Miracle-dz/UniFAR-FTUs huggingface dataset:
[Advances in Intelligent Systems and Computing] Proceedings of the 2012 International Conference of Modern Computer Science and Applications Volume 191 || An Improved Min-Min Algorithm in Cloud Computing

McCaffery (Eds), Systems, Software and Services Process Improvement, EuroSPI 2013 (Communications in Computer and Information Science 364) ISBN 978-3-642-39178-1 7 * € (D) 68,48 | € (A) 70,40 | sFr 85,50 7 € 64,00 | £57.99 Neto (Eds), Robotics in Smart Manufacturing, WRSM 2013 (Communications in Computer and Information Science 371) ISBN 978-3-642-39222-1 7 * € (D) 59,92 | € (A) 61,60 | sFr 75,00 7 € 56,00 | £50.99
Material for Disentangling Latent Hands for Image Synthesis and Pose Estimation

In the following supplementary material, we present more details for our proposed method. Section A provides the derivations for Section 3, the Methodology, in the main manuscripts; Section B provides the details of our network architectures. Ablation experiments and more results are presented in Section C and Section D respectively. Section E shows the numerical evaluation of disentanglement. Note that all the notation and abbreviations here are consistent with the main manuscript.
Sentiment Analysis of Hotel Aspect Using Probabilistic Latent Semantic Analysis, Word Embedding and LSTM

: In the industrial era 5.0, product reviews are necessary for the sustainability of a company. Product reviews are a User Generated Content (UGC) feature which describes customer satisfaction. The researcher used five hotel aspects including location, meal, service, comfort, and cleanliness to measure customer satisfaction. Each product review was preprocessed into a term list document. In this context, we proposed the Probabilistic Latent Semantic Analysis (PLSA) method to produce a hidden topic. Semantic Similarity was used to classify topics into five hotel aspects. The Term Frequency-Inverse Corpus Frequency (TF-ICF) method was used for weighting each term list, which had been expanded from each cluster in the document. The researcher used Word embedding to obtain vector values in the deep learning method from Long Short-Term Memory (LSTM) for sentiment classification. The result showed that the combination of the PLSA + TF ICF 100% + Semantic Similarity method was superior are 0.840 in the fifth categorization of the hotel aspects; the Word Embedding + LSTM method outperformed the sentiment classification at value 0.946; the service aspect received positive sentiment value higher are 45.545 than the other aspects; the comfort aspect received negative sentiment value higher are 12.871 than the other aspects. Other results also showed that sentiment was affected by the aspects.
INFLUENCE OF COOLING SYSTEMS ON THE QUALITY OF OVINE SEMEN CRYOPRESERVED IN STRAWS L

Different types of cooling systems of ram semen were evaluated by the measurement and comparison of cooling rates and their effects on the post-thawing quality. Motility, vigor, morphology damages, viability and acrosomal status were tested at the end of 90 minutes of cooling. Regarding post-thawed semen, the same parameters were used, in addition to the assessment of the integrity of the plasmatic membrane, and the resistance test by the incubation of semen at +37oC for four hours. Semen refrigeration was carried out in a domestic refrigerator and in a horizontal refrigerator. To control the temperature drop of semen in both equipments, the straws were disposed between plastic bags containing water at +32oC, constituting four combinations of cooling procedures: RS (refrigerator without bag), RC (refrigerator with bag), BS (counter refrigerator without bag) and BC (counter refrigerator with bag), resulting in four cooling rates: -1.4oC/min, -0.4oC/min, -2.9°C/min and -0.45oC/min, for RS, RC, BS and BC, respectively. At the end of the cooling period, BS treatment showed the lowest percentage of sperm motility (P<0.05), the lowest mean of live spermatozoa with intact acrosome, and the highest mean of dead spermatozoa with intact acrosome (P<0.05). As for morphological defects, BS treatment had the highest mean whereas the systems RC and BC resulted in the lowest means. There was no significant difference among treatments in relation to frozen-thawed semen at the end of the resistance test. It was concluded that the different cooling rates affected ram semen at the end of cooling stage but not at post-thawing.
DeFusionNET: Defocus Blur Detection via Recurrently Fusing and Refining Multi-Scale Deep Features

Defocus blur detection aims to detect out-of-focus regions from an image. Although attracting more and more attention due to its widespread applications, defocus blur detection still confronts several challenges such as the interference of background clutter, sensitivity to scales and missing boundary details of defocus blur regions. To deal with these issues, we propose a deep neural network which recurrently fuses and refines multi-scale deep features (DeFusionNet) for defocus blur detection. We firstly utilize a fully convolutional network to extract multi-scale deep features. The features from bottom layers are able to capture rich low-level features for details preservation, while the features from top layers can characterize the semantic information to locate blur regions. These features from different layers are fused as shallow features and semantic features, respectively. After that, the fused shallow features are propagated to top layers for refining the fine details of detected defocus blur regions, and the fused semantic features are propagated to bottom layers to assist in better locating the defocus regions. The feature fusing and refining are carried out in a recurrent manner. Also, we finally fuse the output of each layer at the last recurrent step to obtain the final defocus blur map by considering the sensitivity to scales of the defocus degree. Experiments on two commonly used defocus blur detection benchmark datasets are conducted to demonstrate the superority of DeFusionNet when compared with other 10 competitors. Code and more results can be found at: http://tangchang.net
Design and implementation of general examination system based on COM technique

COM technique is analyzed,a general examination system is designed and implemented,It contains proposition module,generating paper module,examination module,reading paper module and management score module.Each module is designed to be one COM component,And improved genetic algorithm is put forward for the generating paper module,this system have received good result in practice because of high efficiency,strong generality,good stabilization,and easy to maintenance.
Deep 3 D Flow Features for Human Action Recognition

The present work investigates the use of 3D flow information for performing Deep Learning (DL)-based human action recognition. Generally, 3D flow fields include rich and fine-grained information, regarding the motion dynamics of the observed human actions. However, despite the great potentials present, 3D flow has not been widely used, mainly due to challenges related to the efficient modeling of the flow information and the addressing of the respective computational complexity issues. In this paper, different techniques are investigated for incorporating 3D flow information in DL action recognition schemes. In particular, a novel sequence modeling approach is introduced, which combines the advantageous characteristics for spatial correlation estimation of Convolutional Neural Networks (CNNs) with the increased temporal modeling capabilities of Long Short Term Memory (LSTM) models. Additionally, an extended CNN-based deep flow model is proposed that extracts features from both the spatial and temporal domains, by applying 3D convolutions; hence, modeling the action dynamics within consecutive frames. Moreover, for compact and efficient 3D motion feature extraction, the combined use of CNNs with a ‘flow colorization’ approach is adopted. The proposed methods significantly outperform similar DL and hand-crafted 3D flow approaches, and compare favorably with most skeleton-based techniques in the currently most challenging public dataset, namely the NTU RGB-D.
Chinese POS Tagging Using Restricted Maximum Entropy Model

This paper presents Chinese Part-of-speech (POS) tagging using maximum entropy technique, in which we introduce a novel gain-driven method for feature selec- tion, then we describe the restricted training method for model learning. We test our approach on the simplifled Chinese corpus of Peking University China and achieve an accuracy of 97.80% and 98.60% over flne and coarse grained tag set - a signiflcant improvement over the existing Chi- nese POS tagger.
Energy Efficient Binarized Neural Networks with Adaptive Voltage and Frequency scaling

Jose Nunez-Yanez University of Bristol Bristol, BS8 1UB, UK j.l. nunez-yanez@bristol.ac. uk Abstract This research investigates the effects of applying adaptive voltage and frequency scaling (AVFS) to a high-performance and reconfigurable binarized neural network. AVFS is made possible by instrumenting the design netlist with timing detectors that enable the exploitation of the operating margins of the FPGA device in a reliable way. The instrumented binarized neural network is targeted to a state-of-the-art FPGA device with nominal operating voltages of 0.85 Volts and fabricated with a 16nm feature size. The results show that the new network can obtain new performance and energy points that are up to 86% better than nominal at an identical level of classification accuracy. The results also indicate that the built-in neural network robustness allows operation beyond the first point of error while maintaining the classification accuracy largely unaffected within a +1/1% accuracy deviation.
1 A strain of Bacillus thuringiensis containing a novel 2 cry 7 Aa 2 gene that is highly toxic to Leptinotarsa 3 decemlineata ( Say ) ( Coleoptera ; Chrysomelidae ) 4

The genome of the Bacillus thuringiensis BM311.1 strain was sequenced and assembled in 12 359 contigs containing a total of 6,390,221 bp. The plasmidic ORF of a putative cry gene from this 13 strain was identified as a potential novel Cry protein of 1138 amino acid residues with a 98% identity 14 respect to Cry7Aa1 protein and a predicted molecular mass of 129.4 kDa. The primary structure of 15 this Cry7Aa2 protein, which revealed the presence of eight conserved blocks and the classical 16 structure of three domains, differed in 28 amino acid residues from that of Cry7Aa1. The cry7Aa2 17 gene was amplified by PCR and then expressed in the acrystalliferous strain BMB171. SDS-PAGE 18 analysis confirmed the predicted molecular mass for the Cry7Aa2 protein and revealed that, after 19 in vitro trypsin incubation, it was degraded to a toxin of 62 kDa. However, when treated with 20 digestive fluids from Leptinotarsa decemlineata larvae two proteinase-resistant fragments of 60 and 21 65 kDa were produced. Spore and crystal mixture produced by the wild-type BM311.1 strain against 22 L. decemlineata neonate larvae resulted in a LC50 (18.8 μg/ml), which was statistically equal to the 23 estimated LC50 (20.8 μg/ml) for the recombinant BMB17-Cry7Aa2 strain. In addition, when this 24 novel toxin was activated in vitro with commercial trypsin, the LC50 value was reduced 4 times 25 approximately (LC50 = 4.9 μg/ml). The advantages of Cry7Aa2 protoxin compared to Cry7Aa1 26 protoxin when used in the control of insect pests are discussed. 27
Detection and Classification of Acoustic Scenes and Events 2019 Challenge ACOUSTIC SCENE CLASSIFICATION USING ATTENTION-BASED CONVOLUTIONAL NEURAL NETWORK Technical Report

This technical report describes the Task 1 Subtask A (Acoustic Scene Classification, ASC) of the DCASE 2019 challenge whose goal is to classify a test audio recording into one of the predefined classes that characterizes the environment. We detemine to use mel-spectrogram as audio feature and deep convolutional neural networks (CNNs) as classifier to classify acoustic scenes. In our method, spectrogram of every audio clip is divided in two ways. In addition, we introduce attention mechanism to further improve the performance. Experimental results illustrate that our best model can achieve classification accuracy of around 70.7% for Development dataset, which is superior to the baseline system with the accuracy of 62.5%.
LIMITS TO GENERAL EXPERTISE : A STUDY OF IN-AND OUT-OF-FIELD GRAPH INTERPRETATION

Graphs are pervasive features in professional science journals, which makes graphing one of (if not the) most important practice (and therefore skill) of professional science. Scientists generally are expected to be experts in graphing. Contrary to this expectation, recent investigations showed that scientists asked to interpret graphs from introductory-level textbooks in their own field did not at all exhibit expert-like behavior. The present study was designed to understand better the nature of graphing practices among professional scientists. I investigated the similarities and differences in scientists’ interpretation of structurally identical in-field and out-of-field graphs. Seventeen physicists interpreted 3 graphs that were derived from entry-level university textbooks in ecology—for cross validation purposes, these were the same graphs used in an earlier expert-expert study—and 3 structurally identical graphs from the field of physics. My analyses reveal that the graphing expertise of physicists is limited even within their field. Their graph interpretations are highly idiosyncratic and contingent both within and across content domains. Common to the interpretive practices on in-field and out-of field graph was that scientists interpreted them according to the purposes of (a) graphing in science in general and (b) those of the graph interpretation interview session specifically. In using varying resources and in experiencing breakdowns, they exhibited considerable differences between in-field and out-of-field graph interpretations. Working on in-field graphs, they drew on general knowledge and prior experiences from their professional life, whereas in the context of out-of-field graph interpretations, scientists provided verbal equivalents for the visible, surface features of the line graphs and drew on mundane everyday life experiences to explicate them. Wolff-Michael Roth 312
Extended End-to-End optimized Image Compression Method based on a Context-Adaptive Entropy Model

In this paper, we propose an extended compression method using a context-adaptive entropy model. Based on the Lee et al. [11]’s approach, we extend the network structure so that compression and quality enhancement methods are jointly optimized. In terms of contexts for estimating distributions, we additionally use offset information. By exploiting the extended structure and the additional contexts, we obtain substantially improved compression performance, in terms of multi-scale structural similarity (MSSSIM) index, compared to the model without the extensions.
Generating Video from Single Image and Sound

In this paper, we propose a method of generating a video linked to sound from a single image and a few seconds of sound while maintaining the appearance of the image. Conventional video generation methods from sound require key points extraction related to the sound in each object, such as the mouth in speech and arms in musical instrument performance. They can not be applied to objects whose shape changes significantly like fireworks. The proposed method can generate a video without extracting specific key points from images. We experimented not only the mouth shape and body pose of human treated in the conventional ways, but also fireworks and sea waves where it is difficult to design key points.
DOID: A Lexical Analyzer for Understanding Mid-Level Compilation Processes

: This research was undertaken to give students in the local Nigerian environment a hands-on, practical and direct knowledge of Compiler Construction and Automata Theory, with the motivation to make it the Country’s first programming language. The project was carried out using the Lex/Flex Lexical Analyzer Generator and the C Language. Compiler construction and automata theory has been one of the tougher subjects for local students in the Nigerian region. The purpose of this research is to make our programmers better understand the processes in program compilation and also catch up with the interest of nonprogrammers who may become fascinated by the exquisiteness of programming when it is only at the highest level, which is more like speaking to a fellow human. Locals and students will also be more interested in the subject matter when they are to learn a Language developed in their country
Knowledge Base Construction from Unstructured Text

 Abstract : Knowledge base assumes a critical job in numerous cutting edge applications. Developing learning base from unstructured content is a testing issue because of its inclination. Subsequently, numerous methodologies propose to change unstructured content to organized content so as to make an Knowledge base. Such methodologies can't yet give sensible outcomes to mapping an extricated predicate to its indistinguishable predicate in another information base. Predicate mapping is a basic system since it can lessen the heterogeneity issue and increment accessibility over the portrayal. A learning base development framework is proposed. In the framework, a mixture mix of a standard based methodology and a closeness based methodology is exhibited for mapping a predicate to its indistinguishable predicate in an information base portrayal. Changing unstructured content into a formal portrayal is a vital objective of the Semantic Web so as to encourage the mix and recovery of data. The development of Knowledge Graphs (KGs) seeks after such a thought, where named elements (genuine things) and their relations are separated from content. The procedure incorporates substance acknowledgment, element goals, element connecting, connection extraction lastly the RDF readiness. For such reason, procedures for favoring the extraction and connecting of named substances with KG people, and also, their relationship with syntactic units that lead to creating increasingly rational certainties are displayed. It likewise gives choices to choosing the extricated data components for making possibly valuable RDF triples for the KG. The incorporation of data extraction units with linguistic structures give a superior comprehension of recommendation based development of KGs.
Virtual Validation and Verification of the VaMEx Initative

We present an overview of the Valles Marineris Explorer (VaMEx) initiative, a DLR-funded project line for the development of required key technologies to enable a future swarm exploration of the Valles Marineris on Mars. The Valles Marineris is a wide canyon range, near the Martian equator. The so far still fictive VaMEx mission scenario compromises a swarm of different robots, including rovers, flying drones and a hominid robot. Here, we present VaMEx-VTB, a virtual testbed (VTB) with a digitalized map of the large and fragmented terrain of the Valles Marineris. The VaMEx-VTB allows an adjustable validation as well as verification of the complex mission design in virtual reality, due to its modular design. It shall also be used in preparation of field tests in the near future for validation of each swarm element’s ability for interactive swarm cooperation and collaboration.
Web Information Gathering Using Personalized Ontology Model

Nowadays, how to gather useful and meaningful information from the Web has become challenging to all users because of the explosion in the amount of Web information. However, the mainstream of Web information gathering techniques has many drawbacks, as they are mostly keyword-based. It is argued that the performance of Web information gathering systems can be significantly improved if user background knowledge is discovered and a knowledge-based methodology is used. In this paper, a knowledge-based model is proposed for Web information gathering. The model uses a world knowledge base and user local instance repositories for user profile acquisition and the capture of user information needs. The knowledge-based model was successfully evaluated by comparing a manually implemented user concept model. The proposed knowledge-based model contributes to better designs of knowledge-based and personalized Web information gathering systems. Ontology’s are mostly used to represent user profiles in personalized web information gathering as a model for Knowledge description and formalization. However, when representing user profiles, so many models had utilized only knowledge from either a user local information or global knowledge base. In this paper, an ontology model is proposed for representing user background knowledge for personalized web information gathering. This model constructs user personalized ontology’s by extracting world knowledge from the system and discovering user background knowledge from user local instance repositories. By comparing it against benchmark models in web information gathering, the ontology model is evaluated.
A Green, Modular and Fair Packet Scheduler for Boosting Throughput over Wireless Links

We study the problem of defining and implementing a packet scheduler able to give the high performance of a wired packet scheduler over a wireless link, i.e. a low execution time with low power consumption, high QoS guarantees and high throughput. A common solution is using a single, integrated scheduler that deals both with the QoS guarantees and the wireless link issues. Unfortunately, such an approach is little flexible and does not allow any of the existing high-quality schedulers for wired links to be used without modification. To address these issues, in this paper we validate a modular architecture which permits the use, as they are, of existing packet schedulers for wired links over wireless links, and at the same time allows the flexibility to adapt to different channel conditions. We validate the effectiveness of the modular architecture by showing, through experimental results, that this architecture enables us to get a new scheduler with the following features, just by combining existing schedulers: execution time and energy consumption close to that of just a Deficit Round Robin, accurate fairness and delay guarantees, possibility to set, by changing one parameter, the desired trade-off between throughput-boosting level and granularity of the service guarantees. In particular, we show that this scheduler, which we named High-throughput Twin Fair scheduler (HFS), outperforms one of the most accurate and efficient integrated schedulers available in the literature.