Miracle-dz/UniFAR-FTUs huggingface dataset:
On Lifecycle of Interactive Web Apps in WeChat

WeChat is the largest mobile instant messaging service in China, where users can send messages to friends or post them over their walls (a.k.a. friend circle, or WeChat Moments). Interactive web apps are quite attractive for businesses, institutes, or individuals to promote products or events. In this paper, we analyze the diffusion statistics of interactive web apps in WeChat and conduct an empirical measurement study over a dataset with 54 million users and 20 thousand web apps crawled. We discover the lifecycle of interactive web apps varies drastically, which is largely dependent on the content, date, time upon the first release, and the social influence of viewers and senders. Meanwhile, we develop a model based on the matrix factorization method to extract latent features of interactive web apps and an app lifecycle model that characterizes how the features affect apps' lifecycle, achieving the mean absolute error (MAE) of 2.32 days in predicting app's lifecycle. Our results hold the promise of helping businesses to promote their marketing information dissemination through long-lived interactive web apps at the right timing and with the appropriate content.
Differences in the bone marrow histology between childhood myelodysplastic syndrome with multilineage dysplasia and refractory cytopenia of childhood without multilineage dysplasia

Refractory cytopenia of childhood (RCC) is subdivided into myelodysplastic syndrome with multilineage dysplasia (MDS‐MLD) and RCC without (w/o) multilineage dysplasia (RCC without MLD). Although RCC is a histomorphological distinct entity, the bone marrow (BM) histology of RCC is not yet characterised in relation to multilineage dysplasia. We investigated the BM histological features of RCC to clarify the characteristics of BM histology of MDS‐MLD in childhood compared to RCC without MLD.
A multi‐contrast MRI approach to thalamus segmentation

Thalamic alterations occur in many neurological disorders including Alzheimer's disease, Parkinson's disease and multiple sclerosis. Routine interventions to improve symptom severity in movement disorders, for example, often consist of surgery or deep brain stimulation to diencephalic nuclei. Therefore, accurate delineation of grey matter thalamic subregions is of the upmost clinical importance. MRI is highly appropriate for structural segmentation as it provides different views of the anatomy from a single scanning session. Though with several contrasts potentially available, it is also of increasing importance to develop new image segmentation techniques that can operate multi‐spectrally. We hereby propose a new segmentation method for use with multi‐modality data, which we evaluated for automated segmentation of major thalamic subnuclear groups using T1‐weighted, T2* ‐weighted and quantitative susceptibility mapping (QSM) information. The proposed method consists of four steps: Highly iterative image co‐registration, manual segmentation on the average training‐data template, supervised learning for pattern recognition, and a final convex optimisation step imposing further spatial constraints to refine the solution. This led to solutions in greater agreement with manual segmentation than the standard Morel atlas based approach. Furthermore, we show that the multi‐contrast approach boosts segmentation performances. We then investigated whether prior knowledge using the training‐template contours could further improve convex segmentation accuracy and robustness, which led to highly precise multi‐contrast segmentations in single subjects. This approach can be extended to most 3D imaging data types and any region of interest discernible in single scans or multi‐subject templates.
Bibliometric Analysis of Lean, Agile, and Leagile Supply Chains in Automobile Industry (1990 - 2017)

This article describes how organizations embrace various supply chain strategies aiming at effective and efficient performance outcomes for gaining competitive advantage. The authors conducted an extensive search for academic publications on lean, agile, and leagile (hybrid) supply chains in context with automobile industries, published since year 1990 in reliable repositories such as Google Scholar, Scopus and ResearchGate. None of these papers used bibliometric analysis on the topic. This paper systematically maps, publications on lean, agile, and legal strategies in automobile industry published during 1990–2017. A five-step process is followed, namely (i) defining appropriate search terms, (ii) initial search results, (iii) refinement of search results, (iv) initial data, statistics, and (v) data analysis; adopted for inclusion of relevant documents for publication and citation analysis. Selected documents include primary search term ‘automobile' along with associated secondary terms such as ‘lean', ‘agile', ‘lean and agile', and ‘leagile' as part of the title, abstract, or keywords. The analysis finds several documents on lean or agile strategies, but only one document exists on ‘leagile' paradigm. Maximum articles are contributed on engineering subjects followed by business, management and accounting and computer sciences. Large publication and high citation counts were observed for lean from United States and Chinese authors, whereas Indian authors contributed in agile studies. This article identifies areas of current research interests discussing crucial contributions by several authors' and provides potential directions for further research investigations in the field.
Message Passing Stein Variational Gradient Descent

Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation ability and particle efficiency compared to traditional variational inference and Markov Chain Monte Carlo methods. However, we observed that particles of SVGD tend to collapse to modes of the target distribution, and this particle degeneracy phenomenon becomes more severe with higher dimensions. Our theoretical analysis finds out that there exists a negative correlation between the dimensionality and the repulsive force of SVGD which should be blamed for this phenomenon. We propose Message Passing SVGD (MP-SVGD) to solve this problem. By leveraging the conditional independence structure of probabilistic graphical models (PGMs), MP-SVGD converts the original high-dimensional global inference problem into a set of local ones over the Markov blanket with lower dimensions. Experimental results show its advantages of preventing vanishing repulsive force in high-dimensional space over SVGD, and its particle efficiency and approximation flexibility over other inference methods on graphical models.
Textual Deconvolution Saliency (TDS) : a deep tool box for linguistic analysis

In this paper, we propose a new strategy, called Text Deconvolution Saliency (TDS), to visualize linguistic information detected by a CNN for text classification. We extend Deconvolution Networks to text in order to present a new perspective on text analysis to the linguistic community. We empirically demonstrated the efficiency of our Text Deconvolution Saliency on corpora from three different languages: English, French, and Latin. For every tested dataset, our Text Deconvolution Saliency automatically encodes complex linguistic patterns based on co-occurrences and possibly on grammatical and syntax analysis.
3D printing spatially varying color and translucency

We present an efficient and scalable pipeline for fabricating full-colored objects with spatially-varying translucency from practical and accessible input data via multi-material 3D printing. Observing that the costs associated with BSSRDF measurement and processing are high, the range of 3D printable BSSRDFs are severely limited, and that the human visual system relies only on simple high-level cues to perceive translucency, we propose a method based on reproducing perceptual translucency cues. The input to our pipeline is an RGBA signal defined on the surface of an object, making our approach accessible and practical for designers. We propose a framework for extending standard color management and profiling to combined color and translucency management using a gamut correspondence strategy we call opaque relative processing. We present an efficient streaming method to compute voxel-level material arrangements, achieving both realistic reproduction of measured translucent materials and artistic effects involving multiple fully or partially transparent geometries.
A Model Based Testing Approach for Java Bytecode Programs

In this paper, we propose an approach to detect non-conformance between a given Java Bytecode program and its specification in the context of unit testing. The main goal of the proposed work is to use the specification pre-state, i.e. the invariant and the precondition, to guide the generation of the test data and the post-state, i.e. invariant and post-condition, as test oracle. In other hand, the code structure makes it possible to know the execution paths of testing method that not be conform to its specification. However, the specification and the System Under Test are not at the same level of abstraction. In the sense, we propose to express the specification at the Bytecode level using static Bytecode instrumentation.
On deeply learning features for automatic person image re-identification

The automatic person re-identification (re-id) problem resides in matching an unknown person image to a database of previously labeled images of people. Among several issues to cope with this research field, person re-id has to deal with person appearance and environment variations. As such, discriminative features to represent a person identity must be robust regardless those variations. Comparison among two image features is commonly accomplished by distance metrics. Although features and distance metrics can be handcrafted or trainable, the latter type has demonstrated more potential to breakthroughs in achieving state-of-the-art performance over public data sets. A recent paradigm that allows to work with trainable features is deep learning, which aims at learning features directly from raw image data. Although deep learning has recently achieved significant improvements in person re-identification, found on some few recent works, there is still room for learning strategies, which can be exploited to increase the current state-of-the-art performance. In this work a novel deep learning strategy is proposed, called here as coarse-to-fine learning (CFL), as well as a novel type of feature, called convolutional covariance features (CCF), for person re-identification. CFL is based on the human learning process. The core of CFL is a framework conceived to perform a cascade network training, learning person image features from generic-to-specific concepts about a person. Each network is comprised of a convolutional neural network (CNN) and a deep belief network denoising autoenconder (DBN-DAE). The CNN is responsible to learn local features, while the DBN-DAE learns global features, robust to illumination changing, certain image deformations, horizontal mirroring and image blurring. After extracting the convolutional features via CFL, those ones are then wrapped in covariance matrices, composing the CCF. CCF and flat features were combined to improve the performance of person re-identification in comparison with component features. The performance of the proposed framework was assessed comparatively against 18 state-of-the-art methods by using public data sets (VIPeR, i-LIDS, CUHK01 and CUHK03), cumulative matching characteristic curves and top ranking references. After a thorough analysis, our proposed framework demonstrated a superior performance.
Strengthening the Blockchain-Based Internet of Value with Trust

In recent years, Blockchain has been expected to create a secure mechanism for exchanging not only for cryptocurrency but also for other types of assets without the need for a powerful and trusted third-party. This could enable a new era of the Internet usage called the Internet of Value (IoV) in which any types of assets such as intellectual and digital properties, equity and wealth can be digitized and transferred in an automated, secure, and convenient manner. In the IoV, Blockchain is used to guarantee security of transactions that the transactions are nearly impossible to be altered; thus it is impractical to retract once a transaction is confirmed. Therefore, to strengthen the IoV, before making any transactions it is crucial to evaluate trust between participants for reducing the risk of dealing with malicious peers. In this article, we clarify the concept of IoV and propose a trust-based IoV model including a system architecture, components and features. Then, we present a trust platform in the IoV considering two concepts, Experience and Reputation, originated from Social Networks for evaluating trust between two any peers in the IoV. The Experience and Reputation are characterized and calculated using mathematical models with analysis and simulation in the IoV environment. We believe this paper consolidates the understandings about IoV technologies and demonstrates how trust is evaluated and used to strengthen the IoV. It also opens important research directions on both IoV and trust in the future.
Towards Self-Perception in Augmented Virtuality: Hand Segmentation with Fully Convolutional Networks

In this work, we propose the use of deep learning techniques to segment items of interest from the local region to increase self-presence in Virtual Reality (VR) scenarios. Our goal is to segment hand images from the perspective of a user wearing a VR headset. We create the VR Hand Dataset, composed of more than 10 . 000 images, including variations of hand position, scenario, outﬁts, sleeve and people. We also describe the procedure followed to automatically generate groundtruth images and create synthetic images. Preliminary results look promising.
Field Trial Analysis of Socially Aware Robot Assistant

The Socially-Aware Robot Assistant (SARA) is an embodied conversational agent that works toward using detection of visual, vocal and verbal cues as an input to estimate the strength of its relationship (namely the level of rapport) with a user. SARA then answers to the user through similar visual, vocal and verbal behaviors with the goal of building and maintaining rapport with that user as we hypothesize that this will improve task performance and user satisfaction over time. In this paper, we report results of a field trial with a semi-automatic SARA system that took place in a large high-profile conference. Participants interacted with SARA during the whole conference, receiving recommendations about sessions to attend and/or people to meet. We analyzed these interactions to shed light on the dynamics of the rapport level between SARA and the conference attendees, and investigate how SARA's task performance would influence the evolution of rapport over time. Although we did not find evidence supporting our claim that the recommendations' outcomes would influence rapport dynamics, our findings emphasize the importance of interactional features plays in both rapport and SARA's task performance. We thus propose design guidelines for the next generation of socially aware personal assistants.
A Study of the Importance of External Knowledge in the Named Entity Recognition Task

In this work, we discuss the importance of external knowledge for performing Named Entity Recognition (NER). We present a novel modular framework that divides the knowledge into four categories according to the depth of knowledge they convey. Each category consists of a set of features automatically generated from different information sources, such as a knowledge-base, a list of names, or document-specific semantic annotations. Further, we show the effects on performance when incrementally adding deeper knowledge and discuss effectiveness/efficiency trade-offs.
A Homomorphic Network Coding Signature Scheme for Multiple Sources and its Application in IoT

As a method for increasing throughput and improving reliability of routing, network coding has been widely used in decentralized IoT systems. When files are shared in the system, network coding signature techniques can help authenticate whether a modified packet in files is injected or not. However, in an IoT system, there are often multiple source devices each of which has its own authentication key, where existing single-source network coding signature schemes cannot work. In this paper, we study the problem of designing secure network coding signatures in the network with multiple sources and propose the multisource homomorphic network coding signature. We also give construction and prove its security.
Methodology for Assessing Mesh-Based Contact Point Methods

Computation of contact points is a critical sub-component of physics-based animation. The success and correctness of simulation results are very sensitive to the quality of the contact points. Hence, quality plays a critical role when comparing methods, and this is highly relevant for simulating objects with sharp edges. The importance of contact point quality is largely overlooked and lacks rigor and as such may become a bottleneck in moving the research field forward. We establish a taxonomy of contact point generation methods and lay down an analysis of what normal contact quality implies. The analysis enables us to establish a novel methodology for assessing and studying quality for mesh-based shapes. The core idea is based on a test suite of three complex cases and a small portfolio of simple cases. We apply our methodology to eight local contact point generation methods and conclude that the selected local methods are unable to provide correct information in all cases. The immediate benefit of the proposed methodology is a foundation for others to evaluate and select the best local method for their specific application. In the longer perspective, the presented work suggests future research focusing on semi-local methods.
The Decision Model for the Optimal Configuration Management of Knowledge Employees in Enterprises

With the development of knowledge economy, the role of knowledge employees in enterprises has become larger and large and employees with rich knowledge have even been the tower of strength in the development of enterprises. Knowledge employees with strong autonomy and creativity have stronger self-awareness in daily work. Thus how to dispatch those knowledge employees and assign them to proper positions has been a problem that must be solved. To solve the problem of employee dispatch, this study proposed to optimize the configuration management of knowledge employees in enterprises using bacterial foraging algorithm, established the mathematical model based on the optimal configuration of knowledge employees, and made simulation experiments on the model. It was found that the method could effectively fulfill the optimal configuration of knowledge employees. It is a feasible method for employee dispatch in enterprises.
Peripheral Gene Therapeutic Rescue of an Olfactory Ciliopathy Restores Sensory Input, Axonal Pathfinding, and Odor-Guided Behavior

Cilia of olfactory sensory neurons (OSNs) are the primary site of odor binding; hence, their loss results in anosmia, a clinical manifestation of pleiotropic ciliopathies for which there are no curative therapies. We used OSN-specific Ift88 knock-out mice (Ift88osnKO) of both sexes to examine the mechanisms of ciliopathy-induced olfactory dysfunction and the potential for gene replacement to rescue odorant detection, restore olfactory circuitry, and restore odor-guided behaviors. Loss of OSN cilia in Ift88osnKO mice resulted in substantially reduced odor detection and odor-driven synaptic activity in the olfactory bulb (OB). Defects in OSN axon targeting to the OB were also observed in parallel with aberrant odor-guided behavior. Intranasal gene delivery of wild-type IFT88 to Ift88osnKO mice rescued OSN ciliation and peripheral olfactory function. Importantly, this recovery of sensory input in a limited number of mature OSNs was sufficient to restore axonal targeting in the OB of juvenile mice, and with delayed onset in adult mice. In addition, restoration of sensory input re-established course odor-guided behaviors. These findings highlight the spare capacity of the olfactory epithelium and the plasticity of primary synaptic input into the central olfactory system. The restoration of peripheral and central neuronal function supports the potential for treatment of ciliopathy-related anosmia using gene therapy. SIGNIFICANCE STATEMENT Ciliopathies, for which there are no curative therapies, are genetic disorders that alter cilia morphology and/or function in numerous tissue types, including the olfactory system, leading to sensory dysfunction. We show that in vivo intranasal gene delivery restores peripheral olfactory function in a ciliopathy mouse model, including axonal targeting in the juvenile and adult olfactory bulb. Gene therapy also demonstrated restoration of olfactory perception by rescuing odor-guided behaviors. Understanding the therapeutic window and viability for gene therapy to restore odor detection and perception may facilitate translation of therapies to ciliopathy patients with olfactory dysfunctions.
A Framework for Detecting and Countering Android UI Attacks via Inspection of IPC Traffic

Android represents an ever-increasing share of the worldwide smart device market. The platform's ubiquity and open nature make Android a prime target for malicious actors. Unfortunately, device fragmentation among manufacturers makes maintaining cyber security difficult, invoking the need for third party security software. We present a framework for detecting and countering deceptive user interface attacks on the Android platform via inspection and analysis of inter-process communication transactions in the operating system. We evaluate our proof of concept implementation on a known class of malware that exploits the Android display system, allowing a malicious application to control the screen and mimic any application launched by the user. We achieve 100% detection rate of this malicious behavior with no false alarms.
The Endochitinase of Clonostachysrosea Expression in Bacillus amyloliquefaciens Enhances the Botrytis cinerea Resistance of Tomato

To investigate whether the ech42 gene in Clonostachysrosea can improve the biocontrol efficacy of Bacillus amyloliquefaciens and its molecular mechanism. Compared to the wild type, the B. amyloliquefaciens transformed with the ech42 gene exhibited higher chitinase activity. The B. amyloliquefaciens-ech42 also showed significantly higher biocontrol efficiency compared to Botrytiscinerea when tomato plants were pre-treated with B. amyloliquefaciens-ech42. No significant difference in biocontrol efficiency was observed between the wild type and B.amyloliquefaciens-ech42 when tomato plants were first infected by Botrytiscinerea. In addition, the activity of the defense-related enzyme polyphenol oxidase, but not superoxide dismutase, was significantly higher in B. amyloliquefaciens-ech42 than in the wild type. The ech42 enhances the biocontrol efficiency of B.amyloliquefaciens by increasing the capacity of preventative/curative effects in plants, rather than by killing the pathogens.