Miracle-dz/UniFAR-FTUs huggingface dataset:
Deepmole: Deep neural networks for skin mole lesion classification

Nowadays, the occurrence of skin cancer cases has grown worldwide due to the extended exposure to the harmful radiation from the Sun. Most common approach to detect the malignancy of skin moles is by visual inspection performed by an expert dermatologist, using a set of specific clinical rules. Computer-aided diagnosis, based on skin mole imaging, is another concurrent method which has experienced major advancements due to improvement of imaging sensors and processing power. However, these schemes use hand-crafted features which are difficult to tune and perform poorly on new cases due to lack of generalization power. In this study we present a method that use a pretrained deep neural network (DNN) to automatically extract a set of representative features that can be later used to diagnose a sample of skin lesion for malignancy. The experimental tests carried out on a clinical dataset show that the classification performance using DNN-based features performs better than the state-of-the-art techniques.
Current strategies for improving access and adherence to antiretroviral therapies in resource-limited settings

The rollout of antiretroviral therapy (ART) significantly reduced human immunodeficiency virus (HIV)-related morbidity and mortality, but good clinical outcomes depend on access and adherence to treatment. In resource-limited settings, where over 90% of the world’s HIV-infected population resides, data on barriers to treatment are emerging that contribute to low rates of uptake in HIV testing, linkage to and retention in HIV care systems, and suboptimal adherence rates to therapy. A review of the literature reveals limited evidence to inform strategies to improve access and adherence with the majority of studies from sub-Saharan Africa. Data from observational studies and randomized controlled trials support home-based, mobile and antenatal care HIV testing, task-shifting from doctor-based to nurse-based and lower level provider care, and adherence support through education, counseling and mobile phone messaging services. Strategies with more limited evidence include targeted HIV testing for couples and family members of ART patients, decentralization of HIV care, including through home- and community-based ART programs, and adherence promotion through peer health workers, treatment supporters, and directly observed therapy. There is little evidence for improving access and adherence among vulnerable groups such as women, children and adolescents, and other high-risk populations and for addressing major barriers. Overall, studies are few in number and suffer from methodological issues. Recommendations for further research include health information technology, social-level factors like HIV stigma, and new research directions in cost-effectiveness, operations, and implementation. Findings from this review make a compelling case for more data to guide strategies to improve access and adherence to treatment in resource-limited settings.
Latent SVM for Object Localization in Weakly Labeled Videos

We consider the problem of object localization in Internet videos. An Internet video (e.g. YouTube videos) is often associated with a semantic label (also known as a tag) describing the main object present within it. However, the tag does not provide any spatial or temporal information about the main object in the video. Such videos are weakly labelled. Given weakly labelled video with video-level object class tags, our goal is to learn a model that can be used to localize the objects in other videos with such tags. We define a latent SVM based learning framework to tackle this problem. We demonstrate the effectiveness of our method on a dataset composed of videos collected from YouTube.
Collaborative Distributed Intrusion Detection System

In order to protect Internet users from various attacks such as worms, viruses and other intrusions, signature-based intrusion detection system (IDS) should be deployed at the critical part of the network with rapid response for updating newly emerged attack signatures and containing the spread of worms or viruses at their early stage. The processing speed of one IDS cannot achieve the throughput requirement in the core networks because of the pattern matching, the key operation for signature-based IDS, is complex and time consuming. In this paper, it argues that if the signature set is shared by multiple IDSs, a packet needs to be checked once and once only by one of the IDSs, so traffic load can be redistributed among the IDSs to avoid local congestion. Packet marking is used to indicate the status of this packet utilized by collaborative IDSs, and a redistribution strategy named inner logical ring (ILR) is built among IDSs to redistribute the traffic load. Meanwhile, caching scheme is used to keep sequence for packets belonging to the same flow. This collaborative distributed IDS is robust with rapid response to various attacks, and the detection throughput is significantly increased from the throughput of the weakest IDS to the summation of all the collaborative IDSs.
Multi-objective optimization for aerodynamic designs by using ARMOGAs

Global trade-offs for aerodynamic design of supersonic transport (SST) have been investigated by multiobjective evolutionary algorithms (MOEAs). The objectives are to reduce both drag and sonic boom to make next-generation SST more feasible. Adaptive range multiobjective genetic algorithms (ARMOGAs) are utilized for the efficient search. The trade-offs are analysed by self-organizing map (SOM), which provides a topology preserving mapping from the high dimensional space to two dimensions. ARMOGAs and SOM can be good design tools to conduct aerodynamic design optimizations and analyse the results.
Encoding rotation invariant features in HEP-2 cell classification

Detection of the antinuclear antibodies (ANAs) in patient serum can be accomplished via indirect immunofluorescence (IIF) technique in laboratory. However, the currently practiced manual detection system relies on specialists to observe HEp-2 slides, which suffers from severe drawbacks like being subjective and time-consuming. In this study, we present an automatic classification system for staining patterns of HEp-2 cells. We propose to extract two kinds of rotation invariant descriptors, i.e., Pairwise Local Ternary Patterns with Spatial Rotation Invariant (PLTP-SRI) feature and Bag-of-Words (BoW) representation based on dense SIFT. The descriptors are concatenated to achieve the advantages of both highly descriptive ability and robustness against rotations. Incorporated with a linear Support Vector Machine (SVM) classifier, our proposed algorithm demonstrates its effectiveness by testing on two datasets: ICPR12 dataset and ICIP2013 training dataset.
Preschool Executive Function Profiles: Implications for Math Achievement in Grades 1 and 3

ABSTRACT This longitudinal study aimed to identify specific executive function (EF) profiles in 5-year-old children and to examine the association between these profiles and later math achievement. Six EF measures were administered to 175 children who were then tested in Grades 1 and 3 on math tasks. Using a cluster analysis, four EF profiles were identified: an optimal EF profile, a typical EF profile, a weak working memory (WM)-shifting profile, and a general EF deficit profile. These groups performed differently in math. In particular, the group with a weak WM-shifting profile, whose performance was equivalent to that of the group with a typical EF profile in arithmetic facts and in math problems in Grade 1, showed difficulties similar to the group, with general EF deficits in Grade 3. The association of minor difficulties in WM at preschool ages with later math achievement and the implications for educational practices are discussed.
Working with Caregivers to Mitigate Automatic Responses in Families Using Mindfulness Techniques in a Parent Discussion Group

The purpose of this thesis was to find effective mitigation strategies for common negative caregiver-child “automated” response patterns for families with high to average risk factors. The participants in this study included a convenience sample of 13 caregivers of preschoolers. This exploratory intervention research study used mixed methods to describe parental automated responses to their children's behavior through self-report. Both quantitative and qualitative data were collected through demographic surveys that included open and closed items, preand poststudy questionnaires, participant attendance and participation recordings, participant selfreporting in their home setting, field notes of self-reported progress in the caregiver discussion group, and an audio recorded semi-structured group discussion. Staged interventions requiring attendance included pre-study instruction, field discussions, and the final group interview; which were also designed as the means to acquire mindfulness skills as well as offered the benefits of group field discussion supports from peers and the parent educator. Study features included inhome daily Mindfulness Journaling and Action Plan Script practices. Overall, the results of the study showed that either full or partial participation in study interventions offered perceived benefits in understanding of 1) child and adult development/ behavior, 2) the workings of emotional regulation/dysregulation and 3) in-turn practical implementation of mindfulness techniques to aid in reducing automatic responses between caregivers and their children. Participants reported the increased identification of triggering events and related thoughts, feelings, and physical responses, the creation of new mental scripts, as well as the selection of mindful calming techniques to offset triggered reactions. This study adds to the current literature in the area of enlisting caregivers as change agents in rewriting automatic response patterns through the use of mindfulness techniques learned in a discussion group with the guidance of a parent educator and peer supports, which resulted in a perceived reduction of negative effects between caregivers and the children in their care.
Serum and tissue leptin in lung cancer: A meta-analysis

Many studies have found that leptin is involved in tumorigenesis and the progression of lung cancer. However, these studies were inconsistent. Therefore, we performed a meta-analysis to investigate the role of leptin in the patients with lung cancer. A systematic literature search in the several databases and on commercial Internet search engines was carried out to identify studies published up to July 8, 2016. The standardized mean difference (SMD) and odds ratio (OR) with 95% confidence interval (CI) were used to investigate the effect sizes. Finally, 21 eligible articles were included in the current meta-analysis. Overall, there is no relationship between levels of serum leptin and lung cancer. However, a subgroup analysis in high-study quality group found a weak association between serum leptin concentrations and lung cancer in Chinese (SMD=0.77, P=0.035). Additionally, the meta-analysis indicates that the serum leptin levels were lower in the weight-losing group than in the sustained weight group (SMD=-0.80, P=0.001). Further, there was evidence of a significant association between expression levels of leptin protein in tissue and lung cancer (OR=7.35, P<0.001). The present meta-analysis suggests that the serum and tissue leptin may be involved in the pathogenesis of lung cancer and tumor metastasis, especially among Chinese. However, the leptin may not appear to play an important role in cancer cachexia development.
Riemannian Metric and Geometric Mean for Positive Semidefinite Matrices of Fixed Rank

This paper introduces a new metric and mean on the set of positive semidefinite matrices of fixed-rank. The proposed metric is derived from a well-chosen Riemannian quotient geometry that generalizes the reductive geometry of the positive cone and the associated natural metric. The resulting Riemannian space has strong geometrical properties: it is geodesically complete, and the metric is invariant with respect to all transformations that preserve angles (orthogonal transformations, scalings, and pseudoinversion). A meaningful approximation of the associated Riemannian distance is proposed, that can be efficiently numerically computed via a simple algorithm based on SVD. The induced mean preserves the rank, possesses the most desirable characteristics of a geometric mean, and is easy to compute.
Deformable Part Model Based Crossroad Recognition for Unmanned Vehicles

GPS is very popular for locating the unmanned vehicles. However, it cannot solve all the problems about location. As crossroad is very important for unmanned vehicles, it is necessary for unmanned vehicles to recognize the crossroad and to load some other recognition tasks (such as traffic light recognition and traffic sign recognition) at the same time. The precision of GPS can hardly tell us if the traffic lights or traffic signs are shown in images. The best way to obtain this is using image to recognize the crossroads. However, most recent works about scene recognition can not solve the problem of specific crossroad recognition well because of weakening the special structure. The latent spacial structure is a key factor that makes specific crossroad different. There are some differences between their idea and ours. In this paper, a method is proposed to help unmanned vehicles recognize the specific crossroads based on deformable part models (DPM's) with latent SVM training. In order to validate the method, a mid-large database is built. The excellent performaence of this method is shown in the experiments and illustrates this method to be promising in the future.
Depression for college students in a traditional Christian culture context: the role of attachment, spirituality and social support

ABSTRACT The role of attachment, perceived social support, and perceived discrepancy from spiritual ideal were assessed in their association with depression in college students in a Christian culture environment. Participants (N = 155) at a small Christian university completed the Center for Epidemiological Studies Depression Scale, the Experiences in Close Relationships questionnaire, and questions about satisfaction with social support and discrepancy of their spiritual well-being to an ideal spiritual well-being. Results showed that social support, insecure attachment and discrepancy from spiritual ideal were all significant predictors of depression. Attachment was found to moderate the association between perceived spiritual well-being and depression, with insecurely attached individuals more at risk for depression if they evaluate their spiritual development as lacking. Implications are discussed.
Biomass of a fragment of the montane seasonal deciduous forest the south Brazil

The objective the work was to quantify the biomass using the destructive method of a forest fragment located in the South of Brazil. The study was conducted in fragment of Montane Seasonal Deciduous Forest with approximately 55 ha, centered at the coordinates S 27˚23’44” and W 53˚25’59”. For that, three plots of 12x12 m were installed. All plants with diameter at breast height (DBH) greater than 5 cm were identified at the species level. All trees with DBH>10 cm had the following information collected: DBH, total height (h), height at the morphological inversion point (MIP) and the diameter at 0%, 25%, 50%, 75% and 90% of the MIP height. As result, we verified that trees with DBH> 10 cm represent more than 90% of the total biomass (345.2 Mg ha -1 ). The estimated biomass of leaf litter was 12.4 Mg ha -1 . The stock of dry aboveground biomass and litter for the study area was 380.5 Mg ha -1 .
The uptake, distribution, and antitumor activity of 1-(2-chloroethyl)-3-cyclohexyl-1-nitrosourea in the murine glioma.

The antitumor activity of 1-(2-chloroethyl)-3-cyclohexyl-1-nitrosourea (CCNU) against the murine ependymoblastoma, glioma 26, and glioma 261 was compared with the uptake and distribution of the 14C-labeled drug in the murine ependymoblastoma. With a modified Wilcoxon rank-sum test, it was found that CCNU increased life-span in tumor animals from 2- to 4-fold, depending on the dose used and the particular tumor type studied. Uptake and distribution studies of CCNU-14C in intracerebral and subcutaneous tumors, brain adjacent to tumor, and distant (normal) brain indicated that the parent drug had relatively constant tissue/plasma ratio levels. Of the ether-soluble fractions, CCNU had a tissue/plasma ratio of greater than 4 and accounted for 15% of the ether-soluble radioactivity. From the data, it was concluded that the antitumor activity of CCNU is probably independent of preferential uptake of distribution between normal and tumor tissue. Although few conclusions concerning the mode of action of CCNU on gliomatous tumors can be made, nevertheless further consideration of its use in the treatment of human gliomas seems warranted.
Construction of 4-band linear-phase orthogonal perfect-reconstruction filter banks using iterative eigenfilter method

This paper presents an eigenfilter based method for the design of 4-band perfect reconstruction (PR) filter banks. All the filters of the filter bank exhibit linear phase and orthogonality property. The eigenfilter method has been used so far to impose linear constraints on the filter, however, in this paper, we discuss how to impose nonlinear constraints on the filter using the eigenfilter method. The analysis lowpass filter impulse response is obtained using an iterative approach in the eigenfilter method to minimize the error as given by the alias cancellation equations in the PR filter bank. Once the analysis lowpass filter impulse response h0[n] is obtained, the other filters are obtained using shuffling matrices. We present two design examples of a 4-channel perfect reconstruction filter bank. We also report a minor modification in the eigenfilter method for imposing non-homogeneous linear constraints on the filter.
Learnability of Non-I.I.D

Learnability has always been one of the most central problems in learning theory. Most previous studies on this issue were based on the assumption that the samples are drawn independently and identically according to an underlying (unknown) distribution. The i.i. d. assumption, however, does not hold in many real applications. In this paper, we study the learnability of problems where the samples are drawn from empirical process of stationary βmixing sequence, which has been a widely-used assumption implying a dependence weaken over time in training samples. By utilizing the independent blocks technique, we provide a sufficient and necessary condition for learnability, that is, average stability is equivalent to learnability with AERM (Asymptotic Empirical Risk Minimization) in the non-i.i. d. learning setting. In addition, we also discuss the generalization error when the test variable is dependent on the training sample.
Resonance-based Control Method for Multi-Joint Robot with Variable Joint Stiffness

This study presents resonance-based control methods proposed by the authors for multi-joint robots. The proposed control methods adjust stiffness of mechanical elements installed in each joint of the robots to minimize actuator torque while generating desired periodic motions. Some of these control method adjust not only stiffness but also motion patterns to reduce further actuator torque. Advantages of proposed controllers are to work well without using exact parameter values nor huge numerical calculations. Some stability analyses, simulations and experiments have verified the effectiveness of the proposed control methods. The proposed controllers can be applied to walking/running robots, energy saving industrial manipulators, and walking support systems.
Comparison of Local-rigid and Non-rigid Registration of Diffusion Weighted MRI for Improved Estimation of ADC.

Our aim is to develop methodologies for the determination of ADC, suitable for use in patient management and drug trials. In this paper we demonstrate improvements in ADC measurement which at the same time maintain the biological structures essential for analysis of tissue heterogeneity. Two motion correction methods are compared, one based upon non-rigid registration using freely available open source algorithms and the other a purpose designed local rigid registration. Performance of these methods is evaluated using metrics computed from regional ADC histograms. While the nonrigid registration method has the advantages of being applicable on the whole volume and in a fully automatic fashion, the local rigid registration method is much faster and also provides advantages with regard to data smoothness by avoiding interpolation and sub-sampling. Our study also shows that the averaging commonly applied to DW-MR images as part of the acquisition protocol should be avoided if at all possible.
Data labeling method based on cluster purity using relative rough entropy for categorical data clustering

Clustering is an important technique in data mining. Clustering a large data set is difficult and time consuming. An approach called data labeling has been suggested for clustering large databases using sampling technique to improve efficiency of clustering. A sampled data is selected randomly for initial clustering and data points which are not sampled and unclustered are given cluster label or an outlier based on various data labeling techniques. Data labeling is an easy task in numerical domain because it is performed based on distance between a cluster and an unlabeled data point. However, in categorical domain since the distance is not defined properly between data points and between data point with cluster, then data labeling is a difficult task for categorical data. In this paper, we have proposed a method for data labeling using Relative Rough Entropy for clustering categorical data. The concept of entropy, introduced by Shannon with particular reference to information theory is a powerful mechanism for the measurement of uncertainty information. In this method, data labeling is performed by integrating entropy with rough sets. In this paper, the cluster purity is also used for outlier detection. The experimental results show that the efficiency and clustering quality of this algorithm are better than the previous algorithms.
Dirty Recycling: Auto Salvage and Its Potential Impacts on Marginalized Populations

The salvage yard represents the final waypoint in the cradle-to-grave cycle of the automobile. Residual amounts of petroleum hydrocarbons, heavy metals, and acids used in automobiles can be extremely harmful to human health and the environment if not managed correctly. The purpose of this study was to assess the extent to which minority populations were exposed to the hazards of the auto salvage industry. Census data for population, income, race/ethnicity, sex, and age were organized using ArcGIS software. Population demographics were analyzed in the areas surrounding 98 auto salvage yards found in Philadelphia and Adams Counties, Pennsylvania. In Philadelphia County, the results showed that low-income minorities, females, and 65+ individuals are over represented groups near auto salvage yards. Conversely, Adams County showed few spatial relationships in demographic distribution. Our findings suggest that in urban counties, such as Philadelphia, depressed property values have resulted in a large percentage of below average income minorities inhabiting areas in close proximity to auto salvage yards. On the other hand, auto salvage yards in rural areas, such as Adams County, do not appear to have the same effect because population density and racial diversity are much lower.