Miracle-dz/UniFAR-FTUs huggingface dataset:
The Cross-Talk Between the TNF-α and RASSF-Hippo Signalling Pathways

The regulation of cell death through apoptosis is essential to a number of physiological processes. Defective apoptosis regulation is associated with many abnormalities including anomalies in organ development, altered immune response and the development of cancer. Several signalling pathways are known to regulate apoptosis including the Tumour Necrosis Factor-α (TNF-α) and Hippo signalling pathways. In this paper we review the cross-talk between the TNF-α pathway and the Hippo signalling pathway. Several molecules that tightly regulate the Hippo pathway, such as members of the Ras-association domain family member (RASSF) family proteins, interact and modulate some key proteins within the TNF-α pathway. Meanwhile, TNF-α stimulation also affects the expression and activation of core components of the Hippo pathway. This implies the crucial role of signal integration between these two major pathways in regulating apoptosis.
NPUFort: a secure architecture of DNN accelerator against model inversion attack

Deep neural network (DNN) models are widely used for inference in many application scenarios. DNN accelerators are not designed with security in mind, but for higher performance and lower energy consumption. Hence, they are suffering from the security risk of being attacked. The insecure design flaws of existing DNN accelerators can be exploited to recover the structure of DNN model from the plain instructions, thus the runtime environment can be controlled to obtain the weights of DNN model. Furthermore, the structure of DNN model running on the accelerator is acquired by the side channel information and interrupt status register. To protect general DNN accelerator from being attacked by model inversion attack, this paper proposes a secure and general architecture called NPUFort, which guarantees the confidentiality of the parameters of DNN model and mitigates side-channel information leakage. The experimental results demonstrate the feasibility and effectiveness of the secure architecture of DNN accelerators with negligible performance overhead.
Sociodemographic correlates of obesity among Ghanaian women

Abstract Objective To examine the sociodemographic correlates of obesity among Ghanaian women. Design The 2003 and 2008 Ghana Demographic and Health Survey data sets were used to examine the sociodemographic characteristics and the BMI of women aged 15–49 years using descriptive statistics, bivariate and multivariate analyses. Setting Ghana is a West African country which is divided into ten administrative regions. The country is further divided into the northern and southern sectors. The northern sector includes the three northern regions (Northern, Upper East and Upper Westen regions) and the seven remaining regions form the southern sector. Subjects Women aged 15–49 years whose BMI values were available. Results The overall prevalence of obesity and overweight increased from 25·5 % in 2003 to 30·5 % in 2008. Obesity varied directly with age from 20 to 44 years. Women with higher education had the highest rate of obesity. Obesity was more common among women from wealthy households compared to women from poor households. Conclusions Obesity and overweight were found to be more common among older women, urban women, married women, women with higher education and women from rich households. Adoption of healthy lifestyles and the implementation of policies that promote healthy living can help reduce the prevalence of overweight and obesity.
Reliable User Profile Analytics and Discovery on Social Networks

In this paper, we introduce heterogeneous methods to analyze and discover user profiles on Online Social Networks (OSNs). We are the first to investigate such methods to profile users on multiple OSNs (Facebook, Twitter, Google+, etc.). In addition, we perform reliable analytics, i.e., users in the datasets are identical. Deeply speaking, if we have a dataset of n number of user profiles on Facebook, we do not analyze n different profiles on corresponding OSN. However, we first perform a user Profile Matching (PM) task from a seed dataset (Facebook for instance) and then match these profiles inside this dataset to their corresponding profiles on other OSNs, then we start our User Profile Analysis and Discovery task (UPAD). We show that our UPAD methods uncover very interesting facts about OSN users.
Medical Cost of Colorectal Cancer Services in Serbia Between 2014 and 2017: National Data Report

Department of Surgery, Clinical Centre “Dr. Dragisa Misovic”, Belgrade, Serbia, 2 Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia, Centre for Clinical Pharmacology, Military Medical Academy, Medical Faculty of Military Medical Academy, University of Defence, Belgrade, Serbia, Department of Global Health Economics and Policy, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia, 5 Institute for Orthopedic and Surgical Diseases “Banjica”, Belgrade, Serbia, Department for Plastic Surgery, Faculty of Medicine, Institute for Mother and Child Health Care of Serbia “Dr. Vukan Cupic”, University of Belgrade, Belgrade, Serbia, Clinical Centre “Dr. Dragisa Misovic”, Belgrade, Serbia, 8 Faculty of Medicine, University of Belgrade, Belgrade, Serbia
Proposal of Enhancement for Quartz Digital Signature

Today, we see a large dependence on systems developed with cryptography. Especially in terms of public key cryptosystems, which are widely used on the Internet. However, public key cryptography was threatened and new sources began to be investigated when Shor in 1997 developed a polynomial time algorithm for factoring integers and to compute the discrete logarithm with a quantum computer. In this context, Patarin proposed Hidden Field Equations (HFE), a trapdoor based on MQ (Multivariate Quadratic) and IP (Isomorphism of Polynomials) problems. Such problems are not affected by the Shor algorithm, moreover MQ Problem was proved by Patarin and Goubin to be NP-complete. Despite the basic HFE has been broken, there are variants that are secure, obtained by a generic modification. The Quartz - digital signature scheme based on HFEv-, with special choice of parameters - is a good example of this resistance to algebraic attacks aimed at the recovery of the private key, because even today it remains secure. Furthermore, it also generates short signatures. However, Joux and Martinet, based on axioms of Birthday Paradox Attack, proved that Quartz is malleable, showing that if the adversary has a valid pair (message, signature), he can get a second signature with 250 computations and 250 calls to the signing oracle, so that the estimated current security standards are at least 2112. Thus, based on Quartz, we present a new digital signature scheme, achieving the adaptive chosen message attacks that make calls to the random oracle, with a security level estimated at 2112. Our cryptosystem also provides an efficiency gain in signature verification algorithm and vector initializations that will be used for signing and verification algorithms. Furthermore we provide an implementation of Original Quartz and Enhanced Quartz in the Java programming language.
Indoor Towers, DPIs, and More People in Parks at Night: New Trends in Mobile Phone Location Research

In this keynote, I will present some results we have obtained using location data from mobile phones interacting with information (news) websites, mobile apps like Pokemon GO and Twitter, and large physical spaces like shopping malls. I will draw some conclusions and generally discuss about the properties of mobile phone data for location-based research to finally close with some remarks about privacy and data security.
Knowledge-Driven Stock Trend Prediction and Explanation via Temporal Convolutional Network

Deep neural networks have achieved promising results in stock trend prediction. However, most of these models have two common drawbacks, including (i) current methods are not sensitive enough to abrupt changes of stock trend, and (ii) forecasting results are not interpretable for humans. To address these two problems, we propose a novel Knowledge-Driven Temporal Convolutional Network (KDTCN) for stock trend prediction and explanation. Firstly, we extract structured events from financial news, and utilize external knowledge from knowledge graph to obtain event embeddings. Then, we combine event embeddings and price values together to forecast stock trend. We evaluate the prediction accuracy to show how knowledge-driven events work on abrupt changes. We also visualize the effect of events and linkage among events based on knowledge graph, to explain why knowledge-driven events are common sources of abrupt changes. Experiments demonstrate that KDTCN can (i) react to abrupt changes much faster and outperform state-of-the-art methods on stock datasets, as well as (ii) facilitate the explanation of prediction particularly with abrupt changes.
Correlation between circulating cell‐free PIK3CA tumor DNA levels and treatment response in patients with PIK3CA‐mutated metastatic breast cancer

Liquid biopsies focusing on the analysis of cell‐free circulating tumor DNA (ctDNA) may have important clinical implications for personalized medicine, including early detection of cancer, therapeutic guidance, and monitoring of recurrence. Mutations in the oncogene, PIK3CA, are frequently observed in breast cancer and have been suggested as a predictive biomarker for PI3K‐selective inhibitor treatment. In this study, we analyzed the presence of PIK3CA mutations in formalin‐fixed, paraffin‐embedded, metastatic tissue and corresponding ctDNA from serum of patients with advanced breast cancer using a highly sensitive, optimized droplet digital PCR (ddPCR) assay. We found 83% of patients with PIK3CA mutation in the metastatic tumor tissue also had detectable PIK3CA mutations in serum ctDNA. Patients lacking the PIK3CA mutation in corresponding serum ctDNA all had nonvisceral metastatic disease. Four patients with detectable PIK3CA‐mutated ctDNA were followed with an additional serum sample during oncological treatment. In all cases, changes in PIK3CA ctDNA level correlated with treatment response. Our results showed high concordance between detection of PIK3CA mutations in tumor tissue and in corresponding serum ctDNA and suggest that serum samples from patients with advanced breast cancer and ddPCR may be used for PIK3CA mutation status assessment to complement imaging techniques as an early marker of treatment response.
Detection of Gray Mold Leaf Infections Prior to Visual Symptom Appearance Using a Five-Band Multispectral Sensor

Fungal leaf diseases cause economically important damage to crop plants. Protective treatments help producers to secure good quality crops. In contrast, curative treatments based on visually detectable symptoms are often riskier and less effective because diseased crop plants may develop disease symptoms too late for curative treatments. Therefore, early disease detection prior symptom development would allow an earlier, and therefore more effective, curative management of fungal diseases. Using a five-lens multispectral imager, spectral reflectance of green, blue, red, near infrared (NIR, 840 nm), and rededge (RE, 720 nm) was recorded in time-course experiments of detached tomato leaves inoculated with the fungus Botrytis cinerea and mock infection solution. Linear regression models demonstrate NIR and RE as the two most informative spectral data sets to differentiate pathogen- and mock-inoculated leaf regions of interest (ROI). Under controlled laboratory conditions, bands collecting NIR and RE irradiance showed a lower reflectance intensity of infected tomato leaf tissue when compared with mock-inoculated leaves. Blue and red channels collected higher intensity values in pathogen- than in mock-inoculated ROIs. The reflectance intensities of the green band were not distinguishable between pathogen- and mock infected ROIs. Predictions of linear regressions indicated that gray mold leaf infections could be identified at the earliest at 9 h post infection (hpi) in the most informative bands NIR and RE. Re-analysis of the imagery taken with NIR and RE band allowed to classify infected tissue.
Learning Task-Specific City Region Partition

The proliferation of publicly accessible urban data provide new insights on various urban tasks. A frequently used approach is to treat each region as a data sample and build a model over all the regions to observe the correlations between urban features (e.g., demographics) and the target variable (e.g., crime count). To define regions, most existing studies use fixed grids or pre-defined administrative boundaries (e.g., census tracts or community areas). In reality, however, definitions of regions should be different depending on tasks (e.g., regional crime count prediction vs. real estate prices estimation). In this paper, we propose a new problem of task-specific city region partitioning, aiming to find the best partition in a city w.r. t. a given task. We prove this is an NP-hard search problem with no trivial solution. To learn the partition, we first study two variants of Markov Chain Monte Carlo (MCMC). We further propose a reinforcement learning scheme for effective sampling the search space. We conduct experiments on two real datasets in Chicago (i.e., crime count and real estate price) to demonstrate the effectiveness of our proposed method.
A Link-based Approach to Detect Media Bias in News Websites

News websites are currently one of the main sources of information. Like traditional media, these sources can have a bias in how they report news. This media bias can influence how people perceive events, political decisions, or discussions. In this paper, we describe a link-based approach to identify news websites with the same political orientation, i.e., characterize the bias of news websites, using network analysis techniques. After constructing a graph from a few seeds with previously known bias, we show that a community detection algorithm can identify groups formed by sources with the same political orientation.
Small-Molecule SB216763-Loaded Microspheres Repair Peripheral Nerve Injury in Small Gap Tubulization

Peripheral nerve injury has yet to be fully resolved because of its complicated pathological process. SB216763 is a small molecular compound that can enhance the remyelination of peripheral nerves by inhibiting glycogen synthase kinase-3β (GSK3β). GSK-3β inhibitor stimulates myelin gene expression and restores the myelin structure. Herein, we presented the effect of integrating small gap tubulization with SB216763-loaded microspheres by using a chitosan conduit. In vitro, SB216763 could promote neurite growth of dorsal root ganglia. In vivo studies showed that SB216763 increased the number of myelinated axons and the thickness of myelin sheaths. Electrophysiological examination and sciatic functional index results also indirectly indicated the role of SB216763 in repairing peripheral nerve injury. SB216763 promoted the recovery of muscle function. Therefore, combining SB216763-loaded PLGA microspheres with conduit small gap tubulization shows potential for applications in repairing peripheral nerve injury.
New Approach of Big Data and Education: Any Term Must Be in the Characters Chessboard as a Super Matrix

The purpose of this paper is to introduce a new approach that must cover all the terms. Therefore, people's educational process is like making a variety of choices in a super-chessboard of language or a matrix composed of words formally. The method of redemption is: First, construct the chessboard, and then, through human-computer interaction and collaboration, generate massive amounts of big data, including various terms representing knowledge, and finally, through machine learning and man-machine interactive to analyze, compare, and query or reuse any of these terms. The result: an accurate query of terms, which can be automatically queried in multiple ways through bilingual or multi-lingual converters. The significance is that the method and its results can be used not only for machine-assisted instruction in the network environment, but also for machine-assisted intelligent text analysis and knowledge module finishing in the network environment, thus opening up view of big data and education. The new approach, because any term must be in the word matrix, each user and its agents query them very accurately and efficiently.
Cell Outage Detection Based on Handover Statistics

This letter presents a novel cell outage detection algorithm based on incoming handovers statistics. The main advantage of the proposed algorithm is that it uses neighbor measurements that allow to detect outage in two cases. First, when the cell in outage is able to report performance indicators; second, when these indicators are not available because the base station is affected. To evaluate the proposed algorithm and compare it with other approaches, a set of tests has been carried out using an LTE simulator and in a live LTE network.
Top Challenges from the first Practical Online Controlled Experiments Summit

Online controlled experiments (OCEs), also known as A/B tests, have become ubiquitous in evaluating the impact of changes made to software products and services. While the concept of online controlled experiments is simple, there are many practical challenges in running OCEs at scale. To understand the top practical challenges in running OCEs at scale and encourage further academic and industrial exploration, representatives with experience in large-scale experimentation from thirteen different organizations (Airbnb, Amazon, Booking.com, Facebook, Google, LinkedIn, Lyft, Microsoft, Netflix, Twitter, Uber, Yandex, and Stanford University) were invited to the first Practical Online Controlled Experiments Summit. All thirteen organizations sent representatives. Together these organizations have tested more than one hundred thousand experiment treatments last year. Thirty-four experts from these organizations participated in the summit in Sunnyvale, CA, USA on December 13-14, 2018. While there are papers from individual organizations on some of the challenges and pitfalls in running OCEs at scale, this is the first paper to provide the top challenges faced across the industry for running OCEs at scale and some common solutions.
Querying Data Lakes using Spark and Presto

Squerall is a tool that allows the querying of heterogeneous, large-scale data sources by leveraging state-of-the-art Big Data processing engines: Spark and Presto. Queries are posed on-demand against a Data Lake, i.e., directly on the original data sources without requiring prior data transformation. We showcase Squerall's ability to query five different data sources, including inter alia the popular Cassandra and MongoDB. In particular, we demonstrate how it can jointly query heterogeneous data sources, and how interested developers can easily extend it to support additional data sources. Graphical user interfaces (GUIs) are offered to support users in (1) building intra-source queries, and (2) creating required input files.
Human Mobility from theory to practice:Data, Models and Applications

The inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mobility and machine learning models applicable to specific problems. We review the state of the art of five main aspects in human mobility: (1) human mobility data landscape; (2) key measures of individual and collective mobility; (3) generative models at the level of individual, population and mixture of the two; (4) next location prediction algorithms; (5) applications for social good. For each aspect, we show experiments and simulations using the Python library ”scikit-mobility” developed by the presenters of the tutorial.
FdGars: Fraudster Detection via Graph Convolutional Networks in Online App Review System

Online review system enables users to submit reviews about the products. However, the openness of Internet and monetary rewards for crowdsourcing tasks stimulate a large number of fraudulent users to write fake reviews and post advertisements to interfere the rank of apps. Existing methods for detecting spam reviews have been successful but they usually aims at e-commerce (e.g. Amazon, eBay) and recommendation (e.g. Yelp, Dianping) systems. Since the behaviors of fraudulent users are complexity and varying across different review platforms, existing methods are not suitable for fraudster detection in online app review system. To shed light on this question, we are among the first to analyze the intentions of fraudulent users from different review platforms and categorize them by utilizing characteristics of contents (similarity, special symbols) and behaviors (timestamps, device, login status). With a comprehensive analysis of spamming activities and relationships between normal and malicious users, we design and present FdGars, the first graph convolutional network approach for fraudster detection in online app review system. Then we evaluate FdGars on real-world large-scale dataset (with 82,542 nodes and 42,433,134 edges) from Tencent App Store. The result demonstrates that F1-score of FdGars can achieve 0.938+, which outperforms several baselines and state-of-art fraudsters detecting methods. Moreover, we deploy FdGars on Tencent Beacon Anti-Fraud Platform to show its effectiveness and scalability. To the best of our knowledge, this is the first work to use graph convolutional networks for fraudster detection in the large-scale online app review system. It is worth to mention that FdGars can uncover malicious accounts even the data lack of labels in anti-spam tasks.
Random Abstract Simplicial Complexes Reduction

Random abstract simplicial complex representation provides a mathematical description of wireless networks and their topology. In order to reduce the energy consumption in this type of network, we intend to reduce the number of network nodes without modifying neither the connectivity nor the coverage of the network. In this paper, we present a reduction algorithm that lower the number of points of an abstract simplicial complex in an optimal order while maintaining its topology. Then, we study the complexity of such an algorithm for a network simulated by a binomial point process and represented by a Vietoris-Rips complex.
Protective effects of 3′‐deoxy‐4‐O‐methylepisappanol from Caesalpinia sappan against glutamate‐induced neurotoxicity in primary cultured rat cortical cells

To examine the neuroprotective effects of Caesalpinia sappan L. , we tested its protection against the glutamate‐induced neurotoxicity in primary cortical cultured neurons. We found that an aqueous extract of this medicinal plant exhibited significant protection against glutamate‐induced toxicity in primary cultured rat cortical cells. In order to clarify the neuroprotective mechanism(s) of this observed effect, isolation was performed to seek and identify active fractions and components. By such fractionation, two known compounds – sappanchalcone and 3′‐deoxy‐4‐O‐methylepisappanol – were isolated from the methanol extracts from the air‐dried and chipped C. sappan. Among these two compounds, 3′‐deoxy‐4‐O‐methylepisappanol exhibited significant neuroprotective activities against glutamate‐induced toxicity, exhibiting cell viability of about 50%, at concentrations ranging from 0.1 μM to 10 μM. Therefore, the neuroprotective effect of C. sappan might be due to the inhibition of glutamate‐induced toxicity by the protosappanin derivative it contains. Copyright © 2009 John Wiley & Sons, Ltd.