Miracle-dz/UniFAR-FTUs huggingface dataset:
Participant Incentive Mechanism Toward Quality-Oriented Sensing

The ubiquity of ever-more-capable mobile devices, especially smartphones, brings forth participatory sensing to collect and interpret information. It can achieve unprecedented quantity of data. However, it is arduous to guarantee quality of data because everyone can contribute data without scrutinization. It is an important issue in quality-oriented participatory sensing. Our idea to address this issue is motivating participants to contribute accurate data for improving data quality directly. In this article, we propose a reputation-based incentive mechanism, RIM, to realize the idea. More specifically, we identify the participants who collect the accurate data and regard them as the reputable ones. Then, the reputable participants are granted a higher chance to obtain rewards so that other people will try to follow such users and become reputable as well. Namely, RIM can encourage and steer users to collect accurate data in the long term. We analyze our incentive mechanism by formalization and premise implications. For a feasibility study of participatory sensing and verification of the implications, we implement and deploy a participatory sensing application focusing on monitoring environmental noise in a specific location as a case study and conduct a simulation based on the case study to further evaluate the proposed incentive mechanism. The results from the case study and the simulation present that RIM can remarkably increase the quality of collected data in participatory sensing while corroborating our theoretical implications.
Biomass Accumulation and Cell Wall Structure of Rice Plants Overexpressing a Dirigent-Jacalin of Sugarcane (ShDJ) Under Varying Conditions of Water Availability

A sugarcane gene encoding a dirigent-jacalin, ShDJ, was induced under drought stress. To elucidate its biological function, we integrated a ShDJ-overexpression construction into the rice Nipponbare genome via Agrobacterium-mediated transformation. Two transgenic lines with a single copy gene in T0 were selected and evaluated in both the T1 and T4 generations. Transgenic lines had drastically improved survival rate under water deficit conditions, at rates close to 100%, while WT did not survive. Besides, transgenic lines had improved biomass production and higher tillering under water deficit conditions compared with WT plants. Reduced pectin and hemicellulose contents were observed in transgenic lines compared with wild-type plants under both well-watered and water deficit conditions, whereas cellulose content was unchanged in line #17 and reduced in line #29 under conditions of low water availability. Changes in lignin content under water deficit were only observed in line #17. However, improvements in saccharification were found in both transgenic lines along with changes in the expression of OsNTS1/2 and OsMYB58/63 secondary cell wall biosynthesis genes. ShDJ-overexpression up-regulated the expression of the OsbZIP23, OsGRAS23, OsP5CS, and OsLea3 genes in rice stems under well-watered conditions. Taken together, our data suggest that ShDJ has the potential for improving drought tolerance, plant biomass accumulation, and saccharification efficiency.
Extended 2D Volumetric Consensus Hippocampus Segmentation

Hippocampus segmentation plays a key role in diagnosing various brain disorders such as Alzheimer's disease, epilepsy, multiple sclerosis, cancer, depression and others. Nowadays, segmentation is still mainly performed manually by specialists. Segmentation done by experts is considered to be a gold-standard when evaluating automated methods, buts it is a time consuming and arduos task, requiring specialized personnel. In recent years, efforts have been made to achieve reliable automated segmentation. For years the best performing authomatic methods were multi atlas based with around 90\% Dice coefficient and very time consuming, but machine learning methods are recently rising with promising time and accuracy performance. A method for volumetric hippocampus segmentation is presented, based on the consensus of tri-planar U-Net inspired fully convolutional networks (FCNNs), with some modifications, including residual connections, VGG weight transfers, batch normalization and a patch extraction technique employing data from neighbor patches. A study on the impact of our modifications to the classical U-Net architecture was performed. Our method achieves cutting edge performance in our dataset, with around 96% volumetric Dice accuracy in our test data, and GPU execution time in the order of seconds per volume. Also, masks are shown to be similar to other recent state-of-the-art hippocampus segmentation methods.
Machine Learning Hyperparameter Fine Tuning Service on Dynamic Cloud Resource Allocation System - taking Heart Sounds as an Example

The hyperparameters tuning of machine learning has always been a difficult and time-consuming task in deep learning area. In many practical applications, the hyperparameter tuning directly affects the accuracy. Therefore, the tuning optimization of hyperparameters is an important topic. At present, hyperparameters can only be set manually based on experience, and use Violent Enumeration, Random Search or through Grid Search to try and error, lack of effective automatic search parameters. In this study, we proposed a machine learning hyperparameter fine tuning service on dynamic cloud resource allocation system, which leverages several mainstream hyperparameter tuning methods such as Hyperopt and Optunity. In the meanwhile, various tuning methods are measured and compared by example application in this work. Finally, we dedicated actual case - Heart Sounds, and then tested it. In order to verify that the system service can not only automate the task of tuning, but also break through the limitation of the number of adjustable parameters. Furthermore the proposed hyperparameter fine tune system makes optimization process more efficient.
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models

Expectation-Maximization (EM) is a prominent approach for parameter estimation of hidden (aka latent) variable models. Given the full batch of data, EM forms an upper-bound of the negative log-likelihood of the model at each iteration and updates to the minimizer of this upper-bound. We first provide a "model level" interpretation of the EM upper-bound as sum of relative entropy divergences to a set of singleton models, induced by the set of observations. Our alternative motivation unifies the "observation level" and the "model level" view of the EM. As a result, we formulate an online version of the EM algorithm by adding an analogous inertia term which corresponds to the relative entropy divergence to the old model. Our motivation is more widely applicable than the previous approaches and leads to simple online updates for mixture of exponential distributions, hidden Markov models, and the first known online update for Kalman filters. Additionally, the finite sample form of the inertia term lets us derive online updates when there is no closed-form solution. Finally, we extend the analysis to the distributed setting where we motivate a systematic way of combining multiple hidden variable models. Experimentally, we validate the results on synthetic as well as real-world datasets.
Social Mindfulness and Psychosis: Neural Response to Socially Mindful Behavior in First-Episode Psychosis and Patients at Clinical High-Risk

Background: Psychosis is characterized by problems in social functioning and trust, the assumed glue to positive social relations. But what helps building trust? A prime candidate could be social mindfulness: the ability and willingness to see and consider another person’s needs and wishes during social decision making. We investigated whether first-episode psychosis patients (FEP) and patients at clinical high-risk (CHR) show reduced social mindfulness, and examined the underlying neural mechanisms. Methods: Twenty FEP, 17 CHR and 46 healthy controls, aged 16–31, performed the social mindfulness task (SoMi) during fMRI scanning, spontaneously and after the instruction “to keep the other’s best interest in mind. ” As first of two people, participants had to choose one out of four products, of which three were identical and one was unique, differing in a single aspect (e.g., color). Results: FEP tended to choose the unique item (unmindful choice) more often than controls. After instruction, all groups significantly increased the number of mindful choices compared to the spontaneous condition. FEP showed reduced activation of the caudate and medial prefrontal cortex (mPFC) during mindful, and of the anterior cingulate cortex (ACC), mPFC, and left dorsolateral prefrontal cortex (dlPFC) during unmindful decisions. CHR showed reduced activation of the ACC compared to controls. Discussion: FEP showed a trend toward more unmindful choices. A similar increase of mindful choices after instruction indicated the ability for social mindfulness when prompted. Results suggested reduced sensitivity to the rewarding aspects of social mindfulness in FEP, and reduced consideration for the other player. FEP (and CHR to a lesser extent) might perceive unmindful choices as less incongruent with the automatic mindful responses than controls. Reduced socially mindful behavior in FEP may hinder the building of trust and cooperative interactions.
SDN Controllers: Benchmarking & Performance Evaluation

Software Defined Networks offer flexible and intelligent network operations by splitting a traditional network into a centralized control plane and a programmable data plane. The intelligent control plane is responsible for providing flow paths to switches and optimizes network performance. The controller in the control plane is the fundamental element used for all operations of data plane management. Hence, the performance and capabilities of the controller itself are extremely important. Furthermore, the tools used to benchmark their performance must be accurate and effective in measuring different evaluation parameters. There are dozens of controller proposals available in existing literature. However, there is no quantitative comparative analysis for them. In this article, we present a comprehensive qualitative comparison of different SDN controllers, along with a quantitative analysis of their performance in different network scenarios. More specifically, we categorize and classify 34 controllers based on their capabilities, and present a qualitative comparison of their properties. We also discuss in-depth capabilities of benchmarking tools used for SDN controllers, along with best practices for quantitative controller evaluation. This work uses three benchmarking tools to compare nine controllers against multiple criteria. Finally, we discuss detailed research findings on the performance, benchmarking criteria, and evaluation testbeds for SDN controllers.
CCured: type-safe retrofitting of legacy code

In this paper we propose a scheme that combines type inference and run-time checking to make existing C programs type safe. We describe the CCured type system, which extends that of C by separating pointer types according to their usage. This type sysiem allows both pointers whose usage can be verified statically to be type safe, and pointers whose safety must be checked at run time. We prove a type soundness result and then we present a surprisingly simple type inference algoriihm that is able lo infer the appropriate pointer kinds for existing C programs. Our experience wiih the CCured sysiem shows that the inference is very effective for many C programs, as it is able to infer that most or all of the pointers are statically verifiable to be type safe. The remaining pointers are instrumented wiih efficient run-time checks to ensure that they are used safely. The resulting performance loss due to run-time checks is 0-150%, which is several times better than comparable approaches thai use only dynamic checking. Using CCured we have discovered programming bugs in established C programs such as several SPECINT95 benchmarks.
Ensemble Convolutional Neural Networks for Face Recognition

Many methods about face recognition have been put forward. Generally, the feature of the face image is extracted and then the classifier is used to complete the face recognition. There is no direct relationship between the method of feature extracting and the classifier. In recent years, based on the Convolutional Neural Networks of deep learning, better effect is achieved by merging feature extraction and classifier. What's more, ensemble classifier has become the common method of improving classifier accuracy. Therefore, in this paper, we have proposed two kinds of methods based on multiple CNNs classifiers for face recognition. These method has the same structure of CNNs but with different training sets. The first kind of method, adopting the way of random sampling and replacing, trains individual CNNs by using different training sets produced in the original set of data. The second kind of method produces different training sets by enlarging and decreasing the image size of the original set of data. The two kinds of method eventually use the voting method to merge individual CNNs results. Experiments have proved that the ensemble CNNs classifier is better than the single CNNs classifier, and the accuracy of the face recognition is as high as 99.5%.
Brain Oscillatory Correlates of Visual Short-Term Memory Errors

Brain dynamics of memory formation were explored during encoding and retention intervals of a visual working memory task. EEG data were acquired while subjects were exposed to grayscale images of widely known object categories (e.g., “luggage,” “chair,” and “car”). Following a short delay, two probes were shown to test memory accuracy. Oscillatory portraits of successful and erroneous memories were contrasted. Where significant differences were identified, oscillatory traits of false memories (i.e., when a novel probe item of the same category is recognized as familiar) were compared with those of successful and erroneous memories. Spectral analysis revealed theta (6–8 Hz) power over occipital channels for encoding of successful and false memories that was smaller when compared to other types of memory errors. The reduced theta power indicates successful encoding and reflects the efficient activation of the underlying neural assemblies. Prominent alpha-beta (10–26 Hz) activity belonging to the right parieto-occipital channels was identified during the retention interval. It was found to be larger for false memories and errors than that of correctly answered trials. High levels of alpha-beta oscillatory activity for errors correspond to poor maintenance leading to inefficient allocation of WM resources. In case of false memories, this would imply necessary cognitive effort to manage the extra semantic and perceptual load induced by the encoded stimuli.
Influence of Visual and Vestibular Hypersensitivity on Derealization and Depersonalization in Chronic Dizziness

Objective: The aim of this study was to investigate the relation between visual and vestibular hypersensitivity, and Depersonalization/Derealization symptoms in patients with chronic dizziness. Materials and Methods: 319 adult patients with chronic dizziness for more than 3 months (214 females and 105 males, mean age: 58 years, range: 13–90) were included in this prospective cross-sectional study. Patients underwent a complete audio-vestibular workup and 3 auto questionnaires: Hospital Anxiety and Depression (HAD), Depersonalization/Derealization Inventory (DDI), and an in-house questionnaire (Dizziness in Daily Activity, DDA) assessing 9 activities with a score ranging from 0 (no difficulty) to 10 (maximal discomfort) and 11 (avoidance) to detect patients with visual and vestibular hypersensitivity (VVH, a score > 41 corresponding to mean + 1 standard deviation). Results: DDI scores were higher in case of VVH (6.9 ± 6.79, n = 55 vs. 4.2 ± 4.81, n = 256 without VVH, p < 0.001, unpaired t-test), migraine (6.1 ± 6.40, n = 110 vs. 4.0 ± 4.42, n = 208no migraine, p < 0.001, unpaired t-test), and motion sickness (6.8 ± 5.93, n = 41 vs. 4.4 ± 5.11, n = 277 no motion sickness, p < 0.01, unpaired t-test). Women scored DDI higher than men (5.1 ± 5.42, n = 213 vs. 3.9 ± 4.91, n = 105, respectively, p < 0.05, unpaired t-test). DDI scores were also related to depression and anxiety. DDI score was also higher during spells than during the basal state. Conclusion: During chronic dizziness, Depersonalization/Derealization symptoms seem to be related to anxiety and depression. Moreover, they were prominent in women, in those with visual and vestibular hypersensitivity, migraine, and motion sickness.
Toward the Internet of Underground Things: A Systematic Survey

This paper provides recent advances in the area of Internet of Underground Things (IoUT) with emphasis on enabling communication technologies, networking issues, and localization techniques. IoUT is enabled by underground things (sensors), communication technology, and networking protocols. This new paradigm of IoUT facilitates the integration of sensing and communication in the underground environment for various industries, such as oil and gas, agriculture, seismic mapping, and border monitoring. These applications require to gather relevant information from the deployed underground things. However, the harsh underground propagation environment including sand, rock, and watersheds do not allow the use of single communication technology for information transfer between the surface and the underground things. Therefore, various wireless and wired communication technologies are used for underground communication. The wireless technologies are based on acoustic waves, electromagnetic waves, magnetic induction and visible light communication while the wired technologies use coaxial cable and optical fibers. In this paper, state-of-art communication technologies are surveyed, and the respective networking and localization techniques for IoUT are presented. Moreover, the advances and applications of IoUT are also reported. Also, new research challenges for the design and implementation of IoUT are identified.
AttResNet: Attention-based ResNet for Image Captioning

Image caption has been widely studied recently which, likes the human to understand a scene, learns the high-level semantic descriptions for a single image. To achieve this goal, many recent methods divide the task into two stages, namely, the encoder and decoder, respectively corresponding to feature extraction and semantic descriptions. With the development of the deep neural network, two stages can be realized with a convolutional neural networks (CNNs) followed by a recurrent neural networks (RNNs). Following the novel idea of such deep encoder-decoder framework, this paper mainly refines the encoder with an attention-based ResNet model to provide better semantic features for the decoder. Attention mechanism has been broadly recognized to be a useful strategy in image captioning, which highlights the image regions of interest and further emphasizes the corresponding semantics to enhance the captioning of image content. Specifically, we design an attention connection and then seamlessly couple it with the well-known ResNet. Thus, we call it AttResNet. To our best knowledge, this is the first attempt to apply ResNet for image captioning. Experiments on MSCOCO dataset validate our proposed model achieves favorable results.
Transcriptional Aneuploidy Responses of Brassica rapa-oleracea Monosomic Alien Addition Lines (MAALs) Derived From Natural Allopolyploid B. napus

Establishing the whole set of aneuploids, for one naturally evolved allopolyploid species, provides a unique opportunity to elucidate the transcriptomic response of the constituent subgenomes to serial aneuploidy. Previously, the whole set of monosomic alien addition lines (MAALs, C1-C9) with each of the nine C subgenome chromosomes, added to the extracted A subgenome, was developed in the context of the allotetraploid Brassica napus donor “Oro,” after the restitution of the ancestral B. rapa (RBR Oro) was realized. Herein, transcriptomic analysis using high-throughput technology was conducted to detect gene expression alterations in these MAALs and RBR. Compared to diploid RBR, the genes of all of the MAALs showed various degrees of dysregulated expressions that resulted from cis effects and more prevailing trans effects. In addition, the trans-effect on gene expression in MAALs increased with higher levels of homology between the recipient A subgenome and additional C subgenome chromosomes, instead of gene numbers of extra chromosomes. A total of 10 trans-effect dysregulated genes, among all pairwise comparisons, were mainly involved in the function of transporter activity. Furthermore, highly expressed genes were more prone to downregulation and vice-versa, suggesting a common trend for transcriptional pattern responses to aneuploidy. These results provided a comprehensive insight of the impact of gene expression of individual chromosomes, in one subgenome, on another intact subgenome for one allopolyploid with a long evolutionary history.
Classification of Advertisement Text on Facebook Using Synthetic Minority Over-Sampling Technique

Understanding in consumer behavior is an important task in the field of marketing. Dentsu's AISAS model is a model that has been proposed to describe consumer behavior. The model defines reaction when the consumer has seen advertising into five stages: attention, interest, search, action, and share. In this paper, advertisement text datasets from Facebook were labelled as the stages of AISAS model and learned to be classified by machine learning algorithms. Nevertheless, like many other real-world data, our dataset had imbalanced class distribution. The classifier algorithms tend to predict mostly the majority class. To overcome this problem, synthetic minority over-sampling technique (SMOTE) was adopted and also combined with chi-square based feature selection technique. Varieties of feature sizes based on various classifier algorithms were compared. In the appropriate feature size, SMOTE could improve the classification performance in terms of recall and F1 score.
Fisher Efficient Inference of Intractable Models

Maximum Likelihood Estimators (MLE) has many good properties. For example, the asymptotic variance of MLE solution attains equality of the asymptotic Cram{e}r-Rao lower bound (efficiency bound), which is the minimum possible variance for an unbiased estimator. However, obtaining such MLE solution requires calculating the likelihood function which may not be tractable due to the normalization term of the density model. In this paper, we derive a Discriminative Likelihood Estimator (DLE) from the Kullback-Leibler divergence minimization criterion implemented via density ratio estimation and a Stein operator. We study the problem of model inference using DLE. We prove its consistency and show that the asymptotic variance of its solution can attain the equality of the efficiency bound under mild regularity conditions. We also propose a dual formulation of DLE which can be easily optimized. Numerical studies validate our asymptotic theorems and we give an example where DLE successfully estimates an intractable model constructed using a pre-trained deep neural network.