Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multimodal Deep Learning Method for Android Malware Detection Using Various Features

With the widespread use of smartphones, the number of malware has been increasing exponentially. Among smart devices, android devices are the most targeted devices by malware because of their high popularity. This paper proposes a novel framework for android malware detection. Our framework uses various kinds of features to reflect the properties of android applications from various aspects, and the features are refined using our existence-based or similarity-based feature extraction method for effective feature representation on malware detection. Besides, a multimodal deep learning method is proposed to be used as a malware detection model. This paper is the first study of the multimodal deep learning to be used in the android malware detection. With our detection model, it was possible to maximize the benefits of encompassing multiple feature types. To evaluate the performance, we carried out various experiments with a total of 41 260 samples. We compared the accuracy of our model with that of other deep neural network models. Furthermore, we evaluated our framework in various aspects including the efficiency in model updates, the usefulness of diverse features, and our feature representation method. In addition, we compared the performance of our framework with those of other existing methods including deep learning-based methods.
A Robust Hierarchical Qp Setting for Screen Content Coding

Unlike conventional camera-captured videos (CCV), the screen content videos (SCV) are generated by computer, like text, animation, or graphics. The video contents are discontinuous, as one abrupt frame is often followed by many static frames. Therefore, the traditional hierarchical quantization parameter (QP) setting for CCV may not be suitable for SCV. In this paper, a robust hierarchical QP setting method is proposed. Concretely, a region recognition method is designed to identify the influential regions, whose quality has tremendous influence on subsequent frames. These influential regions employ a smaller QP to improve the RD (Rate Distortion) performance of encoded video. Experimental results show that the proposed method averagely reduces 1.7% bitrate, and improves RD performance for all the SCV, which outperforms other encoding methods.
Young, vulnerable and uncertain: Young workers' perceptions of work health and safety.

BACKGROUND Young workers are at an increased risk of work place injury, and are less likely to report hazards or injuries, or apply for workers' compensation even though they are over-represented in workers' compensation statistics in comparison with their older peers. OBJECTIVE To identify young workers' perceptions of work health and safety (WHS), why and how they report (or do not report) hazards and injuries, and examine where they source WHS information. This paper reports on the first stage of a larger, mixed methods study on WHS and young workers in South Australia. METHODS A total of 226 young South Australian workers aged between 12 and 25 years completed an online survey. Data were analyzed using chi-squared analysis for categorical variables and t-tests where the dependent variable was continuous. RESULTS Three quarters of young workers identified stress at work, not being trained to do the job, fatigue from work and lifting heavy things at work as WHS issues, although not necessarily as issues that they have personally experienced. Most young workers obtained information about WHS through their employer although a sizable proportion sourced this information from friends and social media. Young workers identified that they lacked confidence to report WHS issues. When they did report issues, many young workers reported these issues to their parents, despite identifying that their parents were often unable to help. CONCLUSIONS Our findings contribute to our understanding of young workers' perceptions of work health and safety. Although young workers could identify their concerns about particular health and safety related issues at work, they lacked the confidence to report their concerns and had limited information about where to go for help. The research suggests that there is a need to empower young people to report WHS concerns to their employer and provide structures and processes that encourage reporting.
Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Networks with High-Resolution Images

Robotic grasp detection for novel objects is a challenging task, but for the last few years, deep learning based approaches have achieved remarkable performance improvements, up to 96.1% accuracy, with RGB-D data. In this paper, we propose fully convolutional neural network (FCNN) based methods for robotic grasp detection. Our methods also achieved state-of-the-art detection accuracy (up to 96.6%) with state-of- the-art real-time computation time for high-resolution images (6-20ms per 360x360 image) on Cornell dataset. Due to FCNN, our proposed method can be applied to images with any size for detecting multigrasps on multiobjects. Proposed methods were evaluated using 4-axis robot arm with small parallel gripper and RGB-D camera for grasping challenging small, novel objects. With accurate vision-robot coordinate calibration through our proposed learning-based, fully automatic approach, our proposed method yielded 90% success rate.
A Hierarchical Model for Action Recognition Based on Body Parts

As increasing attention is paid on human action recognition from skeleton data, this paper focuses on such tasks by proposing a hierarchical model to discover the structure information of body-parts involved in human actions. Considering human actions as simultaneous motions of different body-parts of the human skeleton, we propose a hierarchical model to simultaneously apply discriminative body-parts selection at a same scale and group coupling of bundles of body-parts at different scales, while we decompose the human skeleton into a hierarchy of body-parts of varying scales. To represent such hierarchy of body-parts, we accordingly build a hierarchical RRV (Rotation and Relative Velocity) descriptors. The hierarchical representations encoded by Fisher vectors of the hierarchical RRV descriptors are properly formulated into the hierarchical model via the proposed hierarchical mixed norm, to apply sparse selection of body-parts and regularize the structure of such hierarchy of body-parts. The extensive evaluations on three challenging datasets demonstrate the effectiveness of our proposed approach, which achieves superior performance compared to state-of-the-art results on different sizes of datasets, showing it is more widely applicable than existing approaches.
OriNet: A Fully Convolutional Network for 3D Human Pose Estimation

In this paper, we propose a fully convolutional network for 3D human pose estimation from monocular images. We use limb orientations as a new way to represent 3D poses and bind the orientation together with the bounding box of each limb region to better associate images and predictions. The 3D orientations are modeled jointly with 2D keypoint detections. Without additional constraints, this simple method can achieve good results on several large-scale benchmarks. Further experiments show that our method can generalize well to novel scenes and is robust to inaccurate bounding boxes.
Roles for the IKK-Related Kinases TBK1 and IKKε in Cancer

While primarily studied for their roles in innate immune response, the IκB kinase (IKK)-related kinases TANK-binding kinase 1 (TBK1) and IKKε also promote the oncogenic phenotype in a variety of cancers. Additionally, several substrates of these kinases control proliferation, autophagy, cell survival, and cancer immune responses. Here we review the involvement of TBK1 and IKKε in controlling different cancers and in regulating responses to cancer immunotherapy.
Adversarial Text Generation via Feature-Mover's Distance

Generative adversarial networks (GANs) have achieved significant success in generating real-valued data. However, the discrete nature of text hinders the application of GAN to text-generation tasks. Instead of using the standard GAN objective, we propose to improve text-generation GAN via a novel approach inspired by optimal transport. Specifically, we consider matching the latent feature distributions of real and synthetic sentences using a novel metric, termed the feature-mover's distance (FMD). This formulation leads to a highly discriminative critic and easy-to-optimize objective, overcoming the mode-collapsing and brittle-training problems in existing methods. Extensive experiments are conducted on a variety of tasks to evaluate the proposed model empirically, including unconditional text generation, style transfer from non-parallel text, and unsupervised cipher cracking. The proposed model yields superior performance, demonstrating wide applicability and effectiveness.
Does Top Management Team Heterogeneity Pay?

The upper echelons theory argues that the characteristics of the senior managers affect firm performance. This study sought to test this proposition by evaluating the effect of top management team (TMT) heterogeneity on firm performance through a cross sectional descriptive survey. Primary data was collected from 53 large food and beverage manufacturing firms using a structured questionnaire and secondary data relating to the firms’ financial performance was obtained from the Kenya Revenue Authority. Using ordinary linear regression, the study established that different heterogeneity components had different effects on the various performance perspectives. Age heterogeneity affected financial and customer performance significantly, education level heterogeneity affected customer and internal processes significantly and tenure heterogeneity affected internal processes performance significantly. On the overall, TMT heterogeneity had a significant negative effect on financial, social and internal processes performance. This confirmed the upper echelons proposition and highlighted the harmful consequences of TMT heterogeneity on performance.
Polypharmacy leads to increased prevalence of potentially inappropriate medication in the Indonesian geriatric population visiting primary care facilities

Background The geriatric population is particularly vulnerable to being prescribed potentially inappropriate medication (PIM); however, the prevalence of this occurrence remains poorly investigated in Indonesia. Thus in this research, we focused on investigating the prevalence and predictors of PIM among the Indonesian geriatric population in a primary health care setting. Methods A retrospective observational study was conducted in 25 primary health care facilities in Karawang District, Indonesia. The medical prescriptions of patients aged ≥60 years during January–December 2014 were documented, and the PIM was assessed based on Beers and McLeod criteria. The influence of age, sex, number of diseases, and polypharmacy toward PIM was assessed using a logistic regression model. A P-value of <0.05 defined statistical significance. Results A total of 3,819 subjects were included in the study. PIM was highly prevalent (52.2%) among the Indonesian elderly. Chlorpheniramine, mefenamic acid, ibuprofen, and nifedipine were the most commonly prescribed PIM. Polypharmacy (odds ratio: 1.2 [0.6, 2.1]) was the only factor associated with the use of PIM, while sex, age, and multiple diseases did not show significant association. Conclusion PIM is a concern in the Indonesian geriatric population. Health care professionals are encouraged to review the medications of their geriatric patients using updated safety guidelines to prevent risks associated with PIM.
Blast Damage Assessment of Symmetrical Box-Shaped Underground Tunnel According to Peak Particle Velocity (PPV) and Single Degree of Freedom (SDOF) Criteria

This study aimed to determine the reliability of the damage criteria that was adopted by the peak particle velocity (PPV) method and the single degree of freedom (SDOF) approach to assess the damage level of a box-shaped underground tunnel. An advanced arbitrary Lagrangian Eulerian (ALE) technique available in LS-DYNA software was used to simulate a symmetrical underground tunnel that was subjected to a surface detonation. The validation results of peak pressure into the soil revealed a good consistency with the TM5-855-1 manual within differences that were much less than previous numerical studies. The pressure contours revealed that the blast waves travelled into the soil in a hemispherical shape and the peak reflected the pressure of the tunnel that occurred immediately before the incident pressure reached its highest value. The assessment results proved that the criteria of the above methods could efficiently predict the damage level of a box-shaped tunnel under different circumstances of explosive charge weight and lining thickness at a depth of 4 m within slight differences that were observed during van and small delivery truck (SDT) explosions. However, the efficiency of both the methods was varied with the increase of burial depth. Whereas, using the PPV method significantly underestimated or overestimated the damage level of the tunnel, especially during SDT and container explosions with a lining thickness of 250 mm at burial depths of 6 and 8 m, respectively, the damage level that was obtained by the SDOF method greatly matched with the observed failure modes of the tunnel. Furthermore, new boundary conditions and equations were proposed for the damage criteria of the PVV method.
Japanese guidelines for the management and treatment of generalized pustular psoriasis: The new pathogenesis and treatment of GPP

Generalized pustular psoriasis (GPP) is a rare disease characterized by recurrent fever and systemic flushing accompanied by extensive sterile pustules. The committee of the guidelines was founded as a collaborative project between the Japanese Dermatological Association and the Study Group for Rare Intractable Skin Diseases under the Ministry of Health, Labour, and Welfare Research Project on Overcoming Intractable Diseases. The aim of the guidelines was to provide current information to aid in the treatment of patients with GPP in Japan. Its contents include the diagnostic and severity classification criteria for GPP, its pathogenesis, and recommendations for the treatment of GPP. Since there are few clinical trial data with high levels of evidence for this rare disease, recommendations by the committee are described in the present guidelines.
Immunomodulatory placental‐expanded, mesenchymal stromal cells improve muscle function following hip arthroplasty

No regenerative approach has thus far been shown to be effective in skeletal muscle injuries, despite their high frequency and associated functional deficits. We sought to address surgical trauma‐related muscle injuries using local intraoperative application of allogeneic placenta‐derived, mesenchymal‐like adherent cells (PLX‐PAD), using hip arthroplasty as a standardized injury model, because of the high regenerative and immunomodulatory potency of this cell type.
Bead-Based Hydrodynamic Simulations of Rigid Magnetic Micropropellers

The field of synthetic microswimmers, micro-robots moving in aqueous environments, has evolved significantly in the last years. Micro-robots actuated and steered by external magnetic fields are of particular interest because of the biocompatibility of this energy source and the possibility of remote control, features suited for biomedical applications. While initial work has mostly focused on helical shapes, the design space under consideration has widened considerably with recent works, opening up new possibilities for optimization of propellers to meet specific requirements. Understanding the relation between shape on the one hand and targeted actuation and steerability on the other hand requires an understanding of their propulsion behavior. Here we propose hydrodynamic simulations for the characterization of rigid micropropellers of any shape, actuated by rotating external magnetic fields. The method consists of approximating the propellers by rigid clusters of spheres. We characterize the influence of model parameters on the swimming behavior to identify optimal simulation parameters using helical propellers as a test system. We then explore the behavior of randomly shaped propellers that were recently characterized experimentally. The simulations show that the orientation of the magnetic moment with respect to the propeller's internal coordinate system has a strong impact on the propulsion behavior and has to be known with a precision of ≤ 5° to predict the propeller's velocity-frequency curve. This result emphasizes the importance of the magnetic properties of the micropropellers for the design of desired functionalities for potential biomedical applications, and in particular the importance of their orientation within the propeller's structure.
Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data

Capturing ground truth data to benchmark super-resolution (SR) is challenging. Therefore, current quantitative studies are mainly evaluated on simulated data artificially sampled from ground truth images. We argue that such evaluations overestimate the actual performance of SR methods compared to their behavior on real images. To bridge this simulated-to-real gap, we introduce the Super-Resolution Erlangen (SupER) database, the first comprehensive laboratory SR database of all-real acquisitions with pixel-wise ground truth. It consists of more than 80k images of 14 scenes combining different facets: CMOS sensor noise, real sampling at four resolution levels, nine scene motion types, two photometric conditions, and lossy video coding at five levels. As such, the database exceeds existing benchmarks by an order of magnitude in quality and quantity. This paper also benchmarks 19 popular single-image and multi-frame algorithms on our data. The benchmark comprises a quantitative study by exploiting ground truth data and qualitative evaluations in a large-scale observer study. We also rigorously investigate agreements between both evaluations from a statistical perspective. One interesting result is that top-performing methods on simulated data may be surpassed by others on real data. Our insights can spur further algorithm development, and the publicy available dataset can foster future evaluations.
Gene Expression Analysis Identifies Novel Targets for Cervical Cancer Therapy

Although there has been significant progress in prevention and treatment of cervical cancer, this malignancy is still a leading cause of cancer death for women. Anti-angiogenesis and immunotherapy approaches have been providing survival benefits, however, response rates and durability of response need to be improved. There is a clear need for combination therapies that increase effectiveness of these agents and further improve patient outcome. Previous studies have largely focused on gene expression and molecular pathways in untreated cervix cancer. The goal of this study was to evaluate cancer-specific molecular pathways and their correlation with tumor immune profile in recurrent cervical cancer. Tumor and adjacent normal tissues were used to identify potential combination therapy targets. We found that DNA damage repair pathway genes were significantly overexpressed in the tumor. Based on our results and other recent investigations, we suggest that combination immune checkpoint and PARP inhibitor therapy is a high priority consideration for patients with recurrent, previously treated cervical cancer. We also show that multiple epithelial-mesenchymal transition-related genes, including MAP2K4, ID2, JAK1, FGF2, PIK3R1, AKT3, FGF13, and STAT3 may be potential targets. Interestingly, high-throughput analysis of Cancer Genome Atlas data identified distinct targets, including Fatty acid synthase FASN and Matrix Metallopeptidase 1 MMP1 as novel, promising combination therapy partners.
Development of an AmpliSeqTM Panel for Next-Generation Sequencing of a Set of Genetic Predictors of Persisting Pain

Background: Many gene variants modulate the individual perception of pain and possibly also its persistence. The limited selection of single functional variants is increasingly being replaced by analyses of the full coding and regulatory sequences of pain-relevant genes accessible by means of next generation sequencing (NGS). Methods: An NGS panel was created for a set of 77 human genes selected following different lines of evidence supporting their role in persisting pain. To address the role of these candidate genes, we established a sequencing assay based on a custom AmpliSeqTM panel to assess the exomic sequences in 72 subjects of Caucasian ethnicity. To identify the systems biology of the genes, the biological functions associated with these genes were assessed by means of a computational over-representation analysis. Results: Sequencing generated a median of 2.85 ⋅ 106 reads per run with a mean depth close to 200 reads, mean read length of 205 called bases and an average chip loading of 71%. A total of 3,185 genetic variants were called. A computational functional genomics analysis indicated that the proposed NGS gene panel covers biological processes identified previously as characterizing the functional genomics of persisting pain. Conclusion: Results of the NGS assay suggested that the produced nucleotide sequences are comparable to those earned with the classical Sanger sequencing technique. The assay is applicable for small to large-scale experimental setups to target the accessing of information about any nucleotide within the addressed genes in a study cohort.
CrowdProbe

Devices with integrated Wi-Fi chips broadcast beacons for network connection management purposes. Such information can be captured with inexpensive monitors and used to extract user behavior. To understand the behavior of visitors, we deployed our passive monitoring system---CrowdProbe, in a multi-floor museum for six months. We used a Hidden Markov Models (HMM) based trajectory inference algorithm to infer crowd movement using more than 1.7 million opportunistically obtained probe request frames. However, as more devices adopt schemes to randomize their MAC addresses in the passive probe session to protect user privacy, it becomes more difficult to track crowd and understand their behavior. In this paper, we try to make use of historical transition probability to reason about the movement of those randomized devices with spatial and temporal constraints. With CrowdProbe, we are able to achieve sufficient accuracy to understand the movement of visitors carrying devices with randomized MAC addresses.
A Review of the Cytokine IL-17 in Ocular Surface and Corneal Disease

ABSTRACT Aim: To investigate the role of interleukin-17 in ocular surface and corneal disease. Ocular surface and corneal disease is a leading cause of blindness and is an ongoing challenge for the public health sector to implement effective therapies. The majority of cells in corneal lesions are derived primarily from neutrophils that induce inflammatory events that lead to tissue damage. One of the key pro-inflammatory cytokines is IL-17, and it has been investigated in order to facilitate the understanding of the pathogenesis of ocular surface lesion development. Method: A review of the literature was performed through a systematic approach. Results: IL-17 has been shown to exacerbate dry eye disease, viral and bacterial keratitis lesion severity, although it was found to be protective for Acanthamoeba. Antibodies developed to neutralize IL-17 have shown some promise in reducing the severity of some diseases. Conclusion: IL-17 plays a role in the pathogenesis of ocular surface and corneal disease and targeting this cytokine may provide a useful treatment option in the future.