Miracle-dz/UniFAR-FTUs huggingface dataset:
Trust Evaluation in Online Social Networks Based on Knowledge Graph

With the development of Online Social Networks(OSNs), OSNs have been becoming very popular platforms to publish resources and to establish relationship with friends. However, due to the lack of prior knowledge of others, there are usually risks associated with conducting network activities, especially those involving money. Therefore, it will be necessary to quantify the trust relationship of users in OSNs, which can help users decide whether they can trust another user. In this paper, we present a novel method for evaluating trust in OSNs using knowledge graph (KG), which is the cornerstone of artificial intelligence. And we focus on the two contributions for trust evaluation in OSNs: (i) a novel method using RNN to quantify trustworthiness in OSNs, which is inspired by relationship prediction in KG; (ii) a Path Reliability Measuring algorithm(PRM) to decide the reliability of a path from the trustor to the trustee. The experiment result shows that our method is more effective than traditional methods.
Fluid-structure interaction of shock absorber for structure-borne noise based on compensation valve opening

To study the structure-borne noise of shock absorber caused by the opening of the compensation valve against the spring force acting on the compensation valve and the adhesion force between the compensation valve and its seat, a fluid-structure interaction method is adopted to analyze the generation mechanism at the beginning of the extension stroke. A mathematical model is established for the shock absorber. A three-dimensional finite element model is developed by ADINA and the leader-follower method is introduced to control the mesh distortion. The displacement curve of the compensation valve is computed. The flow field distribution and characteristics are obtained before and after the opening of the compensation valve.
RGVCD: A New Real-time Game Video Clip Detection System

Interesting video clip detection is an important task in computer vision, which can be used as data preprocessing for a wide range of applications such as video retrieval and content-based video clustering. In this paper, we propose a new real-time game video clip detection system for interesting moments termed as RGVCD. During training period, we generate a set of game video clips to train a spatial-temporal network. Given a test video, RGVCD first generates potentially interesting video clips. For each clip, we extract features from images and optical flow using deep convolutional network. Finally, RGVCD outputs interesting degree score based on the spatial-temporal features. Additionally, we collect a set of games video clips and provide the corresponding binary label to denote whether the video clip is of our interest. With this dataset, our system demonstrates its efficiency in real-time interesting video clip detection with high accuracy.
Pointing techniques for distant targets in virtual reality

Raycasting is the most common target pointing technique in virtual reality environments. However, performance on small and distant targets is impacted by the accuracy of the pointing device and the user's motor skills. Current pointing facilitation techniques are currently only applied in the context of the virtual hand, i.e. for targets within reach. We propose to add a cursor on the ray, controlled by the user. This allows target pointing techniques by proximity to be applied, such as the Bubble Cursor. We describe a study comparing several visual feedbacks for this technique. Our results show that just highlighting the nearest target reduces the selection time by 14.8% and the error rate by 82.6% compared to Raycasting in the general case. For small targets, the selection time is reduced by 25.7% and the error rate by 90.8%.
Numerical simulation on Air-decking Charge in the Fragmentation of Concrete

A numerical method is proposed to study the fragmentation of concrete subjected to detonation load by explosives in borehole under air-decking charge. Four comparisons have been performed with AUTODYN-2D to investigate the effects of several conditions on the fragmentation of concrete, including Air-decking locations, air-decking ratios, initiation locations, and explosives types. The numerical results show that compared to the fully coupling borehole, air-decking charge can reduce the initial borehole pressure and redistribute the explosives energy. More detonation energy can accumulate in air-decking rather than crush rock and soil medium close to the explosive in the blast hole. Therefore, more energy may contribute to the consequent crack propagation.
Chinese Word Embeddings with Subwords

Word embeddings are very useful in variety of natural language processing tasks. Recently, more researches have focused on learning word embeddings with morphological knowledge of words, such as character and subword information. In this paper, we present a new method to use subwords and characters together to enhance word embeddings (SWE). In our model, we use subword and character vectors to modify the direction of word vectors, instead of adding them directly. We evaluate SWE on both word similarity task and analogical reasoning task. The results demonstrate that our model can learn better Chinese word embeddings than other baseline models.
Characterize the transition from menus to hotkeys

This paper aims at better understanding the transition from menus to shortcuts. We first discuss the limitations and the opportunities of the theoretical and empirical characterizations of this transition. We then consider keyboard shortcuts as a case study and manually annotate empirical data to estimate several behavioral markers such as the initial switch, the transition duration or the performance dip. These markers serve to precisely characterize and compare three interaction techniques. Finally, we compare two methods to automatically characterize the transition from menus to shortcuts.
Development and Biomechanical Analysis through Inertial Sensors for Human Motion

Biomechanics is a discipline that studies and makes physical analyzes of the movements of the human body. The goal of Biomechanics in sports activities is the characterization and improvement of movement techniques based on scientific knowledge. Currently, this science is very important and has made multiple contributions to the sport, among which it is possible to mention the analysis and improvement of sports techniques, the prevention of injuries, the improvement of the performance of sports equipment, etc. With regard to research, biomechanical parameters for the analysis of movement in a lacrosse player are kinematics, dynamometry, and electromyography. Biomechanics being very important, although it is considered a very theoretical science, complex and impractical. However, it is important for Biomechanics to overcome the challenges in order to more clearly glimpse its role as a useful science in sport and more so when there is no extensive study in the sport of lacrosse.
Recurrent Convolutional Neural Network with Attention for Twitter and Yelp Sentiment Classification: ARC Model for Sentiment Classification

This paper proposes a combination structure of Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) for sentiment classification. Our model has two distinct characteristics: (1) Owing to the characteristics of the combination structure, our model can extract n_gram features with bidirectional time series. (2) Our model utilizes the attention mechanism to effectively capture important emotional content. Experiments conducted on two public datasets twitter and yelp. We evaluate the two aspects of this paper and shows that our model has a significant performance.
A Novel Binary Neural Network with Enhanced Dense Connection

Binary Neural Networks (BNNs), whose weights and activations can be represented by a single bit, are gaining more and more attention. Because their inference speed and memory consumption are far superior to full-precision neural networks. However, the previous work on BNNs often leads to severe accuracy degradation. To address this issue, we propose a novel BNN model named Binary Densely Connected Network (BDCN), which connects each mlpconv (a multilayer perceptron to extract better features) layer to all preceding mlpconv layers. Such enhanced dense connection can strengthen the representation capabilities of BNN, which is beneficial to improve the accuracy of the network. This work further narrows the accuracy gap between BNNs and their full-precision counterpart. We evaluate our BDCN on a range of visual datasets (MNIST, CIFAR-10 and ImageNet) and find that our method outperforms other state-of-art binary networks whilst keeping the model size acceptable.
Deep Learning Based Unsupervised POS Tagging for Sanskrit

In this paper, we present a deep learning based approach to assign POS tags to words in a piece of text given to it as input. We propose an unsupervised approach owing to the lack of a large Sanskrit annotated corpora and use the untagged Sanskrit Corpus prepared by JNU for our purpose. The only tagged corpora for Sanskrit is created by JNU which has 115,000 words which are not sufficient to apply supervised deep learning approaches. For the tag assignment purpose and determining model accuracy, we utilize this tagged corpus. We explore various methods through which each Sanskrit word can be represented as a point multi-dimensional vector space whose position accurately captures its meaning and semantic information associated with it. We also explore other data sources to improve performance and robustness of the vector representations. We use these rich vector representations and explore autoencoder based approaches for dimensionality reduction to compress these into encodings which are suitable for clustering in the vector space. We experiment with different dimensions of these compressed representations and present one which was found to offer the best clustering performance. For modelling the sequence in order to preserve the semantic information we feed these embeddings to a bidirectional LSTM autoencoder. We assign a POS tag to each of the obtained clusters and produce our result by testing the model on the tagged corpus.
Edgebase: A Cooperative Query Answering Database System With A Natural Language Interface

Traditional Database Management Systems (DBMS) require users to meticulously construct and submit queries to generate answers. The lack of query syntax flexibility in traditional database systems make results in simple and direct answers - queries retrieve precisely matched elements stated in the given Boolean query. In this paper, we propose a Cooperative Query Answering Database System (CDBS) that provide answers to user queries in the same manner as humans do, and not as machines. The method of "Cooperative Query Answering (CQA)", emanated from the perception that to provide adequate and "complete" answers to queries, recognition of users' intentions is vital. Most database systems require users to submit their queries using SQL syntax. In addition to presenting answers to queries in human-like manner, we present a cooperative approach to query submission. By this, we present an architecture that combines the rich features of html, Natural Language (NL) with Query-By-Form (QBF) method and MySQL to enable our proposed system accept user queries in plain English language. To authenticate our approach and proposed system, a set of thorough experiments were conducted on two database systems using mysqlslap benchmark and a comparative study with other methods is done.
Evaluation of Visualization Methods' Effect on Convolutional Neural Networks Research

In recent years, as an important research hotspot in the field of artificial intelligence and machine learning, the convolutional neural network has made substantial breakthroughs and has been widely used. In order to better explore and understand its structure, more and more researchers have shifted the focus of their research to the visualization of convolutional neural networks. They learned from the neural network what features were studied. They applied it to parameter adjustment and optimization in the convolutional neural networks and achieved good results. In this paper, the basic structure of convolutional neural network is described first. Secondly, some commonly used volume and neural network models are introduced. Finally, the convolutional neural network visualization technology is evaluated.
Enhancing Test Cases generated by Concolic Testing

Concolic testing tools can automatically generate test cases. However, there is a scope to increase the coverage achieved. Pair-wise testing is a prominent black-box testing method. We propose to combine ideas from these two techniques to enhance the code coverage. We have carried out experimental studies to evaluate the effectiveness of our approach. Our experimental results show that on an average our approach achieves 3.27%, 2.94%, and 6.22% improvements in condition coverage, decision coverage, and MC/DC respectively over a traditional concolic tester. Further, on an average of 4.36% improvement in mutation score is achieved.
A novel CNN-based method for Question Classification in Intelligent Question Answering

Sentence classification, which is the foundation of the subsequent text-based processing, plays an important role in the intelligent question answering (IQA). Convolutional neural networks (CNN) as a kind of common architecture of deep learning, has been widely used to the sentence classification and achieved excellent performance in open field. However, the class imbalance problems and fuzzy sentence feature problem are common in IQA. With the aim to get better performance in IQA, this paper proposes a simple and effective method by increasing generalization and the diversity of sentence features based on simple CNN. In proposed method, the professional entities could be replaced by placeholders to improve the performance of generalization. And CNN reads sentence vectors from both forward and reverse directions to increase the diversity of sentence features. The testing results show that our methods can achieve better performance than many other complex CNN models. In addition, we apply our method in practice of IQA, and the results show the method is effective.
Research on Robustness of Voiceprint Recognition Technology

In the mobile Internet era, voiceprint recognition technology is an important means of identity authentication in social, shopping, online shop, financial transactions and other activities. With the popularity of smart phones, these network activities can be carried out at anytime and anywhere. Inevitably, there are a variety of environmental noise in the voice input process of voiceprint recognition. Improving the anti-noise and robustness of voiceprint recognition system is the key to the wide application of voiceprint recognition technology. This paper focuses on the key modules of voiceprint recognition system, such as endpoint detection, feature extraction, voiceprint modeling and matching. Mainstream technologies of each module are analyzed, and a robust voiceprint recognition method is proposed. In this method, the endpoint detection algorithm based on spectrum is used, and the Empirical Mode Decomposition (EMD Empirical Mode Decomposition) algorithm is used to reconstruct the spectrum, which can effectively realize voiceprint denoising. Using the superior performance of convolution neural network (CNN) model in the field of image, the voiceprint model is constructed to improve the recognition efficiency and realize the robustness of the voiceprint recognition system.
Production of Naturally γ-Aminobutyric Acid-Enriched Cheese Using the Dairy Strains Streptococcus thermophilus 84C and Lactobacillus brevis DSM 32386

The cheese-derived strains Streptococcus thermophilus 84C isolated from Nostrano cheese, and Lactobacillus brevis DSM 32386 isolated from Traditional Mountain Malga cheese have been previously reported as γ-aminobutyric acid (GABA)-producers in vitro. In the present study, the ability of these strains to produce GABA was studied in experimental raw milk cheeses, with the aim to investigate the effect of the culture and the ripening time on the GABA concentration. The cultures used consisted on S. thermophilus 84C alone (84C) or in combination with L. brevis DSM 32386 (84C-DSM). The control culture was a commercial S. thermophilus strain, which was tested alone (CTRL) or in combination with the L. brevis DSM 32386 (CTRL-DSM). The pH evolution, microbiological counts, MiSeq Illumina and UHPLC-HQOMS analysis on milk and cheese samples were performed after 2, 9, and 20 days ripening. During the whole ripening, the pH was always under 5.5 in all batches. The concentration of GABA increased during ripening, with the highest content in 84C after 9 days ripening (84 ± 37 mg/kg), in 84C-DSM and CTRL-DSM after 20 days ripening (91 ± 28 and 88 ± 24 mg/kg, respectively). The data obtained support the hypothesis that S. thermophilus 84C and L. brevis DSM 32386 could be exploited as functional cultures, improving the in situ bio-synthesis of GABA during cheese ripening.
Chinese Name Entity Recognition Using Highway-LSTM-CRF

We have implemented a highway-LSTM-CRF(Long Short-Term Memory, LSTM for short; Conditional Random Field, CRF for short) model for Chinese NER(Named entity recognition, NER for short), which encodes a sequence of input characters as well as all potential words that match a lexicon. Through the highway layer, our model can intelligently select words that are more relevant to the current character. In this way, an effect similar to the attention mechanism is achieved. Our model uses word and word sequence information without being affected by word segmentation errors. Experiments on various datasets demonstrate the effectiveness of leveraging lexicon knowledge and the efficiency of our model.
Envy-Freeness Up to Any Item with High Nash Welfare: The Virtue of Donating Items

Several fairness concepts have been proposed recently in attempts to approximate envy-freeness in settings with indivisible goods. Among them, the concept of envy-freeness up to any item (EFX) is arguably the closest to envy-freeness. Unfortunately, EFX allocations are not known to exist except in a few special cases. We make significant progress in this direction. We show that for every instance with additive valuations, there is an EFX allocation of a subset of items with a Nash welfare that is at least half of the maximum possible Nash welfare for the original set of items. That is, after donating some items to a charity, one can distribute the remaining items in a fair way with high efficiency. This bound is proved to be best possible. Our proof is constructive and highlights the importance of maximum Nash welfare allocation. Starting with such an allocation, our algorithm decides which items to donate and redistributes the initial bundles to the agents, eventually obtaining an allocation with the claimed efficiency guarantee. The application of our algorithm to large markets, where the valuations of an agent for every item is relatively small, yields EFX with almost optimal Nash welfare. We also show that our algorithm can be modified to compute, in polynomial-time, EFX allocations that approximate optimal Nash welfare within a factor of at most 2ρ, using a ρ-approximate allocation on input instead of the maximum Nash welfare one.
Antimicrobials Are a Photodynamic Inactivation Adjuvant for the Eradication of Extensively Drug-Resistant Acinetobacter baumannii

The worldwide emergence of extensively drug resistant (XDR) Acinetobacter baumannii has reduced the number of antimicrobials that exert high bactericidal activity against this pathogen. This is the reason why many scientists are focusing on investigations concerning novel non-antibiotic strategies such as antimicrobial photodynamic inactivation (aPDI) or the use of antimicrobial blue light (aBL). Therefore, the aim of the current study was to screen for antimicrobial synergies of routinely used antibiotics and phototherapies, including both aPDI involving exogenously administered photosensitizing molecules, namely, rose bengal, and aBL, involving excitation of endogenously produced photoactive compounds. The synergy testing was performed in accordance with antimicrobial susceptibility testing (AST) standards, including various methodological approaches, i.e., antibiotic diffusion tests, checkerboard assays, CFU counting and the evaluation of postantibiotic effects (PAEs). We report that combining antimicrobials and aPDI/aBL treatment led to a new strategy that overcomes drug resistance in XDR A. baumannii, rendering this pathogen susceptible to various categories of antibiotics. Sublethal aPDI/aBL treatment in the presence of sub-MIC levels of antimicrobials effectively killed A. baumannii expressing drug resistance to studied antibiotics when treated with only antibiotic therapy. The susceptibility of XDR A. baumannii to a range of antibiotics was enhanced following sublethal aPDI/aBL. Furthermore, 3′-(p-aminophenyl) fluorescein (APF) testing indicated that significantly increased reactive oxygen species production upon combined treatment could explain the observed synergistic activity. This result represents a conclusive example of the synergistic activity between photodynamic inactivation and clinically used antimicrobials leading to effective eradication of XDR A. baumannii isolates and indicates a potent novel therapeutic approach.
Wide & ResNet: An Improved Network for CTR Prediction

The model Wide & Deep is widely applied for CTR prediction in recommender systems, which unites linear model for memorization and deep neural network for generalization. However, the essence of Wide & Deep still lies in feature engineering, especially in the processing of categorical features, which usually requires manually crafted feature engineering to generate all types of cross feature. Some explicit and easy-to-understand feature interactions can be extracted manually, but more feature interactions are hidden. In this paper, we propose an improved network structure for CTR Prediction, Wide & ResNet (WRN), which still keeps the linear model Logistic Regression but introduce the idea of residual network in DNN component. The advantage of doing this is that not only enhance features reuse but also learn the interactions between the low-degree features of shallow layers and the highly nonlinearity features of deep layers, so that more hidden feature interactions can be mined. Experimental results on two real-world dataset: Criteo and Frappe dataset, show that Wide & ResNet significantly outperforms the Wide & Deep model.