Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation into Flow Property Measurements of Low-Slump Concrete Using Vibrating Slope Apparatus (VSA)

In this paper, operation, data interpretation, and validity of vibrating slope apparatus (VSA) tests for concrete flow property measurements were investigated Results from standard slump tests and VSA tests of 14 different concrete mixes in a total of 29 batches of mixtures are presented. The concrete mixes had different water-to-cement ratios (w/c), different dosages of water reducing agent (WRA), and different types and replacement levels of fly ash. The test results indicated that the original method for VSA data analysis was difficult to manage, and the test result interpretations were inappropriate. Two new parameters, the time for starting mass loss (Tstart) and the inverse of average flow rate for 20% mass loss (1/FRavg) at the chute angle of 10°, were therefore proposed to describe concrete flow property. These two parameters, resulting from the original VSA test data, are easy to identify and easy to use. A strong correlation was found between Tstart and concrete slump. Using Tstart and 1/FRavg, the effects of w/c, WRA, and fly ash on concrete rheological behavior were appropriately characterized. The concrete flowability loss with time was also properly expressed.
Payload Identification for Quadruped Robots

The identification of inertial parameters is crucial to achieve high-performance model-based control of legged robots. The inertial parameters of the legs are typically not altered during expeditions and therefore are best identified offline. On the other hand, the trunk parameters depend on the modules mounted on the robot, like a motor to provide the hydraulic power, or different sets of cameras for perception. This motivates the use of recursive approaches to identify online mass and the position of the Center of Mass (CoM) of the robot trunk, when a payload change occurs. We propose two such approaches and analyze their robustness in simulation. Furthermore, experimental trials on our 80-kg quadruped robot HyQ show the applicability of our strategies during locomotion to cope with large payload changes that would otherwise severely compromise the balance of the robot.
Classification of Upper Limb Motion Trajectories Using Shape Features

To understand and interpret human motion is a very active research area nowadays because of its importance in sports sciences, health care, and video surveillance. However, classification of human motion patterns is still a challenging topic because of the variations in kinetics and kinematics of human movements. In this paper, we present a novel algorithm for automatic classification of motion trajectories of human upper limbs. The proposed scheme starts from transforming 3-D positions and rotations of the shoulder/elbow/wrist joints into 2-D trajectories. Discriminative features of these 2-D trajectories are, then, extracted using a probabilistic shape-context method. Afterward, these features are classified using a k-means clustering algorithm. Experimental results demonstrate the superiority of the proposed method over the state-of-the-art techniques.
The role of knowledge sharing and Transactive Memory System on Condition Based Maintenance policy

Today, Condition Based Maintenance (CBM) has reached a sophisticated level in industry and the advantages of predictive maintenance are accepted globally. However, although lots of effort has been put into improving methods and techniques in CBM, most of predictive maintenance programs fail to achieve their predefined goals, which make them financially unjustifiable. In this paper, we argue that organizational factors play a crucial role in CBM implementation by their influence on knowledge sharing processes and the organizational Transactive Memory System (TMS). This proposition has been corroborated by means of a case study within an industrial process plant.
Recognition errors in the quantification of micro-organisms by fluorescence microscopy.

OBJECTIVES An interlaboratory comparison of fluorescence microscopic counting of micro-organisms was carried out to assess counting errors in the measurement of micro-organisms in bioaerosols generated during handling of municipal waste. METHODS Series of 27 replicate samples were collected in the work environment with a modified field exposure chamber. The preparation methods of three Scandinavian laboratories were compared. Four microscopists from these laboratories performed the counts which were also compared. Duplicate counting of identical microscopic fields allowed the assessment of recognition errors. RESULTS The field exposure chamber collected replicate samples with a relative standard deviation of 5% when particles < or = 15 microm aerodynamic diameter were collected. Storage time of 40-200 days had no significant influence on the total micro-organism count. Differences between preparation methods were from 2 to 35% for bacteria, and from 15 to 35% for fungal spores when samples were analysed in Oslo; the results for fungal spore counts were significantly different (P < 0.01). These differences were not confirmed when samples were analysed in Umeå, Copenhagen and Oslo using those laboratories methods. These results can be explained by less efficient redispersion of aggregates when the Umeå and Copenhagen methods were recreated in Oslo yielding a greater number of innumerable aggregates. Differences between microscopists were minor for fungal spores (2-12%) but substantial for bacteria (4-53%). A major source of error was the recognition of bacteria which had a relative standard deviation (rsd) of 37% although a lower size limit of 0.75 microm was adopted for counting of bacteria. Fungal spores were recognised with much better precision (rsd 9%). CONCLUSIONS Recognition errors of bacteria may be substantial and more specific fluorochromes are needed for fluorescence microscopic counting of micro-organisms.
Theoretical and Experimental Study on Die Pressure Prediction in Extrusion of Wood-Plastic Composite

This article presents a theoretical and experimental study on the measurement and prediction of die pressure in the extrusion process of wood-plastic composite (WPC). Die pressure is an important parameter in processing of WPCs as it governs the product strength, quality, and the final output rate, which directly affects the economy of the production. In this research work, a modular die was designed to accommodate for various circular die inserts to produce rod-shaped products having various diameters. Experimentation was carried out to record the pressure for each product. To develop a theoretical formulation for die pressure, two different schemes were considered and modified: non-Newtonian flow and hot extrusion. The comparison of the experimental and theoretical results demonstrated that the experimental values are between the values predicted by the two theoretical schemes. However, it was shown that by applying a correction factor (based on the compaction ratio of die) into the hot extrusion scheme, a theoretical ground can be suggested for pressure prediction in WPC processing. It appeared that the WPC flow in the die resembles more of a plug flow.
Combining Shallow Text Processing and Machine Learning in Real World Applications

In this paper, we present first results we achieved and experiences we had combining shallow text processing methods with machine learning tools. In two research projects, where DFKI and industrial partners are involved, Ger-man real world texts have to be classified into several predefined categories. We will point out that decisions concerning questions such as how deep the texts have to be analysed linguistically and how ML tools must be parameterized are highly domain and data dependent. On the other hand, there are some constants or heuristics which may show in the right direction for future applications.
Endophytic Bacteria and their Potential Application to Improve the Phytoremediation of Contaminated Environments

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A Fault Localization Framework to Alleviate the Impact of Execution Similarity

Coverage-based fault localization (CBFL) techniques contrast the execution spectra of a program entity to assess the extent of how much a program entity is being related to faults. However, different test cases may result in similar executions, which further make the execution spectra of program entities be indistinguishable among similar executions. As a consequence, most of the current CBFL techniques are impacted by the noise of indistinguishable spectra. To alleviate the impact of execution similarity and improve the effectiveness of CBFL techniques, we propose a general fault localization framework. This framework is general to current execution spectra based CBFL techniques, which could synthesize a fault localization technique based on a given base technique. To synthesize the new technique, we use the concept of coverage vector to model execution spectra and capture the execution similarity, then reduce the impact of execution similarity by counting distinct coverage vectors, and finally assess the suspiciousness of basic blocks being related to faults with the spectra of distinct coverage vectors. We adopt four representative fault localization techniques as base techniques, use seven Siemens programs and three median-sized real-life UNIX utility programs as subject programs, to conduct an experimental study on the effectiveness of our framework. The empirical evaluation shows that our framework can effectively alleviate the impact of execution similarity and generate more effective fault localization techniques based on existing ones.
Compressive video sampling

Compressive sampling is a novel framework that exploits sparsity of a signal in a transform domain to perform sampling below the Nyquist rate. In this paper, we apply compressive sampling to significantly reduce the sampling rate of video. A practical system is developed that first splits each video frame into non-overlapping blocks of equal size. Compressive sampling is then performed on sparse blocks, determined by predicting sparsity based on previous reference frames which are sampled conventionally. The blocks identified as sparse are reconstructed using the orthogonal matching pursuit algorithm, whereas the remaining blocks are sampled fully. Thus, the acquisition complexity and sampling time are reduced, while exploiting the local sparsity, within the DCT domain, of a video stream. Our simulation results indicate up to 50% saving in acquisition for Y-components of video with very small performance loss compared to traditional sampling.
Gaze Estimation From Color Image Based on the Eye Model With Known Head Pose

This paper proposes a novel method of gaze estimation based on an eye model with known head pose. The most crucial factors in the eye-model-based approach to gaze estimation are the 3-D positions of the eyeball and iris centers. In the proposed method, an RGB-D camera, Kinect sensor, is used to obtain the head pose as well as the eye region of the color image. The 3-D position of the eyeball center is determined in the calibration phase by gazing at the center of the color image camera. Then, to estimate the 3-D position of the iris center, the 3-D contour of the iris is projected onto the color image with the known head pose obtained from color and depth cues of an RGB-D camera. Thus, the ellipse of the iris in the image can be described using only two parameters: the yaw and pitch angles of the eyeball in the iris coordinate system, rather than the conventional five parameters of an ellipse. The proposed method can fit an iris that is not complete due to eyelid occlusion. The average errors of vertical and horizontal angles of the gaze estimation for seven subjects are 5.9° and 4.4°, respectively. The processing speed is as high as 330 ms per frame. However, for lower resolution and poor illumination images, as tested on the public database EYEDIAP, the performance of the proposed eye-model-based method is inferior to that of the-state-of-the-art appearance-based method.
Validation of Reference Genes for Gene Expression Studies in Virus-Infected Nicotiana benthamiana Using Quantitative Real-Time PCR

Nicotiana benthamiana is the most widely-used experimental host in plant virology. The recent release of the draft genome sequence for N. benthamiana consolidates its role as a model for plant–pathogen interactions. Quantitative real-time PCR (qPCR) is commonly employed for quantitative gene expression analysis. For valid qPCR analysis, accurate normalisation of gene expression against an appropriate internal control is required. Yet there has been little systematic investigation of reference gene stability in N. benthamiana under conditions of viral infections. In this study, the expression profiles of 16 commonly used housekeeping genes (GAPDH, 18S, EF1α, SAMD, L23, UK, PP2A, APR, UBI3, SAND, ACT, TUB, GBP, F-BOX, PPR and TIP41) were determined in N. benthamiana and those with acceptable expression levels were further selected for transcript stability analysis by qPCR of complementary DNA prepared from N. benthamiana leaf tissue infected with one of five RNA plant viruses (Tobacco necrosis virus A, Beet black scorch virus, Beet necrotic yellow vein virus, Barley stripe mosaic virus and Potato virus X). Gene stability was analysed in parallel by three commonly-used dedicated algorithms: geNorm, NormFinder and BestKeeper. Statistical analysis revealed that the PP2A, F-BOX and L23 genes were the most stable overall, and that the combination of these three genes was sufficient for accurate normalisation. In addition, the suitability of PP2A, F-BOX and L23 as reference genes was illustrated by expression-level analysis of AGO2 and RdR6 in virus-infected N. benthamiana leaves. This is the first study to systematically examine and evaluate the stability of different reference genes in N. benthamiana. Our results not only provide researchers studying these viruses a shortlist of potential housekeeping genes to use as normalisers for qPCR experiments, but should also guide the selection of appropriate reference genes for gene expression studies of N. benthamiana under other biotic and abiotic stress conditions.
Using Segmentation to Predict the Absence of Occluded Parts

Occlusion poses a significant difficulty for detecting and localizing object keypoints and subsequent fine-grained identification. We propose a part-based face detection model that utilizes bottom-up class-specific segmentation in order to jointly detect and segment out the foreground pixels belonging to the face. The model explicitly represents occlusion of parts at the detection phase, allowing for hypothesized figure-ground segmentation to suggest coherent patterns of part occlusion. We show that this bi-directional interaction between recognition and grouping results in state-of-the-art part localization accuracy for challenging benchmarks with significant occlusion and yields substantial gains in the precision of keypoint occlusion prediction.
Efficient Removal of Co2+ from Aqueous Solution by 3-Aminopropyltriethoxysilane Functionalized Montmorillonite with Enhanced Adsorption Capacity

To achieve a satisfactory removal efficiency of heavy metal ions from wastewater, silane-functionalized montmorillonite with abundant ligand-binding sites (-NH2) was synthesized as an efficient adsorbent. Ca-montmorillonite (Ca-Mt) was functionalized with 3-aminopropyl triethoxysilane (APTES) to obtain the APTES-Mt products (APTES1.0CEC-Mt, APTES2.0CEC-Mt, APTES3.0CEC-Mt, APTES4.0CEC-Mt) with enhanced adsorption capacity for Co2+. The physico-chemical properties of the synthesized adsorbents were characterized by spectroscopic and microscopic methods, and the results demonstrated that APTES was successfully intercalated into the gallery of Ca-Mt or grafted onto the surface of Ca-Mt through Si-O bonds. The effect of solution pH, ionic strength, temperature, initial concentrations and contact time on adsorption of Co2+ by APTES-Mt was evaluated. The results indicated that adsorption of Co2+ onto Ca-Mt, APTES1.0CEC-Mt and APTES2.0CEC-Mt can be considered to be a pseudo-second-order process. In contrast, adsorption of Co2+ onto APTES3.0CEC-Mt and APTES4.0CEC-Mt fitted well with the pseudo-first-order kinetics. The adsorption isotherms were described by the Langmuir model, and the maximum adsorption capacities of APTES1.0CEC-Mt, APTES2.0CEC-Mt, APTES3.0CEC-Mt and APTES4.0CEC-Mt were 25.1, 33.8, 61.6, and 61.9 mg·g-1, respectively. In addition, reaction temperature had no impact on the adsorption capacity, while both the pH and ionic strength significantly affected the adsorption process. A synergistic effect of ion exchange and coordination interactions on adsorption was observed, thereby leading to a significant enhancement of Co2+ adsorption by the composites. Thus, APTES-Mt could be a cost-effective and environmental-friendly adsorbent, with potential for treating Co2+-rich wastewater.
Integrated Common Sense Learning and Planning in POMDPs

We formulate a new variant of the problem of planning in an unknown environment, for which we can provide algorithms with reasonable theoretical guarantees in spite of large state spaces and time horizons, partial observability, and complex dynamics. In this variant, an agent is given a collection of example traces produced by a reference policy, which may, for example, capture the agent's past behavior. The agent is (only) asked to find policies that are supported by regularities in the dynamics that are observable on these example traces. We describe an efficient algorithm that uses such "common sense" knowledge reflected in the example traces to construct decision tree policies for goal-oriented factored POMDPs. More precisely, our algorithm (provably) succeeds at finding policy for a given input goal when (1) there is a CNF that is almost always observed satisfied on the traces of the POMDP, capturing a sufficient approximation of its dynamics and (2) for a decision tree policy of bounded complexity, there exist small-space resolution proofs that the goal is achieved on each branch using the aforementioned CNF capturing the "common sense rules. " Such a CNF always exists for noisy STRIPS domains, for example. Our results thus essentially establish that the possession of a suitable exploration policy for collecting the necessary examples is the fundamental obstacle to learning to act in such environments.
An Evaluation Framework and a Benchmark for Multi/Hyperspectral Image Compression

This paper benchmarks three multi/hyperspectral image compression approaches: the classic Multi-2D compression approach and two different implementations of 3D approach Full 3D and Hybrid. All approaches are combined with a spectral PCA decorrelation stage to optimize performance. These three compression approaches are compared within a larger comparison framework than the conventionally used PSNR, which includes eight metrics divided into three families. The comparison is carried out with regard to variations in bitrates, spatial, and spectral dimensions variations of images. The time and memory consumption difference between the three approaches is also discussed. Results of this comparison show the weaknesses and strengths of each approach.
Mary had a little scoreTable* or the reacTable* goes melodic

This paper introduces the scoreTable*, a tangible interactive music score editor which started as a simple application for demoing "traditional" approaches to music creation, using the reacTable* technology, and which has evolved into an independent research project on its own. After a brief discussion on the role of pitch in music, we present a brief overview of related tangible music editors, and discuss several paradigms in computer music creation, contrasting synchronous with asynchronous approaches. The final part of the paper describes the current state of the scoreTable* as well as its future lines of research.
Forecasting Word Model: Twitter-based Influenza Surveillance and Prediction

Because of the increasing popularity of social media, much information has been shared on the internet, enabling social media users to understand various real world events. Particularly, social media-based infectious disease surveillance has attracted increasing attention. In this work, we specifically examine influenza: a common topic of communication on social media. The fundamental theory of this work is that several words, such as symptom words (fever, headache, etc.), appear in advance of flu epidemic occurrence. Consequently, past word occurrence can contribute to estimation of the number of current patients. To employ such forecasting words, one can first estimate the optimal time lag for each word based on their cross correlation. Then one can build a linear model consisting of word frequencies at different time points for nowcasting and for forecasting influenza epidemics. Experimentally obtained results (using 7.7 million tweets of August 2012 – January 2016), the proposed model achieved the best nowcasting performance to date (correlation ratio 0.93) and practically sufficient forecasting performance (correlation ratio 0.91 in 1-week future prediction, and correlation ratio 0.77 in 3-weeks future prediction). This report is the first of the relevant literature to describe a model enabling prediction of future epidemics using Twitter.
Effect of Surface Roughness of Roll on Coefficient of Friction in Hot Rolling(Forming Processing and Thermomechanical Treatment)

In order to understand the tribological behavior at the interface between roll and workpice in hot steel rolling, it is important to measure the coefficient of friction and examine the effect of the tribological factors on the coefficient of friction. In this paper, the effects of the surface roughness of roll on the coefficient of friction are investigated by using the tribo-simulatior testing machine for hot rolling developed by the authors. The workpice material used is SPHC. The roll material is SKD11 and the surface roughnesses are 0.1, 0.2, 0.4 and 0.8 μm Ra. The rolling tests are carried out at a temperature of 800°C during a rolling distance of 400 mm, changing the rolling speed from 50 to 70 m/min. The colza oil is used as a base oil and the emulsion concentrations are 0.1 and 3.0%. The coefficient of friction at an emulsion concentration of 3.0% dose not depend on the surface roughness of roll. However, the coefficient of friction at an emulsion concentration of 0.1% decreases with increasing surface roughness of roll up to a surface roughness of 0.3 μm Ra and over 0.3 μm Ra it increases with increasing surface roughness of roll.
Gold Nanoparticle's Temperature Effect in Photoacoustic Tomography

With the help of Photoacoustic tomography ( PAT) to precise positioning the tumor in tissue which use gold nanoparticle as contrast agents , this paper proposes the One Temperature Model (OTM) to analysis the influence of the temperature effect in PAT. The first time quantitative analysis and verify the nanoparticle’s temperature gradient on the importance of PAT.