nils-herrmann/clean_scirepeval huggingface dataset:
Augmented Reality Enabled IoT Services for Environmental Monitoring Utilising Serious Gaming Concept

The paper describes an approach to integration of physical and digital worlds through aggregation of Internet of Things (IoT) service with the Augmented Reality (AR) platform, AR Genie. The solution is generally applicable, but concretely in this paper the IoT service is provided by ekoNET platform which provides the environmental data through AR based applications. The ekoNET solution is developed for a real-time monitoring of air quality and other atmospheric condition parameters such as temperature, air pressure and humidity. AR Genie is Software as a Service (SaaS) and Platform as a Service (PaaS) solution offering features and functionalities for creation and deployment of AR applications using simple and intuitive drag and drop interface requiring no programming knowledge. By extending the AR Genie platform with the ekoNET IoT service we were able to demonstrate usage of a real-time environmental data within AR mobile applications as well as to enable a new, more engaging way of IoT data visualization utilizing serious gaming and AR technologies.
SIFT based monocular SLAM with multi-clouds features for indoor navigation

This work introduces a monocular SLAM method, which uses the Scale Invariant Features Transform (SIFT) representation for the scene. The scene represented as clouds of SIFT features within the map. This hierarchical representation of space, serving to estimate the current direction in the environment within the current session. The system exploits the tracking of the same features of successive frames to calculate scalar weights for these features, to build a map of the environment indicating the camera movement, helping the blind persons to navigate more confidently through auditory pathway of their surroundings. EKF is used to estimate the features tracked within the successive frames. The system is tested for using the proposed method with a hand-held camera walking in indoor environment. The results show a good estimation on the spatial locations of the camera within a few milliseconds. The paper shows an electronic cane for navigating in indoor environment using these clouds of features for long-term appearance-based localization of a cane with web camera vision as the external sensor.
Multisensory approach to handwriting remediation: perceptions of school-based occupational therapists.

The purpose of this study was to describe the current practices of school-based occupational therapists regarding their use of multisensory modalities and activities in handwriting remediation.



A survey was sent to 313 school-based occupational therapist members of the American Occupational Therapy Association who identified themselves as working in a school system as their primary employment setting. Of these, 198 surveys were returned and analyzed descriptively, resulting in a response rate of 63.3%.



More than 130 different multisensory modalities and activities were documented. Twenty-five of these had previously been reported in the literature, the other 114 were documented, by respondents, within the "other" category. Most respondents reported using 5 or more modalities and activities per student, the most frequent being chalk and chalkboard. No consensus among respondents is apparent about the primary sensory systems stimulated by the modalities and activities. No difference in modality and activity use was found on the basis of demographic variables.



Overall findings indicate that the breadth of modalities and activities being used is far greater than that currently found in the literature. The results suggest a need for completing studies designed to examine the use and effectiveness of multisensory modalities and activities in handwriting remediation.
Artemisinin protects PC12 cells against β-amyloid-induced apoptosis through activation of the ERK1/2 signaling pathway

Accumulating evidence displays that an abnormal deposition of amyloid beta-peptide (Aβ) is the primary cause of the pathogenesis of Alzheimer's disease (AD). And therefore the elimination of Aβ is regarded as an important strategy for AD treatment. The discovery of drug candidates using culture neuronal cells against Aβ peptide toxicity is believed to be an effective approach to develop drug for the treatment of AD patients. We have previously showed that artemisinin, a FDA-approved anti-malaria drug, has neuroprotective effects recently. In the present study, we aimed to investigate the effects and potential mechanism of artemisinin in protecting neuronal PC12 cells from toxicity of β amyloid peptide. Our studies revealed that artemisinin, in clinical relevant concentration, protected and rescued PC12 cells from Aβ25-35-induced cell death. Further study showed that artemisinin significantly ameliorated cell death due to Aβ25-35 insult by restoring abnormal changes in nuclear morphology, lactate dehydrogenase, intracellular ROS, mitochondrial membrane potential and activity of apoptotic caspase. Western blotting analysis demonstrated that artemisinin activated extracellular regulated kinase ERK1/2 but not Akt survival signaling. Consistent with the role of ERK1/2, preincubation of cells with ERK1/2 pathway inhibitor PD98059 blocked the effect of artemisinin while PI3K inhibitor LY294002 has no effect. Moreover, Aβ1-42 also caused cells death of PC12 cells while artemisinin suppressed Aβ1-42 cytotoxicity in PC12 cells. Taken together, these results, at the first time, suggest that artemisinin is a potential protectant against β amyloid insult through activation of the ERK1/2 pathway. Our finding provides a potential application of artemisinin in prevention and treatment of AD.
Induction of serine racemase by inflammatory stimuli is dependent on AP-1.

Serine racemase (SRace) is an enzyme that catalyzes the conversion of L-serine to pyruvate or D-serine, an endogenous agonist for NMDA receptors. Our previous studies showed that inflammatory stimuli such as Abeta could elevate steady-state mRNA levels for SRace, perhaps leading to inappropriate glutamatergic stimulation under conditions of inflammation. We report here that a proinflammatory stimulus (lipopolysaccharide) elevated the activity of the human SRace promoter, as indicated by expression of a luciferase reporter system transfected into a microglial cell line. This effect corresponded to an elevation of SRace protein levels in microglia, as well. By contrast, dexamethasone inhibited the SRace promoter activity and led to an apparent suppression of SRace steady-state mRNA levels. A potential binding site for NFkappaB was explored, but this sequence played no significant role in SRace promoter activation. Instead, large deletions and site-directed mutagenesis indicated that a DNA element between -1382 and -1373 (relative to the start of translation) was responsible for the activation of the promoter by lipopolysaccharide. This region fits the consensus for an activator protein-1 binding site. Lipopolysaccharide induced an activity capable of binding this DNA element in electrophoretic mobility shift assays. Supershifts with antibodies against c-Fos and JunB identified these as the responsible proteins. An inhibitor of Jun N-terminal kinase blocked SRace promoter activation, further implicating activator protein-1. These data indicate that proinflammatory stimuli utilize a signal transduction pathway culminating in activator protein-1 activation to induce expression of serine racemase.
Hyperspectral CNN Classification with Limited Training Samples

Hyperspectral imaging sensors are becoming increasingly popular in robotics applications such as agriculture and mining, and allow per-pixel thematic classification of materials in a scene based on their unique spectral signatures. Recently, convolutional neural networks have shown remarkable performance for classification tasks, but require substantial amounts of labelled training data. This data must sufficiently cover the variability expected to be encountered in the environment. For hyperspectral data, one of the main variations encountered outdoors is due to incident illumination, which can change in spectral shape and intensity depending on the scene geometry. For example, regions occluded from the sun have a lower intensity and their incident irradiance skewed towards shorter wavelengths. In this work, a data augmentation strategy based on relighting is used during training of a hyperspectral convolutional neural network. It allows training to occur in the outdoor environment given only a small labelled region, which does not need to sufficiently represent the geometric variability of the entire scene. This is important for applications where obtaining large amounts of training data is labourious, hazardous or difficult, such as labelling pixels within shadows. Radiometric normalisation approaches for preprocessing the hyperspectral data are analysed and it is shown that methods based on the raw pixel data are sufficient to be used as input for the classifier. This removes the need for external hardware such as calibration boards, which can restrict the application of hyperspectral sensors in robotics applications. Experiments to evaluate the classification system are carried out on two datasets captured from a field-based platform.
Childhood motor skill proficiency as a predictor of adolescent physical activity.

Cross-sectional evidence has demonstrated the importance of motor skill proficiency to physical activity participation, but it is unknown whether skill proficiency predicts subsequent physical activity.



In 2000, children's proficiency in object control (kick, catch, throw) and locomotor (hop, side gallop, vertical jump) skills were assessed in a school intervention. In 2006/07, the physical activity of former participants was assessed using the Australian Physical Activity Recall Questionnaire. Linear regressions examined relationships between the reported time adolescents spent participating in moderate-to-vigorous or organized physical activity and their childhood skill proficiency, controlling for gender and school grade. A logistic regression examined the probability of participating in vigorous activity.



Of 481 original participants located, 297 (62%) consented and 276 (57%) were surveyed. All were in secondary school with females comprising 52% (144). Adolescent time in moderate-to-vigorous and organized activity was positively associated with childhood object control proficiency. Respective models accounted for 12.7% (p = .001), and 18.2% of the variation (p = .003). Object control proficient children became adolescents with a 10% to 20% higher chance of vigorous activity participation.



Object control proficient children were more likely to become active adolescents. Motor skill development should be a key strategy in childhood interventions aiming to promote long-term physical activity.
IOT Enabled Home Automation through Nodered and MQTT

In today‘s world the word automation seems to be very common but not practically applied to our country at an extent till yet. This breakthrough prospective can be a major part of the smart city. Most of the home automation system available in market lacks certain important features and they are object dependent and expensive. In this paper an internet of thing enabled, cost-effective home automation is developed. Unlike the conventional approach, this system make use NodeRed which is a open source tool for building internet of thing and uses visual programming through nodes to perform certain task. This system can control the outlets, various things inside the home from anywhere in the world. Every part of the home will be equipped with wireless sensor technology that will log the data to the webserver. These WSN technology will be interconnected through MQTT (Telemetry Transport) protocol which is a publish and subscribe tool to establish the communication between different devices. The third feature can revolutionize everything that is notification. User of the home will get notified through email or twitter if any uncertainty is happen. Webserver will access the home automation system through Ngrok which is a secure introspectable webhook development tool. This whole concept make it advance home automation through internet of things.
Modularity of the rodent mandible: integrating bones, muscles, and teeth.

Several models explain how a complex integrated system like the rodent mandible can arise from multiple developmental modules. The models propose various integrating mechanisms, including epigenetic effects of muscles on bones. We test five for their ability to predict correlations found in the individual (symmetric) and fluctuating asymmetric (FA) components of shape variation. We also use exploratory methods to discern patterns unanticipated by any model. Two models fit observed correlation matrices from both components: (1) parts originating in same mesenchymal condensation are integrated, (2) parts developmentally dependent on the same muscle form an integrated complex as do those dependent on teeth. Another fits the correlations observed in FA: each muscle insertion site is an integrated unit. However, no model fits well, and none predicts the complex structure found in the exploratory analyses, best described as a reticulated network. Furthermore, no model predicts the correlation between proximal parts of the condyloid and coronoid, which can exceed the correlations between proximal and distal parts of the same process. Additionally, no model predicts the correlation between molar alveolus and ramus and/or angular process, one of the highest correlations found in the FA component. That correlation contradicts the basic premise of all five developmental models, yet it should be anticipated from the epigenetic effects of mastication, possibly the primary morphogenetic process integrating the jaw coupling forces generated by muscle contraction with those experienced at teeth.
DeepVS: A Deep Learning Based Video Saliency Prediction Approach

In this paper, we propose a novel deep learning based video sa li ncy prediction method, named DeepVS. Specifically, we establ i h a large-scale eye-tracking database of videos (LEDOV), which includes 32 ubjects’ fixations on 538 videos. We find from LEDOV that human attention is more likely to be attracted by objects, particularly the moving objects or the moving parts of objects. Hence, an object-to-motion convolutional neural network (OM-CNN) is developed to predict the intra-frame saliency for DeepVS, w hich is composed of the objectness and motion subnets. In OM-CNN, cross-net m ask and hierarchical feature normalization are proposed to combine the sp atial features of the objectness subnet and the temporal features of the motion su b et. We further find from our database that there exists a temporal correlati on of human attention with a smooth saliency transition across video frames. We th us propose saliencystructured convolutional long short-term memory (SS-Conv LSTM) network, using the extracted features from OM-CNN as the input. Consequ ently, the interframe saliency maps of a video can be generated, which consid er both structured output with center-bias and cross-frame transitions of hum an attention maps. Finally, the experimental results show that DeepVS advances t he tate-of-the-art in video saliency prediction.
Orchestrating nuclear envelope disassembly and reassembly during mitosis

Cell division in eukaryotes requires extensive architectural changes of the nuclear envelope (NE) to ensure that segregated DNA is finally enclosed in a single cell nucleus in each daughter cell. Higher eukaryotic cells have evolved 'open' mitosis, the most extreme mechanism to solve the problem of nuclear division, in which the NE is initially completely disassembled and then reassembled in coordination with DNA segregation. Recent progress in the field has now started to uncover mechanistic and molecular details that underlie the changes in NE reorganization during open mitosis. These studies reveal a tight interplay between NE components and the mitotic machinery.
Vision based inter-vehicle distance estimation with extended outlier correspondence

In this paper, we propose a vision-based robust vehicle distance estimation algorithm that supports motorists to rapidly perceive relative distance of oncoming and passing vehicles thereby minimizing the risk of hazardous circumstances. And, as it is expected, the silhouettes of background stationary objects may appear in the motion scene, which pop-up due to motion of the camera, which is mounted on dashboard of the host vehicle. To avoid the effect of false positive detection of stationary objects and to determine the ego motion a new Morphological Strip Matching Algorithm and Recursive Stencil Mapping Algorithm(MSM-RSMA)is proposed. A new series of stencils are created where non-stationary objects are taken off after detecting stationary objects by applying a shape matching technique to each image strip pair. Then the vertical shift is estimated recursively with new stencils with identified stationary background objects. Finally, relative comparison of known templates are used to estimate the distance, which is further certified by value obtained for vertical shift. We apply analysis of relative dimensions of bounding box of the detected vehicle with relevant templates to calculate the relative distance. We prove that our method is capable of providing a comparatively fast distance estimation while keeping its robustness in different environments changes.
5-GHz Band Highly Linear VCO IC with a Novel Resonant Circuit

This paper presents a 0.35-mum SiGe BiCMOS VCO IC exhibiting a linear VCO gain (Kvco) for 5-GHz band application. To realize a linear Kvco, a novel resonant circuit is proposed. The measured Kvco changes from 224 MHz/V to 341 MHz/V. The ratio of the maximum Kvco to the minimum one is 1.5 which is less than one-half of that of a conventional VCO. The VCO oscillation frequency range is from 5.45 GHz to 5.95 GHz, the tuning range is 8.8 %, and the dc current consumption is 3.4 mA at a supply voltage of 3.0 V. The measured phase noise is -116 dBc/Hz at 1MHz offset, which is similar to the conventional VCO
Recognition and segmentation of connected characters with selective attention

~I "e have modified the original model of selective attention, which was previmtsly proposed by Fukushima, and e~tended its ability to recognize attd segment connected characters in cmwive handwriting. Although the or~¢inal model q/'sdective attention ah'ead)' /tad the abilio' to recognize and segment patterns, it did not alwa)w work well when too many patterns were presented simuhaneousl): In order to restrict the nttmher q/patterns to be processed simultaneousO; a search controller has been added to the original model. Tlw new mode/mainly processes the patterns contained in a small "search area, " which is mo~vd b)' the search controller A ptvliminao' ev~eriment with compltter simttlatiott has shown that this approach is promisittg. The recogttition arid segmentation q[k'haracters can be sttcces~[itl even thottgh each character itt a handwritten word changes its .shape h)" the e[]'ect o./the charactetw
Emotion Detection using EPOC EEG Device

Due to the growing need for computer applications capable of detecting the emotional state of the users , studying emotions in informatics has increased. The direct options of detecting the emotions are inquiries and questionnaires with specific questions which participants answer on the Likert scale. Because of the fact that every participant has to answer all the questions and those need to be manually evaluated, it is not a very efficient method. That is the reason for inventing new methods for classifying emotions for example through physiological responses. Motivated by every day interaction among humans, a great part of the research in this area has explored detecting emotions from facial and voice information. One of the available software solutions is Noldus FaceReader, which can recognize six emotional states: joy, sadness, anger, surprise, fear, disgust, and a neutral state. However, it depends on good light conditions and the accuracy could be also decreased by an object covering part of a participant’s face, e.g., glasses. In order to address these shortcomings, other approaches to detect emotions have been proposed which focus on different physiological information, such as heart rate, skin conductance, and pupil dilation . A still relatively new field of research in affective brain-computer interaction attempts to detect emotions using electroencephalograms (EEGs) . In our approach, we aim to evaluate EEG devices Emotiv EPOC and Emotiv Insight and classify emotions from the data captured by this devices. Our method is based on the method was used by the psychologists. In order to represent emotions we use dimensional approach  which is based on the fact that all subjective feelings could be projected into the 3D space where dimensions are: (i) arousal – positive/negative emotion, (ii) valence – strong/weak emotion, and (iii) tension – tensed/relieved emotion. We omit the third dimension due to the difficulty of determining the amount of tension. When classifying emotions by this method, respondents identify how positive (valence) and how strong (arousal) was their emotion. These values are projected to 2D space, called Valence–Arousal model, which could be divided to four quadrants: strong-
Efficient Training of Convolutional Neural Nets on Large Distributed Systems

Deep Neural Networks (DNNs) have achieved impressive accuracy in many application domains including im-age classification. Training of DNNs is an extremely compute-intensive process and is solved using variants of the stochastic gradient descent (SGD) algorithm. A lot of recent research has focused on improving the performance of DNN training. In this paper, we present optimization techniques to improve the performance of the data parallel synchronous SGD algorithm using the Torch framework: (i) we maintain data in-memory to avoid file I/O overheads, (ii) we propose optimizations to the Torch data parallel table framework that handles multi-threading, and (iii) we present MPI optimization to minimize communication overheads. We evaluate the performance of our optimizations on a Power 8 Minsky cluster with 64 nodes and 256 NVidia Pascal P100 GPUs. With our optimizations, we are able to train 90 epochs of the ResNet-50 model on the Imagenet-1k dataset using 256 GPUs in just 48 minutes. This significantly improves on the previously best known performance of training 90 epochs of the ResNet-50 model on the same dataset using the same number of GPUs in 65 minutes. To the best of our knowledge, this is the best known training performance demonstrated for the Imagenet-1k dataset using 256 GPUs.
‘Windows onto other Worlds. Musical Exoticism in Iranian Cinema: Between National Imaginary and Global Circulation’ Keynote presentation for the conference ‘Exoticism in Contemporary Transnational Cinema: Music and Spectacle’

This interdisciplinary conference explores exoticism, understood as a highly contested discourse on cultural difference as well as an alluring form of alterity that promotes a sense of cosmopolitan connectivity. Presentations and discussions will revolve around the question how the collapsed distances of globalisation and the transnational flows of media and people have transformed exoticism, which is no longer exclusively the projection of Orientalist fantasies of the Other from one centre, the West, but which emanates from multiple localities and is multidirectional in perspective.