Miracle-dz/UniFAR-FTUs huggingface dataset:
2D Materials in Development of Electrochemical Point-of-Care Cancer Screening Devices

Effective cancer treatment requires early detection and monitoring the development progress in a simple and affordable manner. Point-of care (POC) screening can provide a portable and inexpensive tool for the end-users to conveniently operate test and screen their health conditions without the necessity of special skills. Electrochemical methods hold great potential for clinical analysis of variety of chemicals and substances as well as cancer biomarkers due to their low cost, high sensitivity, multiplex detection ability, and miniaturization aptitude. Advances in two-dimensional (2D) material-based electrochemical biosensors/sensors are accelerating the performance of conventional devices toward more practical approaches. Here, recent trends in the development of 2D material-based electrochemical biosensors/sensors, as the next generation of POC cancer screening tools, are summarized. Three cancer biomarker categories, including proteins, nucleic acids, and some small molecules, will be considered. Various 2D materials will be introduced and their biomedical applications and electrochemical properties will be given. The role of 2D materials in improving the performance of electrochemical sensing mechanisms as well as the pros and cons of current sensors as the prospective devices for POC screening will be emphasized. Finally, the future scopes of implementing 2D materials in electrochemical POC cancer diagnostics for the clinical translation will be discussed.
Oral vaccination with the porcine circovirus type 2 (PCV‐2) capsid protein expressed by Lactococcus lactis induces a specific immune response against PCV‐2 in mice

Porcine circovirus type 2 (PCV2) can cause postweaning, multisystemic wasting syndrome in pigs, which leads to enormous losses in the swine industry worldwide. Here, a genetically engineered Lactococcus strain expressing the main protective antigen of PCV2, the Cap protein, was developed to act against PCV2 infection as an oral vaccine.
Diagnostic accuracy of digital X-ray radiogrammetry on hand bone loss for patients with rheumatoid arthritis

Abstract Background: This study will aim to evaluate the diagnostic accuracy of digital X-ray radiogrammetry (DXR) on hand bone loss (HBL) for rheumatoid arthritis (RA). Methods: In this study, we will search the literature from PubMed, EMBASE, Cochrane Library, PsycINFO, Web of Science, Google Scholar, Cumulative Index to Nursing and Allied Health Literature, Allied and Complementary Medicine Database, Chinese Biomedical Literature Database, China National Knowledge Infrastructure, and WANFANG from the inception to June 1, 2019 without language restrictions. All case–controlled studies on assessing diagnostic accuracy of DXR on HBL for diagnosis of RA will be included. Quality Assessment of Diagnostic Accuracy Studies tool will be used for eligible studies. We will apply RevMan V.5. 3 software and Stata V.12. 0 software for statistical analysis. Results: We will evaluate diagnostic accuracy of DXR on HBL in patients with RA by assessing the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio. Conclusion: This study will detect the diagnostic accuracy of DXR evaluation on HBL in patients with RA. Systematic review registration: PROSPERO CRD42019139489.
Site-specific sequential protein labeling catalyzed by a single recombinant ligase.

Protein ligases of defined substrate specificity are versatile tools for protein engineering. Upon completion of the reaction, the products of currently reported protein ligases contain the amino acid sequence that is recognized by that same ligase, resulting in repeated cycles of ligation and hydrolysis as competing reactions. Thus, previous efforts to sequentially label proteins at distinct positions required ligases of orthogonal specificity. A recombinant Oldenlandia affinis asparaginyl endopeptidase, OaAEP1, is promiscuous for incoming nucleophiles. This promiscuity enabled us to define a nucleophile composed of natural amino acids that is ligated efficiently to the substrate, yet yields a product that is poorly recognized by OaAEP1. Proteins modified with an efficient recognition module could be readily modified to yield a defined product bearing a cleavage-resistant motif, whereas proteins containing this inferior recognition motif remained essentially unmodified. We demonstrate the versatility of the N- or C-terminal protein modifications obtainable with this approach and modified the N- and C-termini of a single substrate protein in a sequential, site-specific manner in excellent yield.
The Association between Obesity and Severity of Dengue Hemorrhagic Fever in Children at Wangaya General Hospital

BACKGROUND: Dengue is a mosquito-borne disease caused by any one of four closely related Dengue virus (DENV 1-4). The clinical sign Dengue virus infection can vary from mild (mild febrile illness), Dengue Fever (DF), Dengue Hemorrhagic Fever (DHF) to Dengue Hemorrhagic Fever with shock (Dengue Shock Syndrome, DSS). AIM: This study was designed to determine the relationship of obesity with the severity of Dengue Hemorrhagic Fever in children. METHODS: It is a case-control study. The data of patients were retrospectively collected from the Department of Child Health at the Wangaya General Hospital between March 2019 to May 2019. It uses consecutive sampling. The total sample of 22 children with DHF with shock and 22 children with DHF without shock were investigated. Statistical analysis has been performed by SPSS Statistics 20.0 for Mac (IBM Corp., Armonk, New York, USA). DHF positive results were compared by the Chi-square test and binary logistic regression. RESULTS: Prevalence of DHF with shock is fifty per cent’s and DHF without shock is 50%. Prevalence of obesity is 40.9%. The result of binary logistic regression analysis of obesity in children and the severity of DHF was significantly correlated with P-value 0.004 and OR = 7.734. CONCLUSION: Obesity is associated with the severity of Dengue Hemorrhagic fever in children.
The Mediating Role of Organizational Commitment on Leadership Behavior and Employee Performance

Employee contributions through performance in an organization will determine the success of the organization. Employee performance will be significant if managed properly and effectively. This study aims to explain the mediating role of organizational commitment on leadership behavior and employee performance. The sample used in this study were 70Stikom Bali employees with total sampling technique (saturated sample) because of small size population. The data obtained were analyzed by descriptive analysis and PLS. The results of the study found that leadership behavior had a positive and significant impact on employee performance. Leadership behavior is found to had a positive and significant impact on organizational commitment. Organizational commitment was also found to had a positive and significant impact on employee performance. Furthermore, organizational commitment is able to mediate partially on the leadership behavior and employee performance. Organizational commitment has a dominant influence on employee performance, where the management of Stikom Bali needs to increase employee participation to be involved in every organizational problem solving and make them feel fully part of the organization.
Eliminating Bias in Recommender Systems via Pseudo-Labeling

Addressing the non-uniform missing mechanism of rating feedback is critical to build a well-performing recommeder in the real-world systems. To tackle the challenging issue, we first define an ideal loss function that should be optimized to achieve the goal of recommendation. Then, we derive the generalization error bound of the ideal loss that alleviates the variance and the misspecification problems of the previous propensity-based methods. We further propose a meta-learning method minimizing the bound. Empirical evaluation using real-world datasets validates the theoretical findings and demonstrates the practical advantages of the proposed upper bound minimization approach.
Analyzing control flow integrity with LLVM-CFI

Control-flow hijacking attacks are used to perform malicious computations. Current solutions for assessing the attack surface after a control flow integrity (CFI) policy was applied can measure only indirect transfer averages in the best case without providing any insights w.r. t. the absolute calltarget reduction per callsite, and gadget availability. Further, tool comparison is underdeveloped or not possible at all. CFI has proven to be one of the most promising protections against control flow hijacking attacks, thus many efforts have been made to improve CFI in various ways. However, there is a lack of systematic assessment of existing CFI protections. In this paper, we present LLVM-CFI, a static source code analysis framework for analyzing state-of-the-art static CFI protections based on the Clang/LLVM compiler framework. LLVM-CFI works by precisely modeling a CFI policy and then evaluating it within a unified approach. LLVM-CFI helps determine the level of security offered by different CFI protections, after the CFI protections were deployed, thus providing an important step towards exploit creation/prevention and stronger defenses. We have used LLVM-CFI to assess eight state-of-the-art static CFI defenses on real-world programs such as Google Chrome and Apache Httpd. LLVM-CFI provides a precise analysis of the residual attack surfaces, and accordingly ranks CFI policies against each other. LLVM-CFI also successfully paves the way towards construction of COOP-like code reuse attacks and elimination of the remaining attack surface by disclosing protected calltargets under eight restrictive CFI policies.
Blockchains and Distributed Databases: a Twin Study

Blockchain has come a long way: a system that was initially proposed specifically for cryptocurrencies is now being adapted and adopted as a general-purpose transactional system. A blockchain is also a distributed system, and as such it shares some similarities with distributed database systems. Existing works that compare blockchains and distributed database systems focus mainly on high-level properties, such as security and throughput. They stop short of showing how the underlying design choices contribute to the overall differences. Our paper is to fill this important gap. In this paper, we perform a twin study of blockchains and distributed database systems as two types of transactional systems. We propose a taxonomy that helps illustrate their similarities and differences. In particular, we compare the systems along four dimensions: replication, concurrency, storage, and sharding. We discuss how the design choices have been driven by the system's goals: blockchain's goal is security, whereas the distributed database's goal is performance. We then conduct an extensive and in-depth performance study on two blockchains, namely Quorum and Hyperledger Fabric, and three distributed databases, namely CockroachDB, TiDB and etcd. We demonstrate how the different design choices in the four dimensions lead to different performance. In addition, we show that for most workloads, blockchain's performance is still lagging far behind that of a distributed database. However, the gap is not as significant as previously reported, and under high contention or constrained workloads, blockchains and databases are even comparable. Our work provides a framework for exploring the design space of hybrid database-blockchain systems.
Chinese electronic health record analysis for recommending medical treatment solutions withNLP and unsupervised learning

Electronic health record (EHR) analysis has become increasingly important in improving the quality of human healthcare. To leverage the full insights from the big EHRs, it is very important to define some application scenarios for which the relevant data can be extracted for training machine learning models to accomplish the expected goals. In this paper, we develop a system on how to recommend medical treatment solutions for patients living in the countryside and small cities when they happen to have schizophrenia but the doctors in the local hospitals do not have sufficient expertise to deal with such challenges. In the EHRs, we take the patients’ symptom descriptions as documents and then develop NLP and unsupervised machine learning techniques to analyze such documents to find the relevant and effective treatment solutions provided by medical experts. Extensive experimental results with different vector representations for documents show that the binary keyword vector representation works best to find relevant and effective medical treatment plans and solutions from the EHRs for any input symptom description.
Graph Analysis and Graph Pooling in the Spatial Domain

The spatial convolution layer which is widely used in the Graph Neural Networks (GNNs) aggregates the feature vector of each node with the feature vectors of its neighboring nodes. The GNN is not aware of the locations of the nodes in the global structure of the graph and when the local structures corresponding to different nodes are similar to each other, the convolution layer maps all those nodes to similar or same feature vectors in the continuous feature space. Therefore, the GNN cannot distinguish two graphs if their difference is not in their local structures. In addition, when the nodes are not labeled/attributed the convolution layers can fail to distinguish even different local structures. In this paper, we propose an effective solution to address this problem of the GNNs. The proposed approach leverages a spatial representation of the graph which makes the neural network aware of the differences between the nodes and also their locations in the graph. The spatial representation which is equivalent to a point-cloud representation of the graph is obtained by a graph embedding method. Using the proposed approach, the local feature extractor of the GNN distinguishes similar local structures in different locations of the graph and the GNN infers the topological structure of the graph from the spatial distribution of the locally extracted feature vectors. Moreover, the spatial representation is utilized to simplify the graph down-sampling problem. A new graph pooling method is proposed and it is shown that the proposed pooling method achieves competitive or better results in comparison with the state-of-the-art methods.
Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator

We study the variance of the REINFORCE policy gradient estimator in environments with continuous state and action spaces, linear dynamics, quadratic cost, and Gaussian noise. These simple environments allow us to derive bounds on the estimator variance in terms of the environment and noise parameters. We compare the predictions of our bounds to the empirical variance in simulation experiments.
Annotating Antisemitic Online Content. Towards an Applicable Definition of Antisemitism

Online antisemitism is hard to quantify. How can it be measured in rapidly growing and diversifying platforms? Are the numbers of antisemitic messages rising proportionally to other content or is it the case that the share of antisemitic content is increasing? How does such content travel and what are reactions to it? How widespread is online Jew-hatred beyond infamous websites and fora, and closed social media groups? However, at the root of many methodological questions is the challenge of finding a consistent way to identify diverse manifestations of antisemitism in large datasets. What is more, a clear definition is essential for building an annotated corpus that can be used as a gold standard for machine learning programs to detect antisemitic online content. We argue that antisemitic content has distinct features that are not captured adequately in generic approaches of annotation, such as hate speech, abusive language, or toxic language. We discuss our experiences with annotating samples from our dataset that draw on a ten percent random sample of public tweets from Twitter. We show that the widely used definition of antisemitism by the International Holocaust Remembrance Alliance can be applied successfully to online messages if inferences are spelled out in detail and if the focus is not on intent of the disseminator but on the message in its context. However, annotators have to be highly trained and knowledgeable about current events to understand each tweet's underlying message within its context. The tentative results of the annotation of two of our small but randomly chosen samples suggest that more than ten percent of conversations on Twitter about Jews and Israel are antisemitic or probably antisemitic. They also show that at least in conversations about Jews, an equally high number of tweets denounce antisemitism, although these conversations do not necessarily coincide.
Secondary Inputs for Measuring User Engagement in Immersive VR Education Environments

This paper presents an experiment to assess the feasibility of using secondary input data as a method of determining user engagement in immersive virtual reality (VR). The work investigates whether secondary data (biosignals) acquired from users are useful as a method of detecting levels of concentration, stress, relaxation etc. in immersive environments, and if they could be used to create an affective feedback loop in immersive VR environments, including educational contexts. A VR Experience was developed in the Unity game engine, with three different levels, each designed to expose the user in one of three different states (relaxation, concentration, stress). While in the VR Experience users physiological responses were measured using ECG and EEG sensors. After the experience users completed questionnaires to establish their perceived state during the levels, and to established the usability of the system. Next a comparison between the reported levels of emotion and the measured signals is presented, which show a strong correspondence between the two measures indicating that biosignals are a useful indicator of emotional state while in VR. Finally we make some recommendations on the practicalities of using biosensors, and design considerations for their incorporation in to a VR system, with particular focus on their integration in to task-based training and educational virtual environments.
ROMark: A Robust Watermarking System Using Adversarial Training

The availability and easy access to digital communication increase the risk of copyrighted material piracy. In order to detect illegal use or distribution of data, digital watermarking has been proposed as a suitable tool. It protects the copyright of digital content by embedding imperceptible information into the data in the presence of an adversary. The goal of the adversary is to remove the copyrighted content of the data. Therefore, an efficient watermarking framework must be robust to multiple image-processing operations known as attacks that can alter embedded copyright information. Another line of research \textit{adversarial machine learning} also tackles with similar problems to guarantee robustness to imperceptible perturbations of the input. In this work, we propose to apply robust optimization from adversarial machine learning to improve the robustness of a CNN-based watermarking framework. Our experimental results on the COCO dataset show that the robustness of a watermarking framework can be improved by utilizing robust optimization in training.
SEUSS: Rapid serverless deployment using environment snapshots

Modern FaaS systems perform well in the case of repeat executions when function working sets stay small. However, these platforms are less effective when applied to more complex, large-scale and dynamic workloads. In this paper, we introduce SEUSS (serverless execution via unikernel snapshot stacks), a new system-level approach for rapidly deploying serverless functions. Through our approach, we demonstrate orders of magnitude improvements in function start times and cacheability, which improves common re-execution paths while also unlocking previously-unsupported large-scale bursty workloads.
Latent Space Representation for Multi-Target Speaker Detection and Identification with a Sparse Dataset Using Triplet Neural Networks

We present an approach to tackle the speaker recognition problem using Triplet Neural Networks. Currently, the i-vector representation with probabilistic linear discriminant analysis (PLDA) is the most commonly used technique to solve this problem, due to high classification accuracy with a relatively short computation time. In this paper, we explore a neural network approach, namely Triplet Neural Networks (TNNs), to built a latent space for different classifiers to solve the Multi-Target Speaker Detection and Identification Challenge Evaluation 2018 (MCE 2018) dataset. This training set contains i-vectors from 3,631 speakers, with only 3 samples for each speaker, thus making speaker recognition a challenging task. When using the train and development set for training both the TNN and baseline model (i.e., similarity evaluation directly on the i-vector representation), our proposed model outperforms the baseline by 23%. When reducing the training data to only using the train set, our method results in 309 confusions for the Multi-target speaker identification task, which is 46% better than the baseline model. These results show that the representational power of TNNs is especially evident when training on small datasets with few instances available per class.
Efficient training of energy-based models via spin-glass control

We introduce a new family of energy-based probabilistic graphical models for efficient unsupervised learning. Its definition is motivated by the control of the spin-glass properties of the Ising model described by the weights of Boltzmann machines. We use it to learn the Bars and Stripes dataset of various sizes and the MNIST dataset, and show how they quickly achieve the performance offered by standard methods for unsupervised learning. Our results indicate that the standard initialization of Boltzmann machines with random weights equivalent to spin-glass models is an unnecessary bottleneck in the process of training. Furthermore, this new family allows for very easy access to low-energy configurations, which points to new, efficient training algorithms. The simplest variant of such algorithms approximates the negative phase of the log-likelihood gradient with no Markov chain Monte Carlo sampling costs at all, and with an accuracy sufficient to achieve good learning and generalization.
Generalized bounds for active subspaces

In this article, we consider scenarios in which traditional estimates for the active subspace method based on probabilistic Poincar\'e inequalities are not valid due to unbounded Poincar\'e constants. Consequently, we propose a framework that allows to derive generalized estimates in the sense that it enables to control the trade-off between the size of the Poincar\'e constant and a weaker order of the final error bound. In particular, we investigate independently exponentially distributed random variables in dimension two or larger and give explicit expressions for corresponding Poincar\'e constants showing their dependence on the dimension of the problem. Finally, we suggest possibilities for future work that aim for extending the class of distributions applicable to the active subspace method as we regard this as an opportunity to enlarge its usability.
Partial differential equation regularization for supervised machine learning

This article is an overview of supervised machine learning problems for regression and classification. Topics include: kernel methods, training by stochastic gradient descent, deep learning architecture, losses for classification, statistical learning theory, and dimension independent generalization bounds. Implicit regularization in deep learning examples are presented, including data augmentation, adversarial training, and additive noise. These methods are reframed as explicit gradient regularization.