Miracle-dz/UniFAR-FTUs huggingface dataset:
An app to detect melanoma using deep learning: An approach to handle imbalanced data based on evolutionary algorithms

Skin cancer is a quite common type of cancer. Its incidence is more often in caucasian people and melanoma is the most lethal one. In order to increase patient prognosis, developing tools to assist early diagnosis is quite important. In the last few years, several methods have been proposed to deal with automated melanoma detection. Nonetheless, most of them are based only in dermoscopy images and/or do not take into account lesion clinical information. In this paper, we developed an ease and accessible mobile tool to assist melanoma detection. The app is linked to a convolutional neural network (CNN) trained on images collected from smartphones and lesion clinical information. Since the occurrence of melanoma is much smaller than other skin lesions, most of the datasets for this problem are imbalanced. To deal with this issue, we present an approach based on evolutionary algorithm to balance datasets. The proposed approach obtained promising results in comparison with related works with a balanced accuracy of 92% and a recall of 94%. However, it is a preliminary study, therefore these metrics may vary when applied to other datasets.
Estimating biomass, fishing mortality, and “total allowable discards” for surveyed non-target fish

This is a pre-copy-editing, author-produced PDF of an article accepted for publication in ICES Journal of Marine Science following peer review. The definitive publisher-authenticated version “Shephard, S. , Reid, D. G. , Gerritsen, H. D. , & Farnsworth, K. D. (2014). Estimating biomass, fishing mortality, and “total allowable discards” for surveyed non-target fish. ICES Journal of Marine Science: Journal du Conseil, fsu146. ” is available online at: doi:10.1093/icesjms/fsu146 peer-reviewed
Amphibians and Reptiles from the Pleistocene (Illinoian) of Williston, Florida

-. 1955. Vascularization of respiratory surfaces in Leiopelma hochstetteri Fitzinger and Xenopus laevis (Daudin). Acta Anat. 25: 34660. c a ge, as the capi laries a es of the palate in reeding speci ens l o e percent of the total i capi laries. 1957. The vascularization of respiratory surfaces in Ambystoma mexicanum (Cope) in ontogeny. Zool. Pol. 8: In Press. CZOPKOWA, G. , AND J. CZOPEK. 1955. The vascu arization of the respiratory surfaces in Bombina variegata (L.). Bull. Acad. Pol. Sci. Cl. II, 3: 313-18.
Preliminary investigation of indoor event detection using active sound probing

: Event detection of indoor objects such as doors has a variety of application including intruder detection, HVAC control, surveillance of independently living elderly persons. Therefore, numerous researches have been done on this topic by the UbiComp research community. In this research, we propose a method to accurately detect events in indoor everyday objects such as doors and windows without using distributed ubiquitous sensors. In our approach, we perform event recognition conjoining the Doppler shift that occurs by the moving indoor objects and the acoustic characteristics of the environment acquired by the impulse response. Analysis of the time series data of the Doppler shift, using directional high frequency sine wave to acquire information of the direction of the occurred event, using stereo microphones to record the re(cid:13)ected wave can be stated as the specialties in this research. Also, by incorporating the knowledge about the state transitions of an object in to a recognition model, we will try to increase the accuracy of our recognitions.
Mediation Design ∗

We propose a mechanism design approach to study the role of a mediator in dispute resolution and bargaining. The mediator provides a buyer and a seller with “reality checks” by controlling the information each party has about her own value for a transaction, and proposes a price at which trade can occur if parties agree. We first consider the class of static information disclosure and trading mechanisms, in which the mediator simultaneously selects the information disclosed to the parties and posts the price at which they can trade. We characterize the mechanism that maximizes the ex-ante gains from trade. We show it is optimal to restrict agents’ information, as this allows to increase the volume of trade and complete some of the most valuable trades that are lost in the welfare maximizing mechanism under full information. We then study the value of the mediator engaging in “shuttle diplomacy” by considering a class of dynamic information disclosure and trading mechanisms, and show that it is possible to design a dynamic mechanism that achieves ex-post efficiency. Shuttle diplomacy facilitates trade by allowing the mediator to condition information releases and prices posted on the history of feedbacks she receives from the parties during her meetings with them.
A Wavelet-based Algorithm for the Computation of Intraseasonal Oscillations Intensity and Frequency Indices and Application to Central Africa

The rainfall modeling at regional scale remains a great challenge in the tropics because of the complexity of the processes that induce rainfall variability. Then the good parameterization of some atmospheric processes will be of great contribution towards the improvement of regional models. In this paper we applied wavelet transform on 2.5°×2.5° daily Outgoing Long-wave Radiation (OLR) datasets for the period 1981-2015 (35 years) to extract Intraseasonal Intensity (ISOI) and intraseasonal Period (ISOP), with application to Central Africa (CA). In fact for each grid point in the study area, the wavelet transform was applied to the 25-70-day filtered daily OLR time series and the wavelet spectrum is obtained. In the resulting spectrum, the maximum variance for each day is taken as ISOI and the period exhibiting that maximum variance is the ISOP. The plots of seasonal mean ISOI and ISOP obtained showed that the ISO characteristics (amplitude, frequency) strongly vary from season to another. The ISO amplitude is extremely high during December-February (DJF) and March-May (MAM) and lower during JJA and SON seasons. As for the period of oscillations, the ISOP peaks during MAM and JJA seasons. But for the four seasons, the period is predominantly contained between 40-50 days, suggesting the dominance of Madden-Julian Oscillation (MJO) signal.
Comparative Metabolomics Study Revealed Difference in Central Carbon Metabolism between Sika Deer and Red Deer Antler

The antler regeneration has been well studied for the past two decades and adopted in the regenerative medicine model for studying on developmental biology. Despite our growing knowledge of functional molecules regulating antler regeneration, we still do not know whether antler from different deer species possess the exact same mechanism or not. Our previous comparative study between sika deer and red deer suggests that the metabolic pathways between them are profoundly different based on protein level. Therefore, the metabolomic technology is used to identify and quantify the metabolites in antler samples, providing interesting insights into differential metabolite profile of antlers between sika deer and red deer. The distinct metabolic characteristics of sika deer compared to red deer provide an opportunity to explain why the red deer antler with a larger size. The enrichment analysis of differential metabolites showed that three pathways including glycine and serine metabolism, methionine metabolism, and pterine biosynthesis had a significant difference between two antler groups.
Optimized workfl ow with TaqMan Assays and digital PCR

optimized by adjusting cycling conditions. RNA isolated from fusion-positive VCaP and fusion-negative LNCaP cell lines were used as reference material. In wet-lab experiments, it was possible to detect TMPRSS2:ERG transcripts with a detection limit of 0.05% fusion rate in a correspondingly high wild-type background. Spike-in experiments allowed the confi rmation of an easy workfl ow. This is the fi rst step in examining the potential of the assay as a noninvasive molecular test to monitor prostate cancer in the future. Current biomedical research aims at personalized treatments in the future for various cancers, for example, prostate cancer. Prostate-specifi c antigen (PSA) is a well-established biomarker for early detection of prostate cancer, but it has substantial false-positive and falsenegative rates [1]. More recently, the TMPRSS2:ERG gene fusion was discovered in most prostate cancers. Due to the high prevalence of the TMPRSS2:ERG gene fusion, which occurs in more than 50% of prostate cancer cases [2], this gene fusion appears to be a suitable biomarker for monitoring prostate cancer. Furthermore, it can be detected in less-invasive sample material such as blood and urine. Research is presently focused on the development of benchtop methodologies that detect biomarkers quickly, simply, and sensitively in order to initiate therapeutic measures correctly and in a timely manner. Use of digital PCR is currently being used as a highly sensitive method for reproducible and robust measurements without the use of standard curves. The Applied BiosystemsTM QuantStudioTM 3D Digital PCR System has a high point-of-care potential in the future due to its small footprint and easy workfl ow.
Effective Automated Feature Derivation via Reinforcement Learning for Microcredit Default Prediction

Microcredit is a new financial instrument serving the segment of population that typically lack collateral and are highly likely to be rejected by traditional financial institutions, by lending very small loans. For platforms that participate in such consumer finance activities, the key challenge lies in risk management and the popular credit scoring method predicting whether a borrower would default or not takes an important role in this field. However, the fact is that we are often facing mass of raw data and the traditional credit scoring is heavily depending on feature engineering involving domain expert knowledge, intuition and trial and error, which is often time consuming.It is very challenging to derive effective features from raw data as the searching space can be very large with noninformative features. In this paper, we propose a new performance-driven framework automated generating discriminating features from raw data via reinforcement learning to help improve the default prediction of the downstream classifier which may be a logistic regression or boosting tree. Specially, we first define a formal paradigm for the automated feature derivation framework which unifies the feature structure, its interpretation and the calculation logic together. For the particularity of the financial industry, the interpretation of the feature is often of high interest. Then we reformulate the feature generation problem as reinforcement learning by constructing a transformation link and regarding it as a sequential decision process. In addition, we carry out an effective practice on default prediction in consumer finance. Finally, experimental results on the data of user behavior log from 360 Financial show the significant improvement of the proposed method over our years of domain expert knowledge and the Genetic Programming. Moreover, this FDRL framework can be easily adapted to other applications due to its versatility.
Studies in the Grazing of Heather Moorland in Northeast Scotland. VI.20-Year Trends in Botanical Composition

Botanical composition and herbivore usage were monitored over a 20-year period at 15 moorland sites; point quadrats were recorded in fixed positions. Although composition reflected soil type and altitude, Calluna vulgaris was initially the main species at all sites, with cover averaging 61%. Grazing pressures varied from light to heavy, causing wide variation in the utilization of Calluna shoots. Hence, Calluna declined at four sites, stayed in balance or showed negligible trend at four sites, and increased at seven sites. At sites with Calluna decline, graminoids and forbs showed a general rise in cover, and ericoids and lichens decreased. Species increasing significantly included Agrostis capillaris, Anthoxanthum odoratum, Festuca ovina, Galium saxatile, Luzula multiflora, Nardus stricta and Rhytidiadelphus squarrosus; Deschampsia flexuosa was reduced in cover. At one site with agricultural reseeding nearby, Cynosurus cristatus, Dactylis glomerata and Lolium perenne became established. At sites with Calluna steady, changes in the main plant groups were small. Bryophytes increased modestly, the chief contributor being Pleurozium schreberi which replaced Hypnum cupressiforme. At sites with Calluna increase, changes were greater when the Calluna sward was continuous rather than patchy. At the former sites graminoids and forbs declined sharply, and bryophytes increased, particularly the pleurocarpous mosses Hylocomium splendens, Hypnum cupressiforme and Pleurozium schreberi. Species richness, as measured by the number of contacts with vascular plant species per point-quadrat pin, was much more affected by soil type than by Calluna trend. Species number declined somewhat at sites with Calluna static and increasing; at sites with Calluna decline, an increase in the number of herbs was offset by reduced numbers of bryophytes and lichens.
Efficiency of Intervention Counseling Program on the Enhanced Psychological Well-being and Reduced Post-traumatic Stress Disorder Symptoms Among Syrian Women Refugee Survivors

Background: The number of individuals displaced from their original countries due to civil wars, hunger, disasters, and international wars is increasing worldwide day by day. These refugees are more vulnerable to Post-Traumatic Stress Disorder (PTSD). Objective: The present study aimed to examine the effectiveness of the intervention program in improving the well-being and reducing Post-Traumatic Stress Disorder (PTSD) among a sample of Syrian refugee women in Jordan who had been displaced due to civil wars in Syria. Methods: The study recruited 40 Syrian refugee females in Jordan who were psychologically challenged, with high PTSD symptoms (assessed by PCL) and a psychological well-being impairment (assessed by PWD). The culture of Jordanian society discriminates and affects the Syrian refugee women, rendering them vulnerable to PTSD. Quasi-experimental design was used, wherein the participants were randomly distributed in experimental and control groups (n=20/group). The control group members did not receive any services related to psychological support or psychiatric medications, while the experimental group underwent a counseling program. Results: The present study demonstrated that the intervention program improves the well-being and reduces PTSD among the Syrian refugee women who constituted the experimental group. The intervention program and the PTSD manifestation were not affected by age. The present study recommended that the program be applied to the refugees in Jordan to improve the well-being of the women in the residential areas. Conclusion: Furthermore, an intervention on the Jordanian cultural impact on the refugee camps was also essential if the condition for the female refugees worsened. Lastly, the effect of Jordanian culture on Syrian refugees should be investigated since the literature presented a negative impact.
INDIVIDUAL ASSESSMENT OF MUSCLE FATIGUE AND ITS RELATION TO THE N-REPETITION MAXIMUM

The main focus of this paper comprises the assessment of muscle fatigue by means of time-domain and frequencydomain parameters. Based on a simple biomechanical model time-domain parameters can be computed using measurement data recorded with an isometric muscle contraction experiment of the knee extensor muscles. Frequency-domain parameters are computed from surface electromyographic recordings. Both time-domain parameters and frequency-domain parameters are compared to the experimentally determined individual N-repetition maximum of healthy subjects. In contrast to the change of the median frequency it turns out that fatigue time is able to quantify muscle fatigue and can be considered as a good predictor for the individual N-repetition maximum.
Energy Eﬃcient Applications for Low Powered Devices

The smartphones, now a days, are not only used for the basic purposes such as calls, etc. but also for many diﬀerent applications for many diﬀerent purposes releasing everyday for navigation etc, hence constraint on energy consumption has become huge bottleneck. We study various techniques to improve the battery life focusing on application level so that developer of each application can build the application such that energy consumption of his application can be optimized reducing the burden on battery of the device. Researchers have provided many solutions to optimize energy consumption that includes detection of energy bugs, battery virtualization, also various optimizations for diﬀerent kind of applications. We also study for network intensive applications, CPU intensive applications, etc many approaches to optimize the energy consumed, considering energy-performance trade-oﬀ. For these optimizations, developer should have clear idea of where the energy is spent inside his application so we also discuss various tools and techniques available to model energy consumption. However, none of these solutions are 100% accurate and remove all energy bugs or provide full optimizations. We identify improvements that can be done in existing approaches to make them more accurate. We focused on implementing techniques to detect energy bugs in the android applications. We presented and implemented algorithm to detect no sleep code paths by extending a generic IFDS/IDE solver for simple android applications having no asynchronous communication. In future it can be extended further to analyze and detect no sleep bugs in all android applications.
A New Therapy for Vitiligo Using Fire Needles: A Systematic Review of Evidence from 3618 Subjects

Introduction Fire needle therapy has been reported as an effective treatment for vitiligo. However, current clinical evidence has not been systematically evaluated. The aim of this study was to determine whether fire needle therapy is effective and safe for treating vitiligo. Methods Seven databases were searched until October 2019 for randomized controlled trials on fire needle therapy, with and without conventional treatments, versus any type of conventional therapy for treating vitiligo. The RevMan 5.3. 5 software was used to perform meta-analysis of the included studies. Results Forty-seven trials comprising 3618 patients were included. Fire needle combined with conventional vitiligo treatments had a higher efficacy (risk ratio (RR): 1.55, 95% confidence interval (CI): 1.46–1.65, P < 0.00001 and RR: 1.41, 95% CI: 1.24–1.61, P < 0.00001, respectively) and a greater effect on restoring the color of the area of the skin lesion (mean difference (MD): 3.40, 95% CI: 2.11–4.69, P < 0.00001), increasing the pigment point of vitiligo (MD: 0.83, 95% CI: 0.54–1.13, P < 0.00001) and improving the cytokine level (MD: 8.10, 95% CI: 6.94–9.27, P < 0.00001) and effectual time (MD: −4.76, 95% CI: −7.33 to −2.19, P=0.0003) than traditional methods. Limb lesions (RR: 1.60, 95% CI: 1.31–1.95, P < 0.00001) were more effectively treated when the treatments included fire needles, whereas the therapeutic effect of fire needles on either the head and neck (RR: 1.13, 95% CI: 0.78–1.64, P=0.52) or torso lesions (RR: 1.22, 95% CI: 0.82–1.81, P=0.33) was not significantly different compared to that without fire needles. No statistically significant differences in adverse effects (RR: 1.15, 95% CI: 0.89–1.49, P=0.28) and recurrence rates (RR: 0.90, 95% CI: 0.17–4.92, P=0.91) during the follow-up period were observed between treatment with and without fire needles. Conclusions Fire needle therapy combined with other conventional treatments is useful in treating vitiligo. Further studies with larger sample sizes should be performed to make a conclusive judgment. This trial is registered with CRD42018094918.
A Few-shot Dynamic Obstacle Avoidance Strategy in Unknown Environments

Obstacle avoidance is one of the basic capabilities of intelligent mobile robots. With the diversification of the application environment, mobile robots are required to avoid obstacles with higher generality. Benefit from the development of mobile platform and deep learning algorithm in recent years, we conceive a few-shot dynamic obstacle avoidance strategy to meet this higher generality demand. Under this metric-based metalearning method, mobile robots can quickly adapt to unknown environments by learning from several samples. In order to verify its effectiveness, we use this strategy to train a model and deploy it to the mobile robot and run multiple obstacle avoidance recognition tests in the real-world environment. The results of experiments performed on the mobile robot platform illustrates a good performance and verifies our proposed strategy. In addition to analyzing the experimental results, the advantages, disadvantages as well as application potential of the proposed strategy as a decision aid are also discussed.
Characterization and Comparative Analysis of RWP-RK Proteins from Arachis duranensis, Arachis ipaensis, and Arachis hypogaea

RWP-RK proteins are important factors involved in nitrate response and gametophyte development in plants, and the functions of RWP-RK proteins have been analyzed in many species. However, the characterization of peanut RWP-RK proteins is limited. In this study, we identified 16, 19, and 32 RWP-RK members from Arachis duranensis, Arachis ipaensis, and Arachis hypogaea, respectively, and investigated their evolution relationships. The RWP-RK proteins were classified into two groups, RWP-RK domain proteins and NODULE-INCEPTION-like proteins. Chromosomal distributions, gene structures, and conserved motifs of RWP-RK genes were compared among wild and cultivated peanuts. In addition, we identified 12 orthologous gene pairs from the two wild peanut species, 13 from A. duranensis and A. hypogaea, and 13 from A. ipaensis and A. hypogaea. One, one, and seventeen duplicated gene pairs were identified within the A. duranensis, A. ipaensis, and A. hypogaea genomes, respectively. Moreover, different numbers of cis-acting elements in the RWP-RK promoters were found in wild and cultivated species (87 in A. duranensis, 89 in A. ipaensis, and 92 in A. hypogaea), and as a result, many RWP-RK genes showed distinct expression patterns in different tissues. Our study will provide useful information for further functional and evolutionary analysis of the RWP-RK genes.
Deep Ensemble Art Style Recognition

The massive digitization of artworks during the last decades created the need for categorization, analysis, and management of huge amounts of data related to abstract concepts, highlighting a challenging problem in the field of computer science. The rapid progress of artificial intelligence and neural networks has provided tools and technologies that seem worthy of the challenge. Recognition of various art features in artworks has gained attention in the deep learning society. In this paper, we are concerned with the problem of art style recognition using deep networks. We compare the performance of 8 different deep architectures (VGG16, VGG19, ResNet50, ResNet152, Inception- V3, DenseNet121, DenseNet201 and Inception-ResNet-V2), on two different art datasets, including 3 architectures that have never been used on this task before, leading to state-of-the-art performance. We study the effect of data preprocessing prior to applying a deep learning model. We introduce a stacking ensemble method combining the results of first-stage classifiers through a meta-classifier, with the innovation of a versatile approach based on multiple models that extract and recognize different characteristics of the input, creating a more consistent model compared to existing works and achieving state-of-the-art accuracy on the largest art dataset available (WikiArt - 68,55%). We also discuss the impact of the data and art styles themselves on the performance of our models forming a manifold perspective on the problem.