Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Loops For Malware Classification Resilient to Feature-unaware Perturbations

In the past few years, both the industry and the academic communities have developed several approaches to detect malicious Android apps. State-of-the-art research approaches achieve very high accuracy when performing malware detection on existing datasets. These approaches perform their malware classification tasks in an "offline" scenario, where malware authors cannot learn from and adapt their malicious apps to these systems. In real-world deployments, however, adversaries get feedback about whether their app was detected, and can react accordingly by transforming their code until they are able to influence the classification. In this work, we propose a new approach for detecting Android malware that is designed to be resilient to feature-unaware perturbations without retraining. Our work builds on two key ideas. First, we consider only a subset of the codebase of a given app, both for precision and performance aspects. For this paper, our implementation focuses exclusively on the loops contained in a given app. We hypothesize, and empirically verify, that the code contained in apps' loops is enough to precisely detect malware. This provides the additional benefits of being less prone to noise and errors, and being more performant. The second idea is to build a feature space by extracting a set of labels for each loop, and by then considering each unique combination of these labels as a different feature: The combinatorial nature of this feature space makes it prohibitively difficult for an attacker to influence our feature vector and avoid detection, without access to the specific model used for classification. We assembled these techniques into a prototype, called LoopMC, which can locate loops in applications, extract features, and perform classification, without requiring source code. We used LoopMC to classify about 20,000 benign and malicious applications. While focusing on a smaller portion of the program may seem counterintuitive, the results of these experiments are surprising: our system achieves a classification accuracy of 99.3% and 99.1% for the Malware Genome Project and VirusShare datasets respectively, which outperforms previous approaches. We also evaluated LoopMC, along with the related work, in the context of various evasion techniques, and show that our system is more resilient to evasion.
Cartola FC Data Analysis: A simulation, analysis, and visualization tool based on Cartola FC Fantasy Game

The search for computational models based on the prediction of scenarios has attracted the attention of many researchers over the years. Within this context, Fantasy Games work as social media environments that mix real information on a virtual world, offering a challenging scenario. This work aims to explore this scenario by introducing a simulation, analysis, and visualization tool, based on the Fantasy Game -- Cartola FC. This tool automatically generates teams by using two distinct strategies based on statistical models for each tournament round, simulating an entire championship. From these theoretic models, we create a visualization tool to support user analysis. Lastly, we perform the planning of experiments to determine the influence of certain aspects of team's composition.
Differentiation of Human Embryonic Stem Cells to Sympathetic Neurons: A Potential Model for Understanding Neuroblastoma Pathogenesis

Background and Aims Previous studies modelling human neural crest differentiation from stem cells have resulted in a low yield of sympathetic neurons. Our aim was to optimise a method for the differentiation of human embryonic stem cells (hESCs) to sympathetic neuron-like cells (SN) to model normal human SNS development. Results Using stromal-derived inducing activity (SDIA) of PA6 cells plus BMP4 and B27 supplements, the H9 hESC line was differentiated to neural crest stem-like cells and SN-like cells. After 7 days of PA6 cell coculture, mRNA expression of SNAIL and SOX-9 neural crest specifier genes and the neural marker peripherin (PRPH) increased. Expression of the pluripotency marker OCT 4 decreased, whereas TP53 and LIN28B expression remained high at levels similar to SHSY5Y and IMR32 neuroblastoma cell lines. A 5-fold increase in the expression of the catecholaminergic marker tyrosine hydroxylase (TH) and the noradrenergic marker dopamine betahydroxylase (DBH) was observed by day 7 of differentiation. Fluorescence-activated cell sorting for the neural crest marker p75, enriched for cells expressing p75, DBH, TH, and PRPH, was more specific than p75 neural crest stem cell (NCSC) microbeads. On day 28 post p75 sorting, dual immunofluorescence identified sympathetic neurons by PRPH and TH copositivity cells in 20% of the cell population. Noradrenergic sympathetic neurons, identified by copositivity for both PHOX2B and DBH, were present in 9.4% ± 5.5% of cells. Conclusions We have optimised a method for noradrenergic SNS development using the H9 hESC line to improve our understanding of normal human SNS development and, in a future work, the pathogenesis of neuroblastoma.
MADE: Security Analytics for Enterprise Threat Detection

Enterprises are targeted by various malware activities at a staggering rate. To counteract the increased sophistication of cyber attacks, most enterprises deploy within their perimeter a number of security technologies, including firewalls, antivirus software, and web proxies, as well as specialized teams of security analysts forming Security Operations Centers (SOCs). In this paper we address the problem of detecting malicious activity in enterprise networks and prioritizing the detected activities according to their risk. We design a system called MADE using machine learning applied to data extracted from security logs. MADE leverages an extensive set of features for enterprise malicious communication and uses supervised learning in a novel way for prioritization, rather than detection, of enterprise malicious activities. MADE has been deployed in a large enterprise and used by SOC analysts. Over one month, MADE successfully prioritizes the most risky domains contacted by enterprise hosts, achieving a precision of 97% in 100 detected domains, at a very small false positive rate. We also demonstrate MADE's ability to identify new malicious activities (18 out of 100) overlooked by state-of-the-art security technologies.
Barriers for Access to New Medicines: Searching for the Balance Between Rising Costs and Limited Budgets

Introduction: There is continued unmet medical need for new medicines across countries especially for cancer, immunological diseases, and orphan diseases. However, there are growing challenges with funding new medicines at ever increasing prices along with funding increased medicine volumes with the growth in both infectious diseases and non-communicable diseases across countries. This has resulted in the development of new models to better manage the entry of new medicines, new financial models being postulated to finance new medicines as well as strategies to improve prescribing efficiency. However, more needs to be done. Consequently, the primary aim of this paper is to consider potential ways to optimize the use of new medicines balancing rising costs with increasing budgetary pressures to stimulate debate especially from a payer perspective. Methods: A narrative review of pharmaceutical policies and implications, as well as possible developments, based on key publications and initiatives known to the co-authors principally from a health authority perspective. Results: A number of initiatives and approaches have been identified including new models to better manage the entry of new medicines based on three pillars (pre-, peri-, and post-launch activities). Within this, we see the growing role of horizon scanning activities starting up to 36 months before launch, managed entry agreements and post launch follow-up. It is also likely there will be greater scrutiny over the effectiveness and value of new cancer medicines given ever increasing prices. This could include establishing minimum effectiveness targets for premium pricing along with re-evaluating prices as more medicines for cancer lose their patent. There will also be a greater involvement of patients especially with orphan diseases. New initiatives could include a greater role of multicriteria decision analysis, as well as looking at the potential for de-linking research and development from commercial activities to enhance affordability. Conclusion: There are a number of ongoing activities across countries to try and fund new valued medicines whilst attaining or maintaining universal healthcare. Such activities will grow with increasing resource pressures and continued unmet need.
FaceFeat-GAN: a Two-Stage Approach for Identity-Preserving Face Synthesis

The advance of Generative Adversarial Networks (GANs) enables realistic face image synthesis. However, synthesizing face images that preserve facial identity as well as have high diversity within each identity remains challenging. To address this problem, we present FaceFeat-GAN, a novel generative model that improves both image quality and diversity by using two stages. Unlike existing single-stage models that map random noise to image directly, our two-stage synthesis includes the first stage of diverse feature generation and the second stage of feature-to-image rendering. The competitions between generators and discriminators are carefully designed in both stages with different objective functions. Specially, in the first stage, they compete in the feature domain to synthesize various facial features rather than images. In the second stage, they compete in the image domain to render photo-realistic images that contain high diversity but preserve identity. Extensive experiments show that FaceFeat-GAN generates images that not only retain identity information but also have high diversity and quality, significantly outperforming previous methods.
Image descriptors analysis supported by Information Visualization with application in automatic classification

Automatic image classification strongly depends on image representation, commonly made using visual descriptors. Several works compare the variety of the available ones, aiming to guide analysts in which one to choose for each scenario, but they consider only numerical accuracy results, which may limit the understanding of reasons for these performance differences. This paper employs Neighbor Joining similarity trees to visually analyze three image descriptors, focusing on their application in an automatic classification scenario. The results demonstrated that these trees provide means to comprehend important information about the descriptors, such as how they describe images characteristics, which image aspects they focus in the representation, and which criteria they consider to distinguish images into different classes, revealing their strengths and limitations regarding the representation of a specific categorization scheme. We believe such analysis may help specialists to better choose which descriptor is adequate to be used in this data mining task.
Leveraging Multi-grained Sentiment Lexicon Information for Neural Sequence Models

Neural sequence models have achieved great success in sentence-level sentiment classification. However, some models are exceptionally complex or based on expensive features. Some other models recognize the value of existed linguistic resource but utilize it insufficiently. This paper proposes a novel and general method to incorporate lexicon information, including sentiment lexicons(+/-), negation words and intensifiers. Words are annotated in fine-grained and coarse-grained labels. The proposed method first encodes the fine-grained labels into sentiment embedding and concatenates it with word embedding. Second, the coarse-grained labels are utilized to enhance the attention mechanism to give large weight on sentiment-related words. Experimental results show that our method can increase classification accuracy for neural sequence models on both SST-5 and MR dataset. Specifically, the enhanced Bi-LSTM model can even compare with a Tree-LSTM which uses expensive phrase-level annotations. Further analysis shows that in most cases the lexicon resource can offer the right annotations. Besides, the proposed method is capable of overcoming the effect from inevitably wrong annotations.
Embedding Undersampling Rotation Forest for Imbalanced Problem

Rotation Forest is an ensemble learning approach achieving better performance comparing to Bagging and Boosting through building accurate and diverse classifiers using rotated feature space. However, like other conventional classifiers, Rotation Forest does not work well on the imbalanced data which are characterized as having much less examples of one class (minority class) than the other (majority class), and the cost of misclassifying minority class examples is often much more expensive than the contrary cases. This paper proposes a novel method called Embedding Undersampling Rotation Forest (EURF) to handle this problem (1) sampling subsets from the majority class and learning a projection matrix from each subset and (2) obtaining training sets by projecting re-undersampling subsets of the original data set to new spaces defined by the matrices and constructing an individual classifier from each training set. For the first method, undersampling is to force the rotation matrix to better capture the features of the minority class without harming the diversity between individual classifiers. With respect to the second method, the undersampling technique aims to improve the performance of individual classifiers on the minority class. The experimental results show that EURF achieves significantly better performance comparing to other state-of-the-art methods.
Learning to Match Transient Sound Events Using Attentional Similarity for Few-shot Sound Recognition

In this paper, we introduce a novel attentional similarity module for the problem of few-shot sound recognition. Given a few examples of an unseen sound event, a classifier must be quickly adapted to recognize the new sound event without much fine-tuning. The proposed attentional similarity module can be plugged into any metric-based learning method for few-shot learning, allowing the resulting model to especially match related short sound events. Extensive experiments on two datasets show that the proposed module consistently improves the performance of five different metric-based learning methods for few-shot sound recognition. The relative improvement ranges from +4.1% to +7.7% for 5-shot 5-way accuracy for the ESC-50 dataset, and from +2.1% to +6.5% for noiseESC-50. Qualitative results demonstrate that our method contributes in particular to the recognition of transient sound events.
Conditional Video Generation Using Action-Appearance Captions

The field of automatic video generation has received a boost thanks to the recent Generative Adversarial Networks (GANs). However, most existing methods cannot control the contents of the generated video using a text caption, losing their usefulness to a large extent. This particularly affects human videos due to their great variety of actions and appearances. This paper presents Conditional Flow and Texture GAN (CFT-GAN), a GAN-based video generation method from action-appearance captions. We propose a novel way of generating video by encoding a caption (e.g., "a man in blue jeans is playing golf") in a two-stage generation pipeline. Our CFT-GAN uses such caption to generate an optical flow (action) and a texture (appearance) for each frame. As a result, the output video reflects the content specified in the caption in a plausible way. Moreover, to train our method, we constructed a new dataset for human video generation with captions. We evaluated the proposed method qualitatively and quantitatively via an ablation study and a user study. The results demonstrate that CFT-GAN is able to successfully generate videos containing the action and appearances indicated in the captions.
Pathogenesis of Acquired Aplastic Anemia and the Role of the Bone Marrow Microenvironment

Aplastic anemia (AA) is characterized by bone marrow (BM) hypocellularity, resulting in peripheral cytopenias. An antigen-driven and likely auto-immune dysregulated T-cell homeostasis results in hematopoietic stem cell injury, which ultimately leads to the pathogenesis of the acquired form of this disease. Auto-immune and inflammatory processes further influence the disease course as well as response rate to therapy, mainly consisting of intensive immunosuppressive therapy and allogeneic hematopoietic cell transplantation. Bone marrow hematopoietic stem and progenitor cells are strongly regulated by the crosstalk with the surrounding microenvironment and its components like mesenchymal stromal cells, also consistently altered in AA. Whether latter is a contributing cause or rather consequence of the disease remains an open question. Overall, niche disruption may contribute to disease progression, sustain pancytopenia and promote clonal evolution. Here we review the existing knowledge on BM microenvironmental changes in acquired AA and discuss their relevance for the pathogenesis and therapy.
An interactive control system for mobile robot based on cloud services

Visual interaction is one of the most important ways for mobile robots to serve the elderly in the future. Mobile robots need to recognize human movements to follow human movements or evade actions. An indoor mobile robot interaction system based on cloud servers is presented. In a human‐machine coexistence environment, the mobile robot recognizes human motion, which represents the control intention for robot. In addition, if the person does not issue a control command, the robot follows the operator moving trajectory as a follower. A mobile robot with four Mecanum wheels is used in experiments. In the robot, a 3D camera is used to capture human motion and recognizes the language control commands. Then, the mobile robot executes the command action after translating the human body language to its motion direction control mode. Moreover, a 2D camera in the robot is used to autonomously capture 2D human multi‐view images and combines facial recognition to achieve specific human target to follow‐up. Some experiments are presented to verify the effectiveness of the whole Interaction control system.
Altered Effective Connectivity in the Default Network of the Brains of First-Episode, Drug-Naïve Schizophrenia Patients With Auditory Verbal Hallucinations

Although the default mode network (DMN) is known to be abnormal in schizophrenia (SZ) patients with auditory verbal hallucinations (AVHs), it is still unclear whether AVHs that occur in SZ are associated with certain information flow in the DMN. This study collected resting-state functional magnetic resonance imaging data from 28 first-episode, drug-naïve SZ patients with AVHs, 20 SZ patients without AVHs, and 38 healthy controls. We used Granger causality analysis (GCA) to examine effective connectivity (EC) of two hub regions [posterior cingulate cortex (PCC) and anteromedial prefrontal cortex (aMPFC)] within the DMN. We used two-sample t-tests to compare the difference in EC between the two patient groups, and used Spearman correlation analysis to characterize the relationship between imaging findings and clinical assessments. The GCA revealed that, compared with the non-AVHs group, EC decreased from aMPFC to left inferior temporal gyrus (ITG) and from PCC to left cerebellum posterior lobe, ITG, and right middle frontal gyrus in SZ patients with AVHs. We also found significant correlations between clinical assessments and mean strengths of connectivity from aMPFC to left ITG and from PCC to left ITG. Moreover, receiver operating characteristic analysis revealed that the above-mentioned effective connectivities had a diagnostic value for distinguishing SZ patients with AVHs from non-AVHs patients. These findings suggest that AVHs in SZ patients may be associated with the aberrant information flows of the DMN, and the left ITG may probably serve as a potential biomarker for the neural mechanisms underlying AVHs in SZ patients.
A Comparative Analysis of Loan Requests Classification Algorithms in a Peer-to-Peer Lending Platform

The Peer-to-Peer (P2P) on-line lending is an emerging lending modality that brings creditors and borrowers closer while enabling a significant reduction of bureaucracy in the lending process. Despite its appealing, the increase in the demand for this lending modality depends on a rigorous settlement of the risk assignment to the potential borrowers. Considering this issue, this article discusses an experimental analysis of classification methods for P2P on-line lending default prediction. The performed experiments were based on the application of the implemented classification algorithms over the data mass formed by borrowers' profiles and loan history records of the P2P Lending Club platform. As the main contribution, the study revealed that it is possible to obtain satisfactory prediction results with a set of attributes smaller than those that were used in studies previously presented in the literature. In addition, it could be verified that, since the algorithms based on decision trees have proved highly effective, the use of these methods is a feasible approach to support the development of lending negotiation tools.
A micro Lie theory for state estimation in robotics

A Lie group is an old mathematical abstract object dating back to the XIX century, when mathematician Sophus Lie laid the foundations of the theory of continuous transformation groups. As it often happens, its usage has spread over diverse areas of science and technology many years later. In robotics, we are recently experiencing an important trend in its usage, at least in the fields of estimation, and particularly in motion estimation for navigation. Yet for a vast majority of roboticians, Lie groups are highly abstract constructions and therefore difficult to understand and to use. This may be due to the fact that most of the literature on Lie theory is written by and for mathematicians and physicists, who might be more used than us, perhaps by their academic formation, to the deep abstractions this theory deals with. In estimation for robotics, it is often not necessary to exploit the full capacity of the theory, and therefore an effort of selection of materials is required. In this paper, we will walk through the most basic principles of the Lie theory, with the aim of conveying clear and useful ideas, and leave a significant corpus of the Lie theory behind. Even with this mutilation, the material included here has proven to be extremely useful in modern estimation algorithms for robotics, especially in the fields of SLAM, visual odometry, and the like. We provide also a vast reference of formulas for the major groups used in robotics, including most jacobian matrices and the way to easily manipulate them.
Erratum to: A reference human genome dataset of the BGISEQ-500 sequencer

1National Institutes for food and drug Control (NIFDC), Beijing 100050, P. R. China, 2BGI-Shenzhen, Shenzhen 518083, P. R. China, 3State Food and Drug Administration Hubei Center for Medical Equipment Quality Supervision and Testing, Wuhan 430000, P. R. China and 4BGI-Qingdao, Qingdao 266555, China ∗Correspondence address. Shangxian Gao, E-mail: gaoshangxian@126.com, Feng Mu, E-mail: mufeng@genomics.cn and Zhaopeng Yang, E-mail: yangzp@nifdc.org. cn †These authors contribute equally to the article.