of robot-robot-human interaction. The cohesiveness of robot groups, termed entitativity, affects humans' willingness to engage with the group and alters their perception of threat and cooperativity. To investigate how group composition affects how people perceive negative social intent from robots, we showed subjects videos of various ways humans are socially rejected by robots under high and low entitativity conditions. The results reveal that when robotic groups are less cohesive, the sense of rejection is greater, implying that humans experience increased anxiety over being rejected by more diverse sets of machines. Understanding the social consequences of robot group dynamics can assist us in avoiding unanticipated negative affects caused by machines.
Skill-Based Automated Playtesting

In order to create well-crafted learning progressions, designers guide players as they present game skills and give ample time for the player to master those skills. However, analyzing the quality of learning progressions is challenging, especially during the design phase, as content is ever-changing. This research presents the application of Stratabots—automated player simulations based on models of players with varying sets of skills—to the human computation game Foldit. Stratabot performance analysis coupled with player data reveals a relatively smooth learning progression within tutorial levels, yet still shows evidence for improvement. Leveraging existing general gameplaying algorithms such as Monte Carlo Evaluation can reduce the development time of this approach to automated playtesting without losing predicitive power of the player model.
Coupling Research on the Integration and Development of Cultural Industry and High-tech Industry

Promoting industrial transformation and upgrading through industrial integration development is an inevitable trend of industrial development. Based on the coupling mechanism and the coupling evaluation model, this paper empirically analyzes the comprehensive evaluation index, integration degree and coupling coordination degree of the two industries based on the related data of the cultural industry and the high-tech industry from 2008 to 2016.The empirical results show that there is a significant integration phenomenon between China's cultural industry and high-tech industry, and the degree of integration is in a highly coupled stage. At the same time, the integration and development between the two industries is fluctuating.
What Can We Do with a Robot for Family Playtime?

As the operation of robots is becoming easier, adopting a robot as a companion for families is increasing. When considering that family playtime has a positive influence on all family members, it is important to provide various activities for families to play together. Robots might mediate fun activities to increase family playtime. Therefore, this research was designed to compare play activities between dyadic child-robot interactions and family-robot interactions. We explored the possible activities for family playtime with a NAO robot. The results from three family groups showed that verbal activities, such as giving an instruction to a robot and talking to a robot, increased when family members are playing together with the robot. Also, increased physical activities were involving all family members, such as giving and receiving balls. Therefore, it will be necessary for a robot to recognize the voices of multiple family members and respond to each member appropriately. More importantly, a robot's time allocation for each family member and encouraging the participation of all members will be necessary to maintain or Increase the family playtime.
Learning Sparse Causal Models is not NP-hard

This paper shows that causal model discovery is not an NP-hard problem, in the sense that for sparse graphs bounded by node degree k the sound and complete causal model can be obtained in worst case order N2(k+2) independence tests, even when latent variables and selection bias may be present. We present a modification of the well-known FCI algorithm that implements the method for an independence oracle, and suggest improvements for sample/real-world data versions. It does not contradict any known hardness results, and does not solve an NP-hard problem: it just proves that sparse causal discovery is perhaps more complicated, but not as hard as learning minimal Bayesian networks.
Advances in understanding meningococcal biologyand epidemiology

Since the first outbreaks of meningococcal meningitis were first described in Geneva in 1804 and in New England in 1806, and since the discovery of the causative agent by Weichselbaum in 1887 and the beginning of epidemics of meningococcal meningitis in the sub-Saharan Africa 100 years ago, Neisseria meningitidis has been recognized as the cause worldwide of epidemic meningitis and meningococcemia. The massive epidemic outbreaks in sub-Saharan Africa in the 1990’s, the emergence since 1995 of serogroups Y, W-135 and X and the prolonged outbreak of serogroup B meningococcal disease in New Zealand over the last decade serve to remind us of the continued potential of the meningococcus to cause global morbidity and mortality. This report reviews new discoveries impacting prevention and future prospects for conquering the meningococcus as a human pathogen.
Chlamydia pneumoniae infection induces vascular smooth muscle cell migration via Rac 1 activation

Chlamydia pneumoniae infection has been shown to be associated with the development of atherosclerosis by promoting the migration of vascular smooth muscle cells (VSMCs). However, how C. pneumoniae infection induces VSMC migration is not fully understood. A primary role of Ras-related C3 botulinum toxin substrate 1 (Rac1) is to generate a protrusive force at the leading edge that contributes to cell migration. Whether Rac1 activation plays a role in C. pneumoniae infection-induced VSMC migration is not well defined. In the present study, we therefore examined Rac1 activation in C. pneumoniae-infected rat primary VSMCs and the role of Rac1 activation in C. pneumoniae infection-induced VSMC migration. Glutathione S-transferase pulldown assay results showed that Rac1 was activated in C. pneumoniae-infected rat primary VSMCs. A Rac1 inhibitor, NSC23766 (50 mM,) suppressed Rac1 activation stimulated by C. pneumoniae infection, and thereby inhibited C. pneumoniae infection-induced VSMC migration. In addition, C. pneumoniae infection-induced Rac1 activation in the VSMCs was blocked by LY294002 (25 mM), an inhibitor of phosphatidylinositol 3-kinase (PI3K). Taken together, these data suggest that C. pneumoniae infection promotes VSMC migration, possibly through activating Rac1 via PI3K.
Phenotypic Characterization of Jawa Brebes (Jabres) Cattle

The aim of this study was to identify the relationship among body measurements in Jabres cattle. A total of 193 cows were used. The parameters recorded were: body length (BL), withers height (WH), rump height (RH), rump length (RL), rump width (RW), heart girth (HG), shoulder width (SW), chest depth (CD), face length (FL), and face width (FW). The results showed that the mean body weight was 260,868 kg while the body measurements were 109.732; 115.572; 119.158; 31.674; 11.589; 147.169; 31.341; 55.892; 40.915; and 17.242 cm for BL, WH, RH, RL, RW, HG, SW, CD, FL, and FW respectively. The coefficients of correlation obtained were: r = 0.269 (BW - BL), r = 0.451 (BW - WH), r = 0.464 (BW - RH), r = 0.123 (BW - RL), r = 0.309 (BW - RW), r = 0.502 (BW - HG), r = 0.519 (BW - SW), r = 0.443 (BW - CD), r = 0.269 (BW - FL), and r = 0.396 (BW - FW) respectively. The components were extracted explaining 51.544% of the total variation. The body measurements could be used as selection criteria for improving body weight of Jabres cattle.
Marijuana Toxicity: Heavy Metal Exposure Through State-Sponsored Access to “la Fee Verte”

Federally unregulated, marijuana growth organizations (MGOs) have now provided a path to exposures to the neurotoxicity of heavy metals. The lack of US Food and Drug Administration (FDA) and US Environmental Protection Agency (EPA) testing and oversight of the MGOs now threatens the public health. Agribusiness and botany experts proclaim the value of cannabis as a perfect rotating plant for phytoremediation programs to help scavenge heavy metals from soils prior to seeding the land for food product. Cannabis has a high affinity for soil contaminants without affecting its own heartiness. However, “legal” marijuana plots have burgeoned in the “Emerald Triangle” of Northern California, Oregon and Washington. According to the FDA’s toxicology program, the largest sources of heavy metals (HMs) are the environments surrounding abandoned or active mines. The history of gold, platinum, coal, and copper mining in these grow areas now threatens the end-user; the plants ability to “scrub the earth” of these highly toxic HMs provides main stream smoke contamination to the consumer. Published reports of cannabis users showing hearing loss and neurological changes to temporal lobe structures involved in audition as well as learning and memory. The apoptotic cascade of cytotoxic events initiated by heavy metals is linked to the progression of Alzheimer’s and Parkinson’s disease, as well as hearing loss related to brain stem and temporal lobe neurotoxicity.
C V ] 1 8 A ug 2 01 5 Image tag completion by local learning

The problem of tag completion is to learn the missing tags of an image. In this paper, we propose to learn a tag scoring vector for each image by local linear learning. A local linear function is used in the neighborhood of each image to predict the tag scoring vectors of its neighboring images. We construct a unified objective function for the learning of both tag scoring vectors and local linear function parameters. In the objective, we impose the learned tag scoring vectors to be consistent with the known associations to the tags of each image, and also minimize the prediction error of each local linear function, while reducing the complexity of each local function. The objective function is optimized by an alternate optimization strategy and gradient descent methods in an iterative algorithm. We compare the proposed algorithm against different state-of-the-art tag completion methods, and the results show its advantages.
Structural Analysis of Minimum Weight Codewords of the (32, 21, 6) and (64, 45, 8) Extended BCH Codes Using Invariance Property

Two typical examples, the (32, 21, 6) and (64, 45, 8) extended code of primitive permuted BCH codes, are considered. The sets of minimum weight codewords are analyzed in terms of Boolean polynomial representation. They are classied by using their split weight structure with respect to the left and right half trellis sections, and for each class, the standard form is presented. Based on the results, we can generate a proper list of the minimum weight codewords of the codes.
Environmental Performance Information Disclosure of A-share Listed Companies

The paper sample period covered the years 2007-2011 A-share listed companies of Chinese chemical industry. We used an environmental performance disclosure index and evaluated the environmental performance disclosure information (EPDI) according to the Sustainability Reporting Guidelines Version 3. 0 (G3) developed by the Global Reporting Initiative (GRI) in 2006. Then we developed a multivariate linear regression to test determinants of EPDI level. The results showed that EPDI level was critically low; Environmental award and listed place of companies were significantly positive correlated to EPDI. The EPDI level of listed companies in Shanghai Stock Exchange was better than that of Shenzhen Stock Exchange. Company size and registration place of companies were positive correlation to EPDI, but failed to pass the significant test. However, the development capability, EPS, and liability degree were negative correlation to EPDI level, and also failed to pass the significant test.
Multi-hump solitary waves of nonlinear Dirac equation

This paper concentrates on a (1+1)-dimensional nonlinear Dirac (NLD) equation with a general self-interaction, being a linear combination of the scalar, pseudoscalar, vector and axial vector self-interactions to the power of the integer $k+1$. The solitary wave solutions to the NLD equation are analytically derived, and the upper bounds of the hump number in the charge, energy and momentum densities for the solitary waves are proved in theory. The results show that: (1) for a given integer $k$, the hump number in the charge density is not bigger than $4$, while that in the energy density is not bigger than $3$; (2) those upper bounds can only be achieved in the situation of higher nonlinearity, namely, $k\in\{5,6,7,\cdots \}$ for the charge density and $k\in\{3,5,7,\cdots\}$ for the energy density; (3) the momentum density has the same multi-hump structure as the energy density; (4) more than two humps (resp. one hump) in the charge (resp. energy) density can only happen under the linear combination of the pseudoscalar self-interaction and at least one of the scalar and vector (or axial vector) self-interactions. Our results on the multi-hump structure will be interesting in the interaction dynamics for the NLD solitary waves.
The reliability and validity of the translated Oregon diabetes knowledge scale among Bengali women residing in Kolkata

Bengali language diabetes knowledge scale is few but need of the hour. Better knowledge can contribute to adequate prevention of the condition. Aim of the study was to assess the reliability and validity of the translated Oregon diabetes knowledge scale among Bengali women residing in Kolkata. An observational study of crosssectional design was conducted among 231 adult Bengali women living in Kolkata . They were selected from the current updated voter list 2016 by simple random sampling. Reliability was tested using Kappa statistic (n=68) and Cronbach‘s coefficient alpha (n= 72). Face validity was assessed by a number of experts as well as group of study participants (n=20). Content validity was computed by comparing scores with respect to different parameters (n=163). P value < 0.05 considered to be statistically significant. Cronbach’s alpha for overall score was 0.62868. Regarding interrater agreement, average kappa coefficient r=0.6399. Test-retest agreement, average r =0.5992. The score was influenced by greater number of education years (p=0.006); also by the presence of diabetes and other co-morbidities like hypertension (p=0.048). Translated Bengali language Oregon knowledge scale was found to have adequate face and content validity. In terms of reliability, the Cronbach’s alpha score and the kappa statistic for the interrater agreement was moderate. Thus use of the scale can improve the efforts regarding diabetes education among women as well as benefit overall health of the society.
UNPLUGGED CITY Escaping from Global Networks and Flows Trap? New Geographies of Logistics:: Urban and Regional Implications

The urban and regional influence of contemporary passengers, goods and information traffic within the new global economy based on knowledge and logistics, and the physical structures that shape the networks (harbours, airports, train stations, motorways) are assumed as capitally important. Nevertheless, so far, the impact of these dynamics is much more attached to hierarchical processes of socio-spatial fragmentation-segmentation-polarization rather than balanced re-distributive systems. Global connectivity has as much strengths as threats, and the integration within the new production, consumption and distribution geographies generates as much opportunities as irreversible mutations. Unplugged City is presented as an incursion on connectivity and accessibility, exploring the strengths and threats derived from the management of logistics and freight transport flows within the frame of the so-called "catch-up" economy. The Trans-European Transport Network Plan (TEN-T) will be on focus as the main policy-making framework behind the new geographical transformation in Europe. The importance of the exponential growing freight transport and logistic sector has been exposed as a key factor to understand the bases of current globalisation. The sharp changes generated by the dropping of transport cost in patterns of production-consumption-distribution geographies have to be contrasted with deep transformations of labour market, linkages between transport and industrial location and new network models. What is close is just what is cheap. Distances nowadays are no measured in kilometres or hours but in Euros, imposing a new logic for the global geographic structure. Regions with better access to locations of input materials and markets are assumed to be more productive, more competitive and hence more successful than more peripheral and isolated regions. But this hypothesis has to be urgently reformulated, contrasting transport policies with quantitative research and the appraisal of real socio-economic impact of the new transport geographies. The assumed idea of connectivity as pure benefiting will be critically discussed as a much complex phenomena. Corridors are fostering the survival of core regional centres meanwhile a growing regional imbalance is monitored; regions that still have to deal with environmental, budgetary and indirect issues derived from the management of these flows.
VOWEL SPACE AREAS ACROSS DIALECTS AND GENDER

This study compares vowel spaces in three regional varieties of American English spoken in central Ohio, south-central Wisconsin, and western North Carolina to determine whether the significant variation in the vowel systems of these dialects also affects the dialect-specific vowel space area. The gender-related differences are assessed by comparing the unnormalized (in Hz) and normalized formant frequency values. Significant effects of speaker dialect were found for the vowel space area defined by four “corner” vowels. However, there were no differences between dialects in the area of an extended 5vowel space. The results indicate that, despite large cross-dialectal differences in the positions of the vowels in the acoustic space, the extended vowel space area encompassing a complete vowel system is unaffected by dialectal variation. The differences in the size of the vowel space due to speaker gender were eliminated by normalizing formant frequency values.
IVSS-2006-UGV-02 Image Understanding Tools for Robot Navigation

This paper presents a software system for online imagebased terrain classification that mimics a human supervisor’s segmentation and classification of training images into “Go” and “NoGo” regions. The system identifies a set of image chips (or exemplars) in the training images that span the range of terrain appearance. It then uses the exemplars to segment novel images and assign a Go/NoGo classification. System performance is compared to that obtained via offline fuzzy c-means clustering.
Robot-Assisted Language Learning Increases Functional Connectivity in Children's Brain

The current study investigated how robot tutors influence brain activity during child-robot interaction (CRI) for learning of second language vocabulary. We gathered EEG signals from two groups of children; 1) Robot group (N=21) who listened to a storytelling social robot and learned French words, and 2) Display group (N=20) who listened to the same story in the French language mediated by only a computer screen. To measure learning-induced changes in the brain, functional connectivity analysis was conducted on EEG signals, which quantifies the communication between brain regions during the learning phase. Results showed a significantly higher functional brain connectivity for the Robot group in the theta frequency band, which has been previously associated with language functions in neuroscientific literature. Our results provide neurophysiological evidence for the benefit of robot tutors in second language learning in children.
The Effect of Repetitive Drop Jumps among Different Heights on Bilateral Asymmetry of Countermovement Jumps

Background: The study explored the influence of repeated drop jumps (DJs) from different drop heights on the lower extremity bilateral asymmetry and muscle activation of countermovement jumps (CMJs). Methods: Eighteen male athletes performed 200 drop jumps (DJs200) from three drop jump height (DJH30, 40 and 50 cm). The CMJs were performed before the first DJ and after the 50th, 100th, 150th and 200th DJs, recording them as pre-CMJ, CMJs50, CMJs100, CMJs150 and CMJs200. One-way repeated measures ANOVA was used to compare differences among the three drop heights at pre-CMJ, CMJs50, CMJs100, CMJs150 and CMJs200, respectively. Results: The peak ground reaction forces (PGRF) of CMJs100, CMJs150 and CMJs200 at DJH50 were greater than at DJH30 and DJH40 (all p < 0.05). The muscle activation during CMJs50 at DJH50 was greater than at DJH30 and DJH40 (all p < 0.05). The muscle activation during CMJs100, CMJs150 and CMJs200 at DJH50 was smaller than at DJH40 and DJH30 (all p < 0.05). The PGRF had no significant difference among the three different drop heights during CMJs50 (p > 0.05). Conclusions: The DJs50 at DJH50 had no effect on the bilateral asymmetry and increased muscle activation of CMJs. The excessive DJs100 at DJH50 increased bilateral asymmetry and decreased CMJ muscle activation during CMJs.