nils-herrmann/clean_scirepeval huggingface dataset:
Generalized k means based clustering for temporal data under weighted and kernel time warp
Venue: Pattern Recognit. Lett.

Generalize k means based clustering to temporal data under time warp.Extend time warp measures and temporal kernels to capture local temporal differences.Propose a tractable estimation of the cluster representatives under extended measures.Propose fast solutions that capture both global and local temporal features.Deep analysis on a wide range of 20 non isotropic, linearly non separable public data. Temporal data naturally arise in various emerging applications, such as sensor networks, human mobility or internet of things. Clustering is an important task, usually applied a priori to pattern analysis tasks, for summarization, group and prototype extraction; it is all the more crucial for dimensionality reduction in a big data context. Clustering temporal data under time warp measures is challenging because it requires aligning multiple temporal data simultaneously. To circumvent this problem, costly k medoids and kernel k means algorithms are generally used. This work investigates a different approach to temporal data clustering through weighted and kernel time warp measures and a tractable and fast estimation of the representative of the clusters that captures both global and local temporal features. A wide range of 20 public and challenging datasets, encompassing images, traces and ecg data that are non isotropic (i.e. non spherical) not well isolated and linearly non separable, is used to evaluate the efficiency of the proposed temporal data clustering. The results of this comparison illustrate the benefits of the method proposed, which outperforms the baselines on all datasets. A deep analysis is conducted to study the impact of the data specifications on the effectiveness of the studied clustering methods.
Low cost inkjet antennas for RFID applications

We present paper based inkjet antennas that are fabricated by using silver based cartridges in standard printers. In addition to their low costs, the produced antennas are flexible, environmentally friendly, and suitable for radio frequency identification (RFID) applications. Among alternative choices, hybrid structures involving loop and parasitic meander parts are preferred and successfully combined with passive RFID chips. We also discuss main challenges in the design and fabrication of low cost inkjet antennas and the related RFID tags.
Enhancement of low cost GNSS localization in connected vehicle networks using Rao Blackwellized particle filters
Venue: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

An essential function for automated vehicle technologies is accurate localization. It is difficult, however, to achieve lane level accuracy with low cost Global Navigation Satellite System (GNSS) receivers due to the biased noisy pseudo range measurements. Approaches such as Differential GNSS can improve the accuracy, but usually require an enormous amount of investment in base stations. The emerging connected vehicle technologies provide an alternative approach to improving the localization accuracy. It has been shown in this paper that localization accuracy can be enhanced by fusing GNSS information within a group of connected vehicles and matching the configuration of the group to a digital map to eliminate the common bias in localization. A Rao Blackwellized particle filter (RBPF) was used to jointly estimate the common biases of the pseudo ranges and the vehicles positions. Multipath biases, which are non common to vehicles, were mitigated by a multi hypothesis detection rejection approach. The temporal correlation was exploited through the prediction update process. The proposed approach was compared to the existing static and smoothed static methods in the intersection scenario. Simulation results show that the proposed algorithm reduced the estimation error by fifty percent and reduced the estimation variance by two orders of magnitude.
Technological, Organisational and Environmental Aspects of Audit Technology Acceptance

The purpose of this paper is to use the Technology, Organization Organisation and Environment (TOE) framework to understand the audit technology adoption in audit firms. Previous studies have only looked from the viewpoint of individual auditors and do not use a framework in which to understand the audit technology adoption. The audit technology differs from other information technology adoption because audit tools change the way in which auditors carry their tasks. One of the major contributions in this study is to use the TOE framework to analyse the factors in organisation adoption. Data of this study were gathered through questionnaire surveys that were self administered to 1,367 audit firms registered in the Malaysian Institute of Accountants directory. Our findings indicate that although firms generally acknowledge that there are advantages of audit technology implementation and the benefits outweigh the costs, the firms also recognise that their organisations are only somewhat ready to adopt and their staff's competency are only at a moderate level to be able to use the audit technology. Our survey also indicates that the role played by professional body support is important to increase its adoption.
Metacognitive Reading Strategies, Motivation, and Reading Comprehension Performance of Saudi EFL Students

Metacognitive reading strategies and reading motivation play a significant role in enhancing reading comprehension. In an attempt to prove the foregoing claim in a context where there is no strong culture for reading, this study tries to find out if there is indeed a relationship between and among metacognitive reading strategies, reading motivation, and reading comprehension performance. Prior to finding out relationships, the study tried to ascertain the level of awareness and use of metacognitive reading strategies of the respondents when they read English academic texts, their level of motivation and reading interests, and their overall reading performance. Using descriptive survey and descriptive correlational methods with 60 randomly selected Saudi college level EFL students in an all male government owned industrial college in Saudi Arabia, the study found out that the respondents moderately use the different metacognitive reading strategies when reading academic texts. Of the three categories of metacognitive reading strategies, the Problem Solving Strategies (PROB) is the most frequently used. It was also revealed that the respondents have high motivation to read. They particularly prefer to read humor/comic books. On the level of reading comprehension performance, the respondents performed below average. Using t test, the study reveals that there is no correlation between metacognitive reading strategies and reading comprehension. There is also no correlation between reading interest/motivation and reading comprehension. However, there is positive correlation between reading strategies and reading motivation. The findings of this study interestingly contradict previous findings of most studies, thus invite more thorough investigation along the same line of inquiry.
Recent Trends, Applications, and Perspectives in 3D Shape Similarity Assessment
Venue: Comput. Graph. Forum

The recent introduction of 3D shape analysis frameworks able to quantify the deformation of a shape into another in terms of the variation of real functions yields a new interpretation of the 3D shape similarity assessment and opens new perspectives. Indeed, while the classical approaches to similarity mainly quantify it as a numerical score, map based methods also define (dense) shape correspondences. After presenting in detail the theoretical foundations underlying these approaches, we classify them by looking at their most salient features, including the kind of structure and invariance properties they capture, as well as the distances and the output modalities according to which the similarity between shapes is assessed and returned. We also review the usage of these methods in a number of 3D shape application domains, ranging from matching and retrieval to annotation and segmentation. Finally, the most promising directions for future research developments are discussed.
Geographical location is a key component to effective breeding of clary sage (Salvia sclarea) for essential oil composition

Wild populations of Croatian clary sage (Salvia sclarea L. were examined for variability to determine cultivation suitability in Croatia and Israel for breeding purposes. Phenotypic variability (coefficient of variation; was recorded for inflorescence weight (39.6% inflorescence yield (52.8% and essential oil yield (67.6% when grown in Croatia. Associations were identified between inflorescence yield and essential oil yield (r 0.9; P 0.0001) inflorescence weight and inflorescence yield (r 0.8; P 0.0001) and inflorescence weight and inflorescence length (r 0.6; P 0.0056) suggesting that populations with elongated inflorescence are indirectly associated with higher essential oil yield. In Israel, the populations reached full bloom between the end of May and early June, corresponding on average to 397.5 days post planting. Linalyl acetate, linalool, a terpineol, sclareol, and geranyl acetate were the leading essential oil components in both Croatia and Israel. The principal compounds
On the use of phone gram units in recurrent neural networks for language identification
Venue: Odyssey

In this paper we present our results on using RNN ba sed LM scores trained on different phone gram orders and u sing different phonetic ASR recognizers. In order to avoi d data sparseness problems and to reduce the vocabulary of all possible n gram combinations, a K means clustering procedure was performed using phone vector embeddin gs as a pre processing step. Additional experiments to opti mize the amount of classes, batch size, hidden neurons, stat e unfolding, are also presented. We have worked with the KALAKA3 database for the plenty closed condition  Thank s to our clustering technique and the combination of high le vel phonegrams, our phonotactic system performs ~13% better than the unigram based RNNLM system. Also, the obtained RNNLM scores are calibrated and fused with other scores f rom an acoustic based i vector system and a traditional PP RLM system. This fusion provides additional improvement s showing that they provide complementary information t the LID system.
Order Embeddings of Images and Language
Venue: ICLR

Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual semantic hierarchy over words, sentences, and images. In this paper we advocate for explicitly modeling the partial order structure of this hierarchy. Towards this goal, we introduce a general method for learning ordered representations, and show how it can be applied to a variety of tasks involving images and language. We show that the resulting representations improve performance over current approaches for hypernym prediction and image caption retrieval.
Jointly Modeling Embedding and Translation to Bridge Video and Language
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Automatically describing video content with natural language is a fundamental challenge of computer vision. Re current Neural Networks (RNNs) which models sequence dynamics, has attracted increasing attention on visual interpretation. However, most existing approaches generate a word locally with the given previous words and the visual content, while the relationship between sentence semantics and visual content is not holistically exploited. As a result, the generated sentences may be contextually correct but the semantics (e.g. subjects, verbs or objects) are not true. This paper presents a novel unified framework, named Long Short Term Memory with visual semantic Embedding (LSTM E) which can simultaneously explore the learning of LSTM and visual semantic embedding. The former aims to locally maximize the probability of generating the next word given previous words and visual content, while the latter is to create a visual semantic embedding space for enforcing the relationship between the semantics of the entire sentence and visual content. The experiments on YouTube2Text dataset show that our proposed LSTM E achieves to date the best published performance in generating natural sentences: 45.3% and 31.0% in terms of BLEU@4 and METEOR, respectively. Superior performances are also reported on two movie description datasets (M VAD and MPII MD) In addition, we demonstrate that LSTM E outperforms several state of the art techniques in predicting Subject Verb Object (SVO) triplets.
Multi Modal Event Topic Model for Social Event Analysis
Venue: IEEE Transactions on Multimedia

With the massive growth of social events in Internet it has become more and more difficult to exactly find and organize the interesting events from massive social media data, which is useful to browse, search, and monitor social events by users or governments To deal with this problem, we propose a novel multi modal social event tracking and evolution framework to not only effectively capture multi modal topics of social events, but also obtain the evolutionary trends of social events and generate effective event summary details over time. To achieve this goal, we propose a novel multi modal event topic model (mmETM) which can effectively model social media documents, including long text with related images, and learn the correlations between textual and visual modalities to separate the visual representative topics and non visual representative topics. To apply the mmETM model to social event tracking, we adopt an incremental learning strategy denoted as incremental mmETM, which can obtain informative textual and visual topics of social events over time to help understand these events and their evolutionary trends. To evaluate the effectiveness of our proposed algorithm, we collect a real world dataset to conduct various experiments. Both qualitative and quantitative evaluations demonstrate that the proposed mmETM algorithm performs favorably against several state of the art methods.
Multi modal Multi view Topic opinion Mining for Social Event Analysis
Venue: ACM Multimedia

In this paper, we propose a novel multi modal multi view topic opinion mining (MMTOM) model for social event analysis in multiple collection sources. Compared with existing topic opinion mining methods, our proposed model has several advantages: (1) The proposed MMTOM can effectively take into account multi modal and multi view properties jointly in a unified and principled way for social event modeling. (2) Our model is general and can be applied to many other applications in multimedia, such as opinion mining and sentiment analysis, multi view association visualization, and topic opinion mining for movie review. (3) The proposed MMTOM is able to not only discover multi modal common topics from all collections as well as summarize the similarities and differences of these collections along each specific topic, but also automatically mine multi view opinions on the learned topics across different collections. (4) Our topic opinion mining results can be effectively applied to many applications including multi modal multi view topic opinion retrieval and visualization, which achieve much better performance than existing methods. To evaluate the proposed model, we collect a real world dataset for research on multi modal multi view social event analysis, and will release it for academic use. We have conducted extensive experiments, and both qualitative and quantitative evaluation results have demonstrated the effectiveness of the proposed MMTOM.
Comparison of working length determination using apex locator and manual method ex vivo study

Electronic apex locators can be a useful adjunct with a high level of accuracy for determining the real root canal length in clinical practice.  The objective of this ex vivo study was to evaluate the accuracy of electronic apex locator for real working length determination in comparison with a manual method.  Forty single rooted teeth were used in this study. After coronal access, manual measurement of the real working length of each root canal was performed by placing a size 10 K file, using as reference the exceeding of the file in the apical foramen. The file was retracted by 1 mm, and its extension was measured to determine the real working length. The electronic measurement of the working length was performed in the same teeth using the Joypex 5 electronic apex locator.  Data were submitted to statistical analysis (Wilcoxon Mann Whitney test, P 0.05) and no significant difference was found between the electronic and manual methods (P 0.05)  It may be concluded that the electronic apex locator is a reliable and accurate method for determining the working length of root canals, optimizing the odontometric procedure in clinical practice.
Student Project to Develop a Neural Network based State of Charge Indicator for Primary Batteries

Herb Hess is Professor of Electrical Engineering at the University of Idaho, where he teaches subjects in He received the PhD Degree from the University of Wisconsin Madison in 1993. His research and teaching interests are in power electronics, electric machines and drives, electrical power systems, and analog/mixed signal electronics. He has taught senior capstone design since 1985 at several universities.
The Proteasome Stress Regulon Is Controlled by a Pair of NAC Transcription Factors in Arabidopsis
Venue: Plant Cell

Proteotoxic stress in Arabidopsis is attenuated by a pair of NAC transcription factors that upregulate the synthesis of the 26S proteasome and other factors that promote protein homeostasis. Proteotoxic stress, which is generated by the accumulation of unfolded or aberrant proteins due to environmental or cellular perturbations, can be mitigated by several mechanisms, including activation of the unfolded protein response and coordinated increases in protein chaperones and activities that direct proteolysis, such as the 26S proteasome. Using RNA seq analyses combined with chemical inhibitors or mutants that induce proteotoxic stress by impairing 26S proteasome capacity, we defined the transcriptional network that responds to this stress in Arabidopsis thaliana. This network includes genes encoding core and assembly factors needed to build the complete 26S particle, alternative proteasome capping factors, enzymes involved in protein ubiquitylation/deubiquitylation and cellular detoxification, protein chaperones, autophagy components, and various transcriptional regulators. Many loci in this proteasome stress regulon contain a consensus cis element upstream of the transcription start site, which was previously identified as a binding site for the NAM/ATAF1/CUC2 78 (NAC78) transcription factor. Double mutants disrupting NAC78 and its closest relative NAC53 are compromised in the activation of this regulon and notably are strongly hypersensitive to the proteasome inhibitors MG132 and bortezomib. Given that NAC53 and NAC78 homo and heterodimerize, we propose that they work as a pair in activating the expression of numerous factors that help plants survive proteotoxic stress and thus play a central regulatory role in maintaining protein homeostasis.
qHiPSTER: The Quantum High Performance Software Testing Environment
Venue: ArXiv

We present qHiPSTER, the Quantum High Performance Software Testing Environment. qHiPSTER is a distributed high performance implementation of a quantum simulator on a classical computer, that can simulate general single qubit gates and two qubit controlled gates. We perform a number of single and multi node optimizations, including vectorization, multi threading, cache blocking, as well as overlapping computation with communication. Using the TACC Stampede supercomputer, we simulate quantum circuits "quantum software" of up to 40 qubits. We carry out a detailed performance analysis to show that our simulator achieves both high performance and high hardware efficiency, limited only by the sustainable memory and network bandwidth of the machine.
When and what to automate in software testing? A multi vocal literature review
Venue: Inf. Softw. Technol.

Many organizations see software test automation as a solution to decrease testing costs and to reduce cycle time in software development. However, establishment of automated testing may fail if test automation is not applied in the right time, right context and with the appropriate approach.  The decisions on when and what to automate is important since wrong decisions can lead to disappointments and major wrong expenditures (resources and efforts) To support decision making on when and what to automate, researchers and practitioners have proposed various guidelines, heuristics and factors since the early days of test automation technologies. As the number of such sources has increased, it is important to systematically categorize the current state of the art and practice, and to provide a synthesized overview.  To achieve the above objective, we have performed a Multivocal Literature Review (MLR) study on when and what to automate in software testing. A MLR is a form of a Systematic Literature Review (SLR) which includes the grey literature (e.g. blog posts and white papers) in addition to the published (formal) literature (e.g. journal and conference papers) We searched the academic literature using the Google Scholar and the grey literature using the regular Google search engine.  Our MLR and its results are based on 78 sources, 52 of which were grey literature and 26 were formally published sources. We used the qualitative analysis (coding) to classify the factors affecting the when and what to automate questions to five groups: (1) Software Under Test (SUT) related factors, (2) test related factors, (3) test tool related factors, (4) human and organizational factors, and (5) cross cutting and other factors. The most frequent individual factors were: need for regression testing (44 sources) economic factors (43) and maturity of SUT (39)  We show that current decision support in software test automation provides reasonable advice for industry, and as a practical outcome of this research we have summarized it as a checklist that can be used by practitioners. However, we recommend developing systematic empirically validated decision support approaches as the existing advice is often unsystematic and based on weak empirical evidence.
Red blood cells in retinal vascular disorders.
Venue: Blood cells, molecules diseases

Microvascular circulation plays a vital role in regulating physiological functions, such as vascular resistance, and maintaining organ health. Pathologies such as hypertension, diabetes, or hematologic diseases affect the microcirculation posing a significant risk to human health. The retinal vasculature provides a unique window for non invasive visualisation of the human circulation in vivo and retinal vascular image analysis has been established to predict the development of both clinical and subclinical cardiovascular, metabolic, renal and retinal disease in epidemiologic studies. Blood viscosity which was otherwise thought to play a negligible role in determining blood flow based on Poiseuille's law up to the 1970s has now been shown to play an equally if not a more important role in controlling microcirculation and quantifying blood flow. Understanding the hemodynamics/rheology of the microcirculation and its changes in diseased states remains a challenging task; this is due to the particulate nature of blood, the mechanical properties of the cells (such as deformability and aggregability) and the complex architecture of the microvasculature. In our review, we have tried to postulate a possible role of red blood cell (RBC) biomechanical properties and laid down future framework for research related to hemorrheological aspects of blood in patients with retinal vascular disorders.