Miracle-dz/UniFAR-FTUs huggingface dataset:
Characters Detection on Namecard with faster RCNN

We apply Faster R-CNN to the detection of characters in namecard, in order to solve the problem of a small amount of data and the inbalance between different class, we designed the data augmentation and the 'fake' data generalizer to generate more data for the training of network. Without using data augmentation, the average IoU in correct samples could be no less than 80% and the mAP result of 80% was also achieved with Faster R-CNN. By applying the data augmentation, the variance of mAP is decreased and both of the IoU and mAP score has increased a little.
Photoluminescence Rainbow from Coelenteramide—A Theoretical Study

A wide variety of marine bioluminescent organisms emit light via the excited‐state coelenteramide, which is produced from the coelenterazine oxidation via a series of complicated chemical reactions in protein. Photoluminescence of coelenteramide is a simple way to produce light without experiencing the intricate reactions starting from coelenterazine. To extend the color range of light emission, many coelenterazine analogues were synthesized, but mostly only produce blue and cyan fluorescence. Based on the 42 synthesized coelenterazine analogues, we theoretically studied the absorption and fluorescence properties of the corresponding coelenteramide analogues. The electronic effect, steric effect, conjugated effect and solvated effect were considered. The results indicated that conjugated effect has great influence on the strength and wavelength of fluorescence and large electron transfer is beneficial to redshift. Based on the regularities, we theoretically designed six coelenteramide analogues, and together with the original coelenteramide, the seven‐ones emit the seven colors of rainbow via their photoluminescences. This study expands the coelenteramide fluorescence to the whole visible light region and could inspire new application.
Activity Recognition Based on Kinetic Energy Harvester and Accelerometer

Harvesting energy from human motion has created an opportunity for battery-free wearable devices to realize long-term human activity monitoring. By simply using the collected motion energy or acceleration is insufficient to distinguish some similar activities very well, it motivates us to exploit the combination of the harvesting kinetic energy and acceleration for activity recognition. An activity recognition approach SRC-EA is proposed, which is based on sparse representation classification to utilize multimodal information. To validate the effectiveness of our approach, a wearable sensor node consists of a commercial energy harvester is developed to collect the harvesting energy and acceleration. We test our approach on a real world data set that covers 10 participants, 9 common activities, and 3 different on-body device positions. The experiment results show that our SRC-EA approach achieves an average accuracy of 92.64% for user-independent activity recognition. Furthermore, our method can not only save energy by reducing sampling rate of accelerometer, but also harvest energy from human motion while the recognition accuracy is maintained at the same level.
RecNet: A Convolutional Network for Efficient Radiomap Reconstruction

Wireless signals have been a strong indicator of nearby wireless environment, which can be used in a wide range of applications, including the construction and maintenance of wireless network in smart cities. However, wireless signals can change drastically due to moving pedestrians and automatic power adjustment of Access Points (APs), which renders previous radiomap inaccurate. To achieve sufficient accuracy, surveyors have to update the radiomap constantly, which incurs high maintenance cost in the long run. To address this, we propose RecNet, a neural network to reconstruct a fine-grained radiomap with a small number of new samples. The intuition lies in the visualization of numerical signal strength values by a heatmap. A high-resolution heatmap corresponds to a fine-grained radiomap while a low-resolution one corresponds to a coarse-grained radiomap. Then we reduce the radiomap reconstruction to the image super-resolution: generating a high-resolution image from a low-resolution one. Based on the above, we design and implement the RecNet based on the image super-resolution neural network. Extensive experiments in two large test sites demonstrate that RecNet is able to reconstruct an accurate radiomap with only 50% of fingerprints, and reduces the signal error by more than 20% compared with a recent reconstruction algorithm.
Adaptive Rule Monitoring System

Rule-based techniques are gaining importance with their ability to augment large scale data processing systems. However, there still remain key challenges amongst current rule-based techniques, including rule monitoring, adapting and evaluation. Among these challenges, monitoring the precision of rules is highly important as it enables analysts to maintain the accuracy of a rule-based system. In this paper, we propose an Adaptive Rule Monitoring System (ARMS) for monitoring the precision of rules. The approach employs a combination of machine learning and crowdsourcing techniques. ARMS identifies rules deteriorating the performance of a rule based system, using the feedback receives from the crowd. To enable analysts identifying the imprecise rules, ARMS leverage machine learning algorithms to analyze the crowd's feedback. The evaluation results show that ARMS can identify the imprecise rules more successfully compared to the default practice of the system, which rely exclusively on analysts.
#AIDS Analyse Information Dangers Sexualité : caractériser les discours à propos du VIH dans les forums de santé

Contexte : Les forums de discussion consacres au VIH remplissent trois fonctions. Tout d'abord, ils sont utilises par les internautes comme sources d'information pour se renseigner sur les traitements, les risques d'infection lies au sida ou le « vivre avec » la maladie ou le virus. Ensuite, ils servent de media pour les institutions de sante ou les associations afin de communiquer des informations de prevention sur le VIH. Enfin, ils apparaissent comme sources de connaissances pour les professionnels de sante (medecins, soignants) pour comprendre les comportements lies au VIH, et pour les professionnels de la prevention, pour moderer les forums et adapter les campagnes de prevention aux differents publics. Objectif : Notre objectif dans cet article est de proposer un processus d'ingenierie des connaissances com-plet, permettant : 1) de collecter des messages dans des medias sociaux ; 2) de classifier ces messages de maniere semi-automatique selon le genre, le niveau d'expertise, le niveau informationnel, le type de risque ainsi que les emotions exprimees ; 3) de visualiser ces nouvelles connaissances dans une representation ori-ginale prenant en compte la temporalite et la hierarchie sous-jacente a la classification. Cette visualisation pourrait permettre aux gestionnaires de sites de forums et aux professionnels de sante de naviguer dans le flot de messages pour suivre l'evolution de l'importance de ces thematiques. Methodes : Notre approche combine une demarche qualitative et quantitative. Nous avons travaille sur plus de 226 252 messages issus du forum Sida Info Service. Deux chercheurs en sciences de l'information et de la communication ont elabore une grille d'analyse, puis ont annote 4 481 messages selon cette grille. Ces donnees ont ete utilisees pour apprendre des classifieurs qui ont permis d'etiqueter l'ensemble des messages du site a notre disposition. Afin de definir les meilleurs classifieurs, nous avons compare l'efficacite des me-thodes de classification traditionnelles statistiques et plusieurs architectures d'apprentissage profond. Une fois les messages etiquetes, nous avons utilise une visualisation de type streamgraph, combinee avec un outil de navigation hierarchique, pour visualiser l'evolution de ces annotations dans le temps. Resultats : Les resultats sont prometteurs et montrent l'efficacite des methodes d'apprentissage profond pour caracteriser les messages des forums de maniere automatique. La methode de visualisation mise en place permet d'explorer les resultats de ces methodes et ainsi faciliter l'acces aux connaissances.
Spatiotemporal variation in the pollination systems of a supergeneralist plant: is Angelica sylvestris (Apiaceae) locally adapted to its most effective pollinators?

Background and Aims In terms of pollination systems, umbellifers (plants of the carrot family, Apiaceae) are regarded as generalists, since their (usually dichogamous) flowers are visited by a wide range of insects representing several taxonomic orders. However, recent analyses of insect effectiveness revealed that these plants may be pollinated effectively by a narrow assemblage of insect visitors. Of particular interest was whether populations of an umbellifer species varied in pollinator assemblages and whether this could lead to local specialization of the pollination system. We also explored whether variation in pollinator assemblages was associated with variation in floral traits, and whether this variation influences reproductive output. Methods The focus was on Angelica sylvestris, a common European species visited by a taxonomically diverse insect assemblage. In three populations, located along an ~700-km transect, over three growth seasons insect visitors were identified, their effectiveness was assessed by surveying pollen loads present on the insect body, insect activity on umbels, nectar and scent composition was studied, and transplantation experiments were performed. Key Results The populations investigated in this study differed in their nectar and scent profiles and, despite the similar taxonomic composition of insect visitor assemblages, were effectively pollinated by disparate pollinator morphogroups, i.e. flies and beetles. Although this suggested local adaptations to the most effective pollinators, analyses of body pollen loads and behaviour on umbels demonstrated functional equivalency of the visitor morphogroups, which is probably related to the fact that A. sylvestris bears few ovules per flower. The transplantation experiments confirmed that reproductive success was not related to the source of experimental plants and that the insects do not exhibit preferences towards local genotypes. Conclusions Angelica sylvestris is morphologically well adapted to ecological generalization, and there is little evidence that the surveyed populations represent distinct pollination ecotypes. Most likely, the observed variation in floral characters can be interpreted as 'adaptive wandering'. Specialization in this family seems possible only under very special circumstances, for example when the pollinator community comprises insect visitor groups that clearly differ in their pollination capacity (e.g. due to differences in their functional morphology) and/or have different perceptional biases (e.g. for colour or scent). However, the barrier to the evolution of morphological adaptations resulting in the fine-tuning of the flower towards particular pollinator types may arise from the architectural constraints on the floral bauplan that make umbellifers so uniform in their floral displays and so successful in attracting large numbers of pollinators.
Single-camera pose estimation using mirage

Recently, mirage pose estimation method was proposed for multi-camera systems. Multi-camera mirage analytically solves a system of linear equations for six pose parameters in O(n) time. Mirage promises to execute in real time with high accuracy and shows lower rotational and translational errors compared to eight other well-known perspective-n-points (PnP) methods. However, the simulated tests and real experiments showed that, in case of a single camera, the analytical system of linear equations is not solvable due to the reduced rank of the linear system that is obtained by the formulation. In this study, an important revision to mirage is proposed to support single camera systems properly. The results of simulations and real experiments demonstrate smaller pose estimation errors compared to a group of eight well-known state-of-the-art PnP methods.
Joint Video Object Discovery and Segmentation by Coupled Dynamic Markov Networks

It is a challenging task to extract segmentation mask of a target from a single noisy video, which involves object discovery coupled with segmentation. To solve this challenge, we present a method to jointly discover and segment an object from a noisy video, where the target disappears intermittently throughout the video. Previous methods either only fulfill video object discovery, or video object segmentation presuming the existence of the object in each frame. We argue that jointly conducting the two tasks in a unified way will be beneficial. In other words, video object discovery and video object segmentation tasks can facilitate each other. To validate this hypothesis, we propose a principled probabilistic model, where two dynamic Markov networks are coupled–one for discovery and the other for segmentation. When conducting the Bayesian inference on this model using belief propagation, the bi-directional message passing reveals a clear collaboration between these two inference tasks. We validated our proposed method in five data sets. The first three video data sets, i.e., the SegTrack data set, the YouTube-objects data set, and the Davis data set, are not noisy, where all video frames contain the objects. The two noisy data sets, i.e., the XJTU-Stevens data set, and the Noisy-ViDiSeg data set, newly introduced in this paper, both have many frames that do not contain the objects. When compared with state of the art, it is shown that although our method produces inferior results on video data sets without noisy frames, we are able to obtain better results on video data sets with noisy frames.
Towards a Policy-Based Distributed Data-Sharing Economy

Large scale data sharing is important, especially now, with more open societies of components such as Smart Cities and the Smart Homes creating data sharing ecosystems. We propose that access to data and knowledge be controlled through fine-grained, user-specified explicitly represented policies. Fine-grained policies allow stakeholders to have a more precise level of control over who, when, and how their data is accessed. We propose a realisation of a policy language, and supporting socially aware mechanisms, to facilitate data sharing in a distributed environment.
Spindle: Informed Memory Access Monitoring

Memory monitoring is of critical use in understanding applications and evaluating systems. Due to the dynamic nature in programs’ memory accesses, common practice today leaves large amounts of address examination and data recording at runtime, at the cost of substantial performance overhead (and large storage time/space consumption if memory traces are collected). Recognizing the memory access patterns available at compile time and redundancy in runtime checks, we pro-pose a novel memory access monitoring and analysis framework, Spindle. Unlike methods delaying all checks to runtime or performing task-speciﬁc optimization at compile time, Spindle performs common static analysis to identify predictable memory access patterns into a compact program structure summary. Custom memory monitoring tools can then be developed on top of Spindle, leveraging the structural information extracted to dramatically reduce the amount of instrumentation that incurs heavy runtime memory address examination or recording. We implement Spindle in the popular LLVM compiler, supporting both single-thread and multi-threaded programs. Our evaluation demonstrated the effectiveness of two Spindle-based tools, performing memory bug detection and trace collection respectively, with a variety of programs. Results show that these tools are able to aggressively prune online memory monitoring processing, fulﬁlling desired tasks with performance overhead signiﬁcantly reduced (2 . 54 × on average for memory bug detection and over 200 × on average for access tracing, over state-of-the-art solutions).
Context collapse and privacy management: Diversity in Facebook friends increases online news reading and sharing

Social media users are able to read, share, and discuss news online with other people coming from diverse contexts in their lives, including family members, co-workers, and friends. Past research has indicated that “context collapse” occurs when people must imagine and negotiate interacting with a large and diverse online audience. Using survey data from 771 US Internet users, we find that more context collapse in people’s Facebook friends is positively related to both sharing and reading news. Furthermore, reading news on Facebook mediates the relationship between context collapse and news sharing. Finally, privacy management moderates the relationship between reading and sharing news on Facebook, where people who are more open in their privacy management practices share more news.
Intelligent Healthcare Systems Assisted by Data Analytics and Mobile Computing

The advances in information technology have facilitated great progress in healthcare technologies in various domains. However, these new technologies have also made healthcare data not only much larger in size but also substantially more difficult to handle and process. Moreover, because the data are created from a variety of devices within a short time span, these data are stored in different formats and created quickly, which can to a large extent be regarded as a big data problem. This paper discusses how to develop intelligent patientcentric healthcare applications and services from the perspectives of mobile computing and big data analytics technologies. This healthcare system consists of a data collection layer with a unified standard, a data management layer for distributed storage and parallel computing, and a data-oriented service layer. Furthermore, various healthcare applications are discussed to show that mobile computing and big data technologies enhance the performance of the system toward improving a humans well-being.
Supervised Classification of Social Spammers using a Similarity-based Markov Random Field Approach

Social spam has been plaguing online social networks for years. Being the sites where online users spend most of their time, the battle to capitalize and monetize users' attention is actively fought by both spammers and legitimate sites operators. Social spam detection systems have been proposed as early as 2010. They commonly exploit users' content and behavioral characteristics to build supervised classifiers. Yet spam is an evolving concept, and developed supervised classifiers often become obsolete with the spam community continuously trying to evade detection. In this paper, we use similarity between users to correct evasion-induced errors in the predictions of spam filters. Specifically, we link similar accounts based on their shared applications and build a Markov Random Field model on top of the resulting similarity graph. We use this graphical model in conjunction with traditional supervised classifiers and test the proposed model on a dataset that we recently collected from Twitter. Results show that the proposed model improves the accuracy of classical classifiers by increasing both the precision and the recall of state-of-the-art systems.
Modeling SSD RAID reliability under general settings

Solid-state drives (SSDs) are susceptible to the limited number of program/erase (P/E) cycles and uncorrectable flash errors, and hence achieving high reliability of SSD storage systems is a critical issue. RAID provides a viable option for enhancing system reliability by distributing redundancy across a number of SSDs. However, the flash error rate of an SSD increases with the number of P/E cycles, and this time-varying nature complicates the reliability analysis of SSD RAID. In addition, there remains very limited formal analysis that quantifies the reliability dynamics of an SSD RAID array under general settings. To this end, we propose a new continuous time Markov chain (CTMC) model to characterize the reliability dynamics of SSD RAID over time under two general settings: (1) fault tolerance against a general number of device failures and (2) non-uniform workload. We validate the correctness of our CTMC model via trace-driven simulations. Based on our model, we further analyze the impact of different RAID parameters on the reliability dynamics of an SSD RAID array.
Women, Motherhood and Work

The present study attempts to make an interdisciplinary approach to issues of associating motherhood and family with work and employment. Thus, in the perspective of discussing the specific theoretical construct in the context of social sciences and through the interplay of History, Literature, Sociology, Politics and Economics, the study examines women’s status, which, in the history of Western thought, is defined, in social, economic, political and cultural terms, within the framework of a production reproduction system. Remarkably, the crucial issue of ideology or, as Beauvoir posits, of the religion of motherhood, in historic, social and economic terms, has always been associated with a policy of priorities concerning birthrate and family, which are considered fundamental factors for the development of the social fabric. It is demonstrated that the relationship of childbearing and childrearing, work and motherhood has been a recurring issue not only in the social scene, but also within feminist movements in Europe and America, depending on historical, political and economic conditions. This is a common economic, political and cultural context in social history, impregnated with patriarchal principles, and generating the glass ceiling effect, namely, barriers and disparities impeding women to rise to the upper rungs of the corporate ladder. Thus, in the name of deified motherhood, women have constantly been deprived of the possibility of pursuing career advancement or intellectual achievement on account of being forced to bear children. The specific premise has been variously reflected in literature since the time of Rousseau, Darwin and Freud, demonstrating that, in interrelation to work and employment, spirituality and idealization, motherhood ideology has still remained, in social and scientific terms, an open question.
Ge FinFET CMOS Inverters with Improved Channel Surface Roughness by Using In-situ ALD Digital O3 Treatment

Improved electrical characteristics of CMOS inverters composed of Ge n- and p-finFETs were demonstrated by utilizing newly introduced Ge surface treatments. In-situ ALD digital O3 treatment was adopted on the surface of Ge fin sidewall in order to reduce the roughness and etching damages through the GeO desorption mechanism. By combining this treatment with optimized microwave annealing (MWA), SS and the ION/IOFF ratio were remarkably improved in both n-finFET and p-finFET, and Ge CMOS inverters with high voltage gain of 50.3 V/V at low V}_{D}}=0.6\ V} was realized.