Miracle-dz/UniFAR-FTUs huggingface dataset:
MM: A new Framework for Multidimensional Evaluation of Search Engines

In this paper, we proposed a framework to evaluate information retrieval systems in presence of multidimensional relevance. This is an important problem in tasks such as consumer health search, where the understandability and trustworthiness of information greatly influence people's decisions based on the search engine results, but common topicality-only evaluation measures ignore these aspects. We used synthetic and real data to compare our proposed framework, named MM, to the understandability-biased information evaluation (UBIRE), an existing framework used in the context of consumer health search. We showed how the proposed approach diverges from the UBIRE framework, and how MM can be used to better understand the trade-offs between topical relevance and the other relevance dimensions.
Variational Recurrent Model for Session-based Recommendation

Session-based recommendation performance has been significantly improved by Recurrent Neural Networks (RNN). However, existing RNN-based models do not expose the global knowledge of frequent click patterns or consider variability of sequential behaviors in sessions. In this paper, we propose a novel Variational Recurrent Model (VRM), which employs the stochastic latent variable to capture the knowledge of frequent click patterns and impose variability for the sequential behavior modeling. A stochastic generative process of session sequence is specified, where the latent variable modulates the generation of session sequences in RNN. We further extend VRM to a Conditional Variational Recurrent Model (CVRM) by considering additional information (e.g., focused category in sessions) as the generative condition. When evaluated on a public benchmark dataset, VRM and its extension clearly demonstrate their superiority over popular baselines and state-of-the-art models.
Dense Auto-Encoder Hashing for Robust Cross-Modality Retrieval

Cross-modality retrieval has been widely studied, which aims to search images as response to text queries or vice versa. When faced with large-scale dataset, cross-modality hashing serves as an efficient and effective solution, which learns binary codes to approximate the cross-modality similarity in the Hamming space. Most recent cross-modality hashing schemes focus on learning the hash functions from data instances with fully modalities. However, how to learn robust binary codes when facing incomplete modality (i.e., with one modality missed or partially observed), is left unexploited, which however widely occurs in real-world applications. In this paper, we propose a novel cross-modality hashing, termed Dense Auto-encoder Hashing (DAH), which can explicitly impute the missed modality and produce robust binary codes by leveraging the relatedness among different modalities. To that effect, we propose a novel Dense Auto-encoder Network (DAN) to impute the missing modalities, which densely connects each layer to every other layer in a feed-forward fashion. For each layer, a noisy auto-encoder block is designed to calculate the residue between the current prediction and original data. Finally, a hash-layer is added to the end of DAN, which serves as a special binary encoder model to deal with the incomplete modality input. Quantitative experiments on three cross-modality visual search benchmarks, i.e., the Wiki, NUS-WIDE, and FLICKR-25K, have shown that the proposed DAH has superior performance over the state-of-the-art approaches.
Meet AR-bot: Meeting Anywhere, Anytime with Movable Spatial AR Robot

Many kinds of preparations are needed when meeting. For example, projector, laptop, cables and ETC. As such, this video have constructed Meet AR-bot, which helps users to keep meeting going smoothly, based on the projection of Augmented Reality(AR). Our system can easily provide meeting room environment through the movable setting via wheel-based stand. Users do not need to carry a personal laptop and connect them to the projector. Robot reconstructs the 3D geometry information through pan-tilt system and compute projection areas to project information in the space. Users can also control through mobile devices. We offer presentation, table interaction, file sharing and virtual object registration by mobile device.
PARL: Let Strangers Speak Out What You Like

Review-based methods are one of the dominant methods to address the data sparsity problem of recommender system. However, the performance of most existing review-based methods will degrade when the review is also sparse. To this end, we propose a method to exploit user-item p air-dependent features from a uxiliary r eviews written by l ike-minded users (PARL) to address such problem. That is, both the reviews written by the user and the reviews written for the item are incorporated to highlight the useful features covered by the auxiliary reviews. PARL not only alleviates the sparsity problem of reviews but also produce extra informative features to further improve the accuracy of rating prediction. More importantly, it is designed as a plug-and-play model which can be plugged into various deep recommender systems to improve recommendations provided by them. Extensive experiments on five real-world datasets show that PARL achieves better prediction accuracy than other state-of-the-art alternatives. Also, with the exploitation of auxiliary reviews, the performance of PARL is robust on datasets with different characteristics.
PDGFRα expression as a novel therapeutic marker in well-differentiated neuroendocrine tumors

ABSTRACT Aims: To evaluate the biological significance of dense vascular networks associated with low-grade NENs, we assessed the impact of PDGFRα tissue expression in 77 GEP/NEN patients, associating PDGFRα expression with the morphological characterization in low-grade tumors. Methods and results: Paraffin-embedded specimens of 77 GEP- NEN tissues, collected from January 2006 to March 2018, were evaluated for PDGFRα tissue expression and correlations with clinicopathological characteristics. PDGFRα tissue expression was significantly correlated with grade and the NEN growth pattern (p < 0.001) but not with gender, primary site or lymph nodes metastatic status. PDGFRα staining was mainly localized in the vascular pole of the neuroendocrine cells and in Enterochromaffin (EC) cells. In particular PDGFRα tissue expression was significantly more expressed in G2 (p < 0.001) than G1 and G3 cases (p 0.004; p < 0.0002;) and correlated with an insular growth pattern. PDGFRα tissue expression was associated with the Ki67 index and we found a significant negative trend of association with the Ki67 proliferation index (P < 0.001): thus PDGFRα expression is referred to morphological and not to proliferative data. Conclusions: PDGFRα represents an effective target for new anti-angiogenic treatment in WD- GEP-NENs, in particular in G2 cases, and in G3 cases only when there is a mixed insular-acinar pattern. In this context, it is important to carefully delineate those tumors that might better respond to this type of treatment alone or in combination. Further investigation of the relationship between PD-L1 and PDGFRa is warranted, and may contribute to optimize the therapeutic approach in patients with GEP-NENs.
Metabolic Determinants of Enzyme Evolution in a Genome-Scale Bacterial Metabolic Network

Abstract Different genes and proteins evolve at very different rates. To identify the factors that explain these differences is an important aspect of research in molecular evolution. One such factor is the role a protein plays in a large molecular network. Here, we analyze the evolutionary rates of enzyme-coding genes in the genome-scale metabolic network of Escherichia coli to find the evolutionary constraints imposed by the structure and function of this complex metabolic system. Central and highly connected enzymes appear to evolve more slowly than less connected enzymes, but we find that they do so as a by-product of their high abundance, and not because of their position in the metabolic network. In contrast, enzymes catalyzing reactions with high metabolic flux—high substrate to product conversion rates—evolve slowly even after we account for their abundance. Moreover, enzymes catalyzing reactions that are difficult to by-pass through alternative pathways, such that they are essential in many different genetic backgrounds, also evolve more slowly. Our analyses show that an enzyme’s role in the function of a metabolic network affects its evolution more than its place in the network’s structure. They highlight the value of a system-level perspective for studies of molecular evolution.
Beauty Product Image Retrieval Based on Multi-Feature Fusion and Feature Aggregation

We propose a beauty product image retrieval method based on multi-feature fusion and feature aggregation. The key idea is representing the image with the feature vector obtained by multi-feature fusion and feature aggregation. VGG16 and ResNet50 are chosen to extract image features, and Crow is adopted to perform deep feature aggregation. Benefited from the idea of transfer learning, we fine turn VGG16 on the Perfect-500K data set to improve the performance of image retrieval. The proposed method won the third price in Perfect Corp. Challenge 2018 with the best result 0.270676 mAP. We released our code on GitHub: https://github.com/wangqi12332155/ACMMM-beauty-AI-challenge.
Tunable Coupled-Resonator-Induced Transparency in a Photonic Crystal System Based on a Multilayer-Insulator Graphene Stack

We achieve the effective modulation of coupled-resonator-induced transparency (CRIT) in a photonic crystal system which consists of photonic crystal waveguide (PCW), defect cavities, and a multilayer graphene-insulator stack (MGIS). Simulation results show that the wavelength of transparency window can be effectively tuned through varying the chemical potential of graphene in MGIS. The peak value of the CRIT effect is closely related to the structural parameters of our proposed system. Tunable Multipeak CRIT is also realized in the four-resonator-coupled photonic crystal system by modulating the chemical potentials of MGISs in different cavity units. This system paves a novel way toward multichannel-selective filters, optical sensors, and nonlinear devices.
Underwater Target Detection and 3D Reconstruction System Based on Binocular Vision

To better solve the problem of target detection in marine environment and to deal with the difficulty of 3D reconstruction of underwater target, a binocular vision-based underwater target detection and 3D reconstruction system is proposed in this paper. Two optical sensors are used as the vision of the system. Firstly, denoising and color restoration are performed on the image sequence acquired by the vision of the system and the underwater target is segmented and extracted according to the image saliency using the super-pixel segmentation method. Secondly, aiming to reduce mismatch, we improve the semi-global stereo matching method by strictly constraining the matching in the valid target area and then optimizing the basic disparity map within each super-pixel area using the least squares fitting interpolation method. Finally, based on the optimized disparity map, triangulation principle is used to calculate the three-dimensional data of the target and the 3D structure and color information of the target can be given by MeshLab. The experimental results show that for a specific size underwater target, the system can achieve higher measurement accuracy and better 3D reconstruction effect within a suitable distance.
TrajectMe: Planning sightseeing tours with Hotel Selection from Trajectory Data

In this article, we propose TrajectMe, an algorithm that solves the orienteering problem with hotel selection in several cities, taking advantage of the tourists' trajectories extracted from location-based services. This method is an extension of the state-of-the-art memetic-based algorithm. To this end, we collect data from Foursquare and Flickr location-based services, reconstruct the trajectories of tourists. Next, we build a hotel graph model (HGM) using a set of trajectories and a set of hotels to infer typical sequences of hotels and point of interest (PoI). The HGM is applied in the initialization phase and in the genetic operations of the memetic algorithm to provide good sequences of hotels, whereas the associated sequence of PoIs are improved by applying local search moves. We evaluate our proposal using a large and real dataset from three Italian cities using up to 1000 hotels. The results show that our approach is effective and outperforms the state-of-the-art when using large real datasets. Our approach is better than the baseline algorithm by up to 208% concerning the solution score and proved to be more profitable toward PoI visiting time, being 54% better than state-of-the-art.
Extracting Interpersonal Stance from Vocal Signals

The role of emotions and other affective states within Human-Computer Interaction (HCI) is gaining importance. Introducing affect into computer applications typically makes these systems more efficient, effective and enjoyable. This paper presents a model that is able to extract interpersonal stance from vocal signals. To achieve this, a dataset of 3840 sentences spoken by 20 semi-professional actors was built and was used to train and test a model based on Support Vector Machines (SVM). An analysis of the results indicates that there is much variation in the way people express interpersonal stance, which makes it difficult to build a generic model. Instead, the model shows good performance on the individual level (with accuracy above 80%). The implications of these findings for HCI systems are discussed.
Multi-parameter mechanism design and sequential posted pricing

We study the classic mathematical economics problem of Bayesian optimal mechanism design where a principal aims to optimize expected revenue when allocating resources to self-interested agents with preferences drawn from a known distribution. In single parameter settings (i.e., where each agent's preference is given by a single private value for being served and zero for not being served) this problem is solved [20]. Unfortunately, these single parameter optimal mechanisms are impractical and rarely employed [1], and furthermore the underlying economic theory fails to generalize to the important, relevant, and unsolved multi-dimensional setting (i.e., where each agent's preference is given by multiple values for each of the multiple services available) [25]. In contrast to the theory of optimal mechanisms we develop a theory of sequential posted price mechanisms, where agents in sequence are offered take-it-or-leave-it prices. We prove that these mechanisms are approximately optimal in single-dimensional settings. These posted-price mechanisms avoid many of the properties of optimal mechanisms that make the latter impractical. Furthermore, these mechanisms generalize naturally to multi-dimensional settings where they give the first known approximations to the elusive optimal multi-dimensional mechanism design problem. In particular, we solve multi-dimensional multi-unit auction problems and generalizations to matroid feasibility constraints. The constant approximations we obtain range from 1.5 to 8. For all but one case, our posted price sequences can be computed in polynomial time. This work can be viewed as an extension and improvement of the single-agent algorithmic pricing work of [9] to the setting of multiple agents where the designer has combinatorial feasibility constraints on which agents can simultaneously obtain each service.
Recommending Serendipitous Items using Transfer Learning

Most recommender algorithms are designed to suggest relevant items, but suggesting these items does not always result in user satisfaction. Therefore, the efforts in recommender systems recently shifted towards serendipity, but generating serendipitous recommendations is difficult due to the lack of training data. To the best of our knowledge, there are many large datasets containing relevance scores (relevance oriented) and only one publicly available dataset containing a relatively small number of serendipity scores (serendipity oriented). This limits the learning capabilities of serendipity oriented algorithms. Therefore, in the absence of any known deep learning algorithms for recommending serendipitous items and the lack of large serendipity oriented datasets, we introduce SerRec our novel transfer learning method to recommend serendipitous items. SerRec uses transfer learning to firstly train a deep neural network for relevance scores using a large dataset and then tunes it for serendipity scores using a smaller dataset. Our method shows benefits of transfer learning for recommending serendipitous items as well as performance gains over the state-of-the-art serendipity oriented algorithms
Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection

Community structure is ubiquitous in real-world complex networks. The task of community detection over these networks is of paramount importance in a variety of applications. Recently, nonnegative matrix factorization (NMF) has been widely adopted for community detection due to its great interpretability and its natural fitness for capturing the community membership of nodes. However, the existing NMF-based community detection approaches are shallow methods. They learn the community assignment by mapping the original network to the community membership space directly. Considering the complicated and diversified topology structures of real-world networks, it is highly possible that the mapping between the original network and the community membership space contains rather complex hierarchical information, which cannot be interpreted by classic shallow NMF-based approaches. Inspired by the unique feature representation learning capability of deep autoencoder, we propose a novel model, named Deep Autoencoder-like NMF (DANMF), for community detection. Similar to deep autoencoder, DANMF consists of an encoder component and a decoder component. This architecture empowers DANMF to learn the hierarchical mappings between the original network and the final community assignment with implicit low-to-high level hidden attributes of the original network learnt in the intermediate layers. Thus, DANMF should be better suited to the community detection task. Extensive experiments on benchmark datasets demonstrate that DANMF can achieve better performance than the state-of-the-art NMF-based community detection approaches.
Cross-modal Moment Localization in Videos

In this paper, we address the temporal moment localization issue, namely, localizing a video moment described by a natural language query in an untrimmed video. This is a general yet challenging vision-language task since it requires not only the localization of moments, but also the multimodal comprehension of textual-temporal information (e.g., "first" and "leaving") that helps to distinguish the desired moment from the others, especially those with the similar visual content. While existing studies treat a given language query as a single unit, we propose to decompose it into two components: the relevant cue related to the desired moment localization and the irrelevant one meaningless to the localization. This allows us to flexibly adapt to arbitrary queries in an end-to-end framework. In our proposed model, a language-temporal attention network is utilized to learn the word attention based on the temporal context information in the video. Therefore, our model can automatically select "what words to listen to" for localizing the desired moment. We evaluate the proposed model on two public benchmark datasets: DiDeMo and Charades-STA. The experimental results verify its superiority over several state-of-the-art methods.
Social Networks as Real-time Data Distribution Platforms for Smart Cities

Cities need to collect, store and process huge amounts of data in order to provide smarter services, such as mobility, traffic, and disaster management. Social networks, such as Twitter and Instagram, are participatory sensing systems where communities generate a large amount of data every day. Existing studies have been often focused on collecting and transforming social media data into insights that may help citizens, with no concern whether the dataset represents the phenomenon they want to understand. In this paper, we explore the adequacy of social networking services to be used as platforms for smart city applications. Here we propose a methodology composed of three steps and undertake a performance analysis with Twitter as a proof of concept. Our results show that in spite of well-known data delivery constraints of the Twitter Streaming API, the majority of tweets could be successfully captured with a delay that makes it adequate to real-time processing.
A Kullback-Leibler Divergence Based Recurrent Mixture Density Network for Acoustic Modeling in Emotional Statistical Parametric Speech Synthesis

This paper proposes a Kullback-Leibler divergence (KLD) based recurrent mixture density network (RMDN) approach for acoustic modeling in emotional statistical parametric speech synthesis (SPSS), which aims at improving model accuracy and emotion naturalness. First, to improve model accuracy, we propose to use RMDN as acoustic model, which combines an LSTM with a mixture density network (MDN). Adding mixture density layer allows us to do multimodal regression as well as to predict variances, thus modeling more accurate probability density functions of acoustic features. Second, we further introduce Kullback-Leibler divergence regularization in model training. Inspired by KLD's success in acoustic model adaptation, we aim to improve the emotion naturalness by maximizing the distances between the distributions of emotional speech and neutral speech. Objective and subjective evaluations show that the proposed approach improves the prediction accuracy of acoustic features and the naturalness of the synthesized emotional speech.
Multi-Emotion Category Improving Embedding for Sentiment Classification

Sentiment analysis and opinion mining are significant and valuable for subject information extraction from the text. Word embedding that can map the words to low-dimensional vector representations has been widely used in natural language processing tasks. But the word embedding based on context such as Word2Vec and GloVe is lack of capturing the sentiment information. Most of existing sentiment analysis methods incorporate sentiment polarity (positive and negative) to improve the sentiment embedding for sentiment tasks. Instead of making a new word embedding model, we introduce the multi-emotion category (MEC) model to improve the pre-trained word vectors which aims to move target word vectors closer to the words from both similar semantics and similar emotions. The MEC model can give eight-dimensional vector for one word in emotion space that can capture more sentiment information than the binary polarity labels. In addition, the obvious advantage of the MEC model is that it can be fit for any pre-trained word embedding. The experimental results on several Chinese and English data sets show that this new model can improve the conventional word embedding and some existing sentiment embedding for sentiment classification.
DFN: Dual Fusion Networks for Single Stage Face Detection

This1 paper proposes a novel feature fusion method of the double shortcuts and constitutes a real-time face detector named Dual Fusion Networks (DFN). Firstly, we fuse the skip connection in ResNet with the densely connection in DenseNet, forming a double shortcuts structure to exploit the potential of the network and obtain robust features. Secondly, we introduce a semi-focal loss to solve the imbalance between positive and negative samples to a certain extent. Finally, we evaluate DFN on several public face detection benchmarks and achieve state-of-the-art performance. For 640*640 input, DFN achieves a recall rate of 97.9% on FDDB, and precisions of 99.1%, 98.5% on AFW, PASCAL FACE, separately, and test for 14 FPS on a GTX 1070Ti.