Miracle-dz/UniFAR-FTUs huggingface dataset:
What makes people accept or reject companion robots?: A research agenda

Social companion robots are intentionally developed and designed to support humans in useful tasks and to use social cues to establish a relationship to the user. However, so far no social companion robots existed outside of research labs to perform long-term studies "in the wild", exploring how this relationship actually evolves over time. In this paper we present the research agenda for such a study using the soon commercially available BUDDY robot from Blue Frog Robotics. We chose a sociological ethnographic approach with eight households, methodologically mainly focusing on qualitative data gathered through a series of household visits. For data analysis we aim at extending the Domestic Robot Ecology (DRE), which was originally developed based on ethnographic studies with vacuum cleaning robots, and for data interpretation we base our work on a newly proposed sociological framework, we call everyday-life centered approach (ELCA).
Investigation of the Influence of Pre-Charged Hydrogen on Fracture Toughness of As-Received 2.25Cr1Mo0.25V Steel and Weld

Fracture failure caused by hydrogen embrittlement (HE) is a major concern for the system reliability and safety of hydrogen storage vessels, which are generally made of 2.25Cr1Mo0. 25V steel. Thus, study of the influence of pre-charged hydrogen on fracture toughness of as-received 2.25Cr1Mo0. 25V steel and weld is of significant importance. In the current work, the influence of hydrogen on fracture toughness of as-received 2.25Cr1Mo0. 25V steel and weld was systematically studied. Base metal (BM) and weld metal (WM) specimens under both hydrogen-free and hydrogen-charged conditions were tested using three-point bending tests. Hydrogen was pre-charged inside specimens by the immersion charging method. The J-integral values were calculated for quantitatively evaluating the fracture toughness. In order to investigate the HE mechanisms, optical microscopy (OM) and scanning electron microscopy (SEM) were used to characterize the microstructure of BM and WM specimens. The results revealed that the presence of pre-charged hydrogen caused a significant decrease of the fracture toughness for both BM and WM specimens. Moreover, the pre-charged hydrogen led to a remarkable transition of fracture mode from ductile to brittle pattern in 2.25Cr1Mo0. 25V steel.
Attribute weighted fuzzy clustering algorithm based on mutual information

It is studied by applying the mutual information which is used to assess the contribution of each attribute that has the different important degrees to the classification in the fuzzy clustering algorithm, then the attribute weighted fuzzy clustering algorithm based on mutual information is proposed. By using the mutual information to quantify the contribution of each attribute to the classification, the attributes are weighted and introduced into the fuzzy C mean algorithm. For incomplete data sets, the missing attribute is also introduced as a target object to be optimized and as a part of the iterative to be optimization. Finally, an example verifies the applicability of the algorithm in dealing with incomplete data sets and incomplete data sets, and analyzes the effect of each attribute value loss on clustering results in incomplete data sets.
Biomarkers of Cutaneous Leishmaniasis

Cutaneous leishmaniasis (CL) is an immune-mediated skin pathology caused mainly by Leishmania (L.) major, Leishmania tropica, Leishmania braziliensis, L. mexicana, and L. amazonensis. The burden of CL in terms of morbidity and social stigmas are concentrated on certain developing countries in Asia, Africa, and South America. People with asymptomatic CL represent a large proportion of the infected individuals in the endemic areas who exhibit no lesion and can control the infection by as yet not fully understood mechanisms. Currently, there is no approved prophylactic control measure for CL. Discovery of biomarkers of CL infection and immunity can inform the development of more precise diagnostics tools as well as curative or preventive strategies to control CL. Herein, we provide a brief overview of the state-of-the-art for the biomarkers of CL with a special emphasis on the asymptomatic CL biomarkers. Among the identified CL biomarkers so far, direct biomarkers which indicate the actual presence of the infection as well as indirect biomarkers which reflect the host's reaction to the infection, such as alterations in delayed type hypersensitivity, T-cell subpopulations and cytokines, adenosine deaminase, and antibodies against the sand fly saliva proteins are discussed in detail. The future avenues such as the use of systems analysis to identify and characterize novel CL biomarkers are also discussed.
Relationships between the inflammatory potential of the diet, aging and anthropometric measurements in a cross-sectional study in Pakistan

BACKGROUND: Little is known about associations between dietary inflammation, age and anthropometric measurements. OBJECTIVE: In this regard, we examine how DII is related to age, anthropometrics [weight, Body mass index (BMI), waist to hip ratio (WHR)] and other parameters of nutrition (energy, protein, fats and cholesterol intake, Net Endogenous Acid Production (NEAP) and Phytochemical Index (PI)) in a cross-sectional study in Pakistan. DESIGN: Only men (n = 651, age 54–95 years) participated in the study. Anthropometric data were collected using standard methods. DII was calculated from nutrients derived from 24-hr Dietary Recall questionnaires. NEAP and PI were calculated by established algorithms using information on nutrient intake. RESULTS: The results show that with increasing age, there was a significant increase in the DII score (p < 0.05). Similarly significant positive correlations were found between DII score and weight, BMI, WHR and % BF (p, for all trends < 0.05). DII score significantly positively correlated with the dietary factors studied i.e. energy, protein, and fats (p, for all trends < 0.0001) but non-significantly with cholesterol (p > 0.05). Similarly, a significant positive correlation with NEAP (p < 0.0001) was found, but negative with PI (p < 0.0001). CONCLUSION: In conclusion, the present study shows direct positive correlations between the DII, age and indices of obesity, and thus supports the hypothesis that diet may have a role in the development of obesity through inflammatory modulation mechanisms in elderly.
Impact of the gate structure on ESD characteristic of tunnel field-effect transistors

Tunnel field-effect transistor (TFET) is a promising candidate in replacing a MOSFET, particularly for the next generation low-power integrated circuits (ICs). The gate grounded TFET (ggTFET) is considered as a basic electrostatic discharge (ESD) protection device in the TFET ICs. In this work, the impact of the gate structure on the ESD characteristic of the TFETs has been investigated using TCAD simulation. It is found that the technology parameters such as gate length, gate oxide thickness, and gate structure geometry affect both the triggering voltage and the failure current of the TFET.
Big Data Integration for Product Specifications

The product domain contains valuable data for many important applications. Given the large and increasing number of sources that provide data about product speciﬁcations and the velocity as well as the variety with which such data are available, this domain represents a challenging scenario for developing and evaluating big data integration solutions. In this paper, we present the results of our efforts towards big data integration for product speciﬁcations. We present a pipeline that decomposes the problem into different tasks from source and data discovery, to extraction, data linkage, schema alignment and data fusion. Although we present the pipeline as a sequence of tasks, different conﬁgurations can be deﬁned depending on the application goals.
Differential aging‐related changes in neurophysiology and gene expression in IB4‐positive and IB4‐negative nociceptive neurons

Despite pain prevalence altering with age, the effects of aging on the properties of nociceptors are not well understood. Nociceptors, whose somas are located in dorsal root ganglia, are frequently divided into two groups based on their ability to bind isolectin B4 (IB4). Here, using cultured neurons from 1‐, 3‐, 5‐, 8‐, 12‐, and 18‐month‐old mice, we investigate age‐dependent changes in IB4‐positive and IB4‐negative neurons. Current‐clamp experiments at physiological temperature revealed nonlinear changes in firing frequency of IB4‐positive, but not IB4‐negative neurons, with a peak at 8 months. This was likely due to the presence of proexcitatory conductances activated at depolarized membrane potentials and significantly higher input resistances found in IB4‐positive neurons from 8‐month‐old mice. Repetitive firing in nociceptors is driven primarily by the TTX‐resistant sodium current, and indeed, IB4‐positive neurons from 8‐month‐old mice were found to receive larger contributions from the TTX‐resistant window current around the resting membrane potential. To further address the mechanisms behind these differences, we performed RNA‐seq experiments on IB4‐positive and IB4‐negative neurons from 1‐, 8‐, and 18‐month‐old mice. We found a larger number of genes significantly affected by age within the IB4‐positive than IB4‐negative neurons from 8‐month‐old mice, including known determinants of nociceptor excitability. The above pronounced age‐dependent changes at the cellular and molecular levels in IB4‐positive neurons point to potential mechanisms behind the reported increase in pain sensitivity in middle‐aged rodents and humans, and highlight the possibility of targeting a particular group of neurons in the development of age‐tailored pain treatments.
Computational methods for birth‐death processes

Many important stochastic counting models can be written as general birth‐death processes (BDPs). BDPs are continuous‐time Markov chains on the non‐negative integers in which only jumps to adjacent states are allowed. BDPs can be used to easily parameterize a rich variety of probability distributions on the non‐negative integers, and straightforward conditions guarantee that these distributions are proper. BDPs also provide a mechanistic interpretation—birth and death of actual particles or organisms—that has proven useful in evolution, ecology, physics, and chemistry. Although the theoretical properties of general BDPs are well understood, traditionally statistical work on BDPs has been limited to the simple linear (Kendall) process. Aside from a few simple cases, it remains impossible to find analytic expressions for the likelihood of a discretely‐observed BDP, and computational difficulties have hindered development of tools for statistical inference. But the gap between BDP theory and practical methods for estimation has narrowed in recent years. There are now robust methods for evaluating likelihoods for realizations of BDPs: finite‐time transition, first passage, equilibrium probabilities, and distributions of summary statistics that arise commonly in applications. Recent work has also exploited the connection between continuously‐ and discretely‐observed BDPs to derive EM algorithms for maximum likelihood estimation. Likelihood‐based inference for previously intractable BDPs is much easier than previously thought and regression approaches analogous to Poisson regression are straightforward to derive. In this review, we outline the basic mathematical theory for BDPs and demonstrate new tools for statistical inference using data from BDPs. WIREs Comput Stat 2018, 10:e1423. doi: 10.1002/wics.1423
Facial feature extraction method based on shallow and deep fusion CNN

To reduce the loss of feature information, this paper proposes a facial feature extraction method based on Shallow and Deep Fusion Convolutional Neural Networks (SDFCNN). Firstly, shallow features are extracted by employing parallel multiple convolutional layers of different scales. Then, the obtained features are fused with those extracted by a deeper layer. Finally, the results are further fused with features extracted by another deeper layer for Softmax classification. Experimental results show that, facial features extracted by SDFCNN provide better representation than that of DeepID network. The accuracy of SDFCNN can reach 95.72% on LFW.
The car door safety system with wireless alert transmission

This paper presents a car door safety system which has two major features: 1) sense objects when car door is opened with audio and visible light warning, 2) transmit alert by low power wireless communication which a technology based on IEEE 802.11ah named Sigfox has been chosen when the crash detected. The car door safety system which this paper presented is based on popular microcontrollers and sensors, it is easy to build, integrate and low cost.
Optogenetic dissection of descending behavioral control in Drosophila

In most animals, the brain makes behavioral decisions that are transmitted by descending neurons to the nerve cord circuitry that produces behaviors. In insects, only a few descending neurons have been associated with specific behaviors. To explore how these neurons control an insect’s movements, we developed a novel method to systematically assay the behavioral effects of activating individual neurons on freely behaving terrestrial D. melanogaster. We calculated a two-dimensional representation of the entire behavior space explored by these flies and associated descending neurons with specific behaviors by identifying regions of this space that were visited with increased frequency during optogenetic activation. Applying this approach across a population of descending neurons, we found, that (1) activation of most of the descending neurons drove stereotyped behaviors, (2) in many cases multiple descending neurons activated similar behaviors, and (3) optogenetically-activated behaviors were often dependent on the behavioral state prior to activation.
Sense of meaning influences mental functioning in chronic renal patients

In this preliminary study, we examined whether aspects of spiritual well-being accounted for mental and physical health-related quality of life in 68 patients with end-stage renal disease, when controlling for age, type of treatment, physical symptoms, and worries. Hierarchical multiple regressions showed that meaning was associated with better mental health, while worry and physical symptoms also accounted for poor mental health. Faith and peace did not contribute to mental health. Older age, type of treatment (hemodialysis), and physical symptoms accounted for poor physical health. Our findings suggest that clinicians should include spiritual well-being in future interventions for end-stage renal disease patients.
Sulfur nutrition stimulates lead accumulation and alleviates its toxicity in Populus deltoides

Sulfur (S) can modulate plant responses to toxic heavy metals, but the underlying physiological and transcriptional regulation mechanisms remain largely unknown. To investigate the effects of S supply on lead (Pb)-induced toxicity in poplars, Populus deltoides monilifera (Aiton) Eckenw. saplings were exposed to 0 or 50 μM Pb together with one of the three S concentrations (0 (low S), 100 (moderate S) or 1500 (high S) μM Na2SO4). Populus deltoides roots absorbed Pb and it was partially translocated to the aerial organs, thereby decreasing the CO2 assimilation rate and leaf growth. Lead accumulation in poplars caused the overproduction of O2- and H2O2 to induce higher levels of total thiols (T-SH) and glutathione (GSH). Lead uptake by the roots and its accumulation in the aerial organs were repressed by low S application, but stimulated by high S supply. Lead-induced O2- and H2O2 production were exacerbated by S limitation, but alleviated by high S supply. Moreover, the concentrations of S-containing antioxidants including T-SH and GSH were reduced in S-deficient poplars, but increased in high S-treated plants, which corresponded well to the changes in the activities of enzymes involved in S assimilation and GSH biosynthesis. The transcript levels of both genes encoding sulfate transporters, i.e., SULTR1.1 and SULTR2.2, were elevated by low S application or high S supply in the roots, and the transcriptional upregulation of both genes was more pronounced under Pb exposure. Furthermore, the mRNA levels of several genes involved in S assimilation and the biosynthesis of GSH and phytochelatins, i.e., ATPS1, ATPS3, GSHS1, GSHS2 and PCS1, were upregulated in poplar roots with high S supply, particularly under Pb exposure. These results indicate that a high S supply can stimulate Pb accumulation and reduce its toxicity in poplars by improving S assimilation and stimulating the biosynthesis of S-containing compounds including T-SH and GSH.
Moving object detection from a moving stereo camera via depth information and visual odometry

It is important for driving safety to know whether surrounding objects are moving or static. Most existing methods use pre-trained object detectors to detect vehicles and humans before determine whether they are moving or not. However, there are not only these two type objects on the real road. This study presents a system using depth information and visual odometry to detect moving object. It also adopts an adaptive thresholding method to enhance performance on detection. The proposed system can accurately detect moving objects according to the experimental results.
Leveraging Power-Performance Relationship of Energy-Efficient Modern DRAM Devices

Computer servers are equipped with an increasing number of memory modules each with more capacity, making main-memory systems now the second most energy-consuming component trailing only processors in big-memory servers. These big-memory servers and their main-memory systems should offer high energy efficiency. In pursuit of energy-efficient main-memory systems, prior work exploited mobile low power double data rate (LPDDR) devices’ advantages (lower power than DDR devices) while attempting to surmount their limitations (longer latency, lower bandwidth, or both). However, we show that such main-memory architectures (based on the latest LPDDR4 devices) are no longer effective and even hurt overall energy efficiency of servers by 49% on memory-intensive workloads compared with ones based on DDR4 devices. The reason is that the power consumption of modern DDR4 devices has substantially reduced by adopting the strength of mobile and graphics memory whereas LPDDR4 has focused more on increasing data transfer rates while sacrificing energy efficiency; the power consumption of DDR4 devices can significantly vary across manufacturers in this analysis. Moreover, exploring new energy-saving features of DDR4 devices in depth, we show that activating these features often hurts overall energy efficiency of servers because of their performance penalties. Subsequently, we propose a simple but effective scheme that adaptively exploits DRAM power-down modes and hence improves the system energy-delay product by 4.0%.
Transactive memory systems and Web 2.0 in knowledge sharing: A conceptual model based on activity theory and critical realism

The aim of this paper is to advance understanding of interactive knowledge sharing (KS) processes through exploring the role of transactive memory systems (TMSs) and Web 2.0. In the context of the information systems literature, there is little focus on their particular role in KS. To address this gap, this paper develops a conceptual model based on activity theory and critical realism outlining the role of TMS and Web 2.0 as mediating tools. This paper further reveals that their use as tools depends on deeper underlying structures/factors embedded within the community, namely, informal networks and trust among people. The new conceptual model and theoretical propositions are then illustrated by a qualitative study undertaken in Bulgarian organisations. This illustrative case provides support for the model, where TMS and Web 2.0 are found to facilitate KS. It further demonstrates that informal networks and trust among people support the use and the positive effects of these tools. The contribution of this paper is in the new analytical approach and conceptual model developed, which advances our understanding of interactive KS by explaining the linkages between the various factors involved.
Semantic image query based on bimodal hash

With the popularity of social network, there has been an explosive growth of web images data, which requires a highperformance image query engine. Many researchers have paid significant efforts in this area and the key points are: semantic-based query and hash-based query. Semantic-based query techniques can make the search engine more intelligent and accurate. While, hash-based techniques can deal with large-scaled image data. In order to tackcle the huge amount image data, a novel bimodal hash approach for semantic images query is proposed in this paper. According to the distribution characteristics of web images, our hash function is synthesized based on a semi-supervised learning methods and considers the image modality as well as text modality. Since our hashing method can preserve semantic similarity among images, our image query technique is likely to be efficient and accurate. The results of extensive experiments on bimodal datasets prove the validity of our approach.
Handling Massive N-Gram Datasets Efficiently

Two fundamental problems concern the handling of large n-gram language models: indexing, that is, compressing the n-grams and associated satellite values without compromising their retrieval speed, and estimation, that is, computing the probability distribution of the n-grams extracted from a large textual source. Performing these two tasks efficiently is vital for several applications in the fields of Information Retrieval, Natural Language Processing, and Machine Learning, such as auto-completion in search engines and machine translation. Regarding the problem of indexing, we describe compressed, exact, and lossless data structures that simultaneously achieve high space reductions and no time degradation with respect to the state-of-the-art solutions and related software packages. In particular, we present a compressed trie data structure in which each word of an n-gram following a context of fixed length k, that is, its preceding k words, is encoded as an integer whose value is proportional to the number of words that follow such context. Since the number of words following a given context is typically very small in natural languages, we lower the space of representation to compression levels that were never achieved before, allowing the indexing of billions of strings. Despite the significant savings in space, our technique introduces a negligible penalty at query time. Specifically, the most space-efficient competitors in the literature, which are both quantized and lossy, do not take less than our trie data structure and are up to 5 times slower. Conversely, our trie is as fast as the fastest competitor but also retains an advantage of up to 65% in absolute space. Regarding the problem of estimation, we present a novel algorithm for estimating modified Kneser-Ney language models that have emerged as the de-facto choice for language modeling in both academia and industry thanks to their relatively low perplexity performance. Estimating such models from large textual sources poses the challenge of devising algorithms that make a parsimonious use of the disk. The state-of-the-art algorithm uses three sorting steps in external memory: we show an improved construction that requires only one sorting step by exploiting the properties of the extracted n-gram strings. With an extensive experimental analysis performed on billions of n-grams, we show an average improvement of 4.5 times on the total runtime of the previous approach.