OpenRaiser/Intern-Atlas huggingface dataset:
Privacy-preserving Medical Treatment System through Nondeterministic Finite Automata
Venue: arXiv cs.CR 2020
Year: 2020

In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for the remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent the medical model, which includes illness states, treatment methods and state transitions caused by exerting different treatment methods. A medical model is encrypted and outsourced to the cloud to deliver telemedicine services. Using P-Med, patient-centric diagnosis and treatment can be made on-the-fly while protecting the confidentiality of a patient's illness states and treatment recommendation results. Moreover, a new privacy-preserving NFA evaluation method is given in P-Med to get a confidential match result for the evaluation of an encrypted NFA and an encrypted data set, which avoids the cumbersome inner state transition determination. We demonstrate that P-Med realizes treatment procedure recommendation without privacy leakage to unauthorized parties. We conduct extensive experiments and analyses to evaluate efficiency.
Deep learning approach to describe and classify fungi microscopic images
Venue: arXiv cs.CV 2020
Year: 2020

Preliminary diagnosis of fungal infections can rely on microscopic examination. However, in many cases, it does not allow unambiguous identification of the species by microbiologist due to their visual similarity. Therefore, it is usually necessary to use additional biochemical tests. That involves additional costs and extends the identification process up to 10 days. Such a delay in the implementation of targeted therapy may be grave in consequence as the mortality rate for immunosuppressed patients is high. In this paper, we apply a machine learning approach based on deep neural networks and Fisher Vector (advanced bag-of-words method) to classify microscopic images of various fungi species. Our approach has the potential to make the last stage of biochemical identification redundant, shortening the identification process by 2-3 days, and reducing the cost of the diagnosis.
Functional Space Variational Inference for Uncertainty Estimation in Computer Aided Diagnosis
Venue: arXiv cs.LG 2020
Year: 2020

Deep neural networks have revolutionized medical image analysis and disease diagnosis. Despite their impressive performance, it is difficult to generate well-calibrated probabilistic outputs for such networks, which makes them uninterpretable black boxes. Bayesian neural networks provide a principled approach for modelling uncertainty and increasing patient safety, but they have a large computational overhead and provide limited improvement in calibration. In this work, by taking skin lesion classification as an example task, we show that by shifting Bayesian inference to the functional space we can craft meaningful priors that give better calibrated uncertainty estimates at a much lower computational cost.
Embedding Culture and Grit in the Technology Acceptance Model (TAM) for Higher Education
Venue: arXiv cs.CY 2020
Year: 2020

The implementors of learning technologies within education environments often follow strategies that assume the educational environment within which they are being introduced is culturally neutral. A comprehensive literature review including 150 papers on educational technology challenges was undertaken. The purpose of this review is explore different contextual challenges to the adoption of educational technology in the higher education sector. The cultural factors that define the key stakeholders (e.g., teachers, lectures, students and support staff) are often ignored when the implementation processes are undertaken. Furthermore, it is often assumed that the personnel responsible for the implementation are also culturally neutral and do not possess any attributes unique to their culture. It has been shown that cultural factors may significantly influence the implementation of learning technologies and to design strategies that fail to consider factors may limit their efficiency and effectiveness. The challenges are interrelated and based on the findings, this review proposes a conceptual framework by integrating culture and grit into the Technology Acceptance Model(TAM) for implementing educational technology in higher education. The framework will be useful to guide both practice and research.
MPSUM: Entity Summarization with Predicate-based Matching
Venue: arXiv cs.IR 2020
Year: 2020

With the development of Semantic Web, entity summarization has become an emerging task to generate concrete summaries for real world entities. To solve this problem, we propose an approach named MPSUM that extends a probabilistic topic model by integrating the idea of predicate-uniqueness and object-importance for ranking triples. The approach aims at generating brief but representative summaries for entities. We compare our approach with the state-of-the-art methods using DBpedia and LinkedMDB datasets.The experimental results show that our work improves the quality of entity summarization.
ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
Venue: arXiv cs.IR 2020
Year: 2020

Recommender system (RS) devotes to predicting user preference to a given item and has been widely deployed in most web-scale applications. Recently, knowledge graph (KG) attracts much attention in RS due to its abundant connective information. Existing methods either explore independent meta-paths for user-item pairs over KG, or employ graph neural network (GNN) on whole KG to produce representations for users and items separately. Despite effectiveness, the former type of methods fails to fully capture structural information implied in KG, while the latter ignores the mutual effect between target user and item during the embedding propagation. In this work, we propose a new framework named Adaptive Target-Behavior Relational Graph network (ATBRG for short) to effectively capture structural relations of target user-item pairs over KG. Specifically, to associate the given target item with user behaviors over KG, we propose the graph connect and graph prune techniques to construct adaptive target-behavior relational graph. To fully distill structural information from the sub-graph connected by rich relations in an end-to-end fashion, we elaborate on the model design of ATBRG, equipped with relation-aware extractor layer and representation activation layer. We perform extensive experiments on both industrial and benchmark datasets. Empirical results show that ATBRG consistently and significantly outperforms state-of-the-art methods. Moreover, ATBRG has also achieved a performance improvement of 5.1% on CTR metric after successful deployment in one popular recommendation scenario of Taobao APP.
Malware Detection at the Microarchitecture Level using Machine Learning Techniques
Venue: arXiv cs.CR 2020
Year: 2020

Detection of malware cyber-attacks at the processor microarchitecture level has recently emerged as a promising solution to enhance the security of computer systems. Security mechanisms, such as hardware-based malware detection, use machine learning algorithms to classify and detect malware with the aid of Hardware Performance Counters (HPCs) information. The ML classifiers are fed microarchitectural data extracted from Hardware Performance Counters (HPCs), which contain behavioral data about a software program. These HPCs are captured at run-time to model the program's behavior. Since the amount of HPCs are limited per processor, many techniques employ feature reduction to reduce the amount of HPCs down to the most essential attributes. Previous studies have already used binary classification to implement their malware detection after doing extensive feature reduction. This results in a simple identification of software being either malware or benign. This research comprehensively analyzes different hardware-based malware detectors by comparing different machine learning algorithms' accuracy with binary and multi-class classification models. Our experimental results indicate that when compared to complex machine learning models (e. g. Neural Network and Logistic), light-weight J48 and JRip algorithms perform better in detecting the malicious patterns even with the introduction of multiple types of malware. Although their detection accuracy slightly lowers, their robustness (Area Under the Curve) is still high enough that they deliver a reasonable false positive rate.
On the Problem of $p_1^{-1}$ in Locality-Sensitive Hashing
Venue: arXiv cs.DS 2020
Year: 2020

A Locality-Sensitive Hash (LSH) function is called $(r,cr,p_1,p_2)$-sensitive, if two data-points with a distance less than $r$ collide with probability at least $p_1$ while data points with a distance greater than $cr$ collide with probability at most $p_2$. These functions form the basis of the successful Indyk-Motwani algorithm (STOC 1998) for nearest neighbour problems. In particular one may build a $c$-approximate nearest neighbour data structure with query time $\tilde O(n^ρ/p_1)$ where $ρ=\frac{\log1/p_1}{\log1/p_2}\in(0,1)$. That is, sub-linear time, as long as $p_1$ is not too small. This is significant since most high dimensional nearest neighbour problems suffer from the curse of dimensionality, and can't be solved exact, faster than a brute force linear-time scan of the database. Unfortunately, the best LSH functions tend to have very low collision probabilities, $p_1$ and $p_2$. Including the best functions for Cosine and Jaccard Similarity. This means that the $n^ρ/p_1$ query time of LSH is often not sub-linear after all, even for approximate nearest neighbours! In this paper, we improve the general Indyk-Motwani algorithm to reduce the query time of LSH to $\tilde O(n^ρ/p_1^{1-ρ})$ (and the space usage correspondingly.) Since $n^ρp_1^{ρ-1} < n \Leftrightarrow p_1 > n^{-1}$, our algorithm always obtains sublinear query time, for any collision probabilities at least $1/n$. For $p_1$ and $p_2$ small enough, our improvement over all previous methods can be \emph{up to a factor $n$} in both query time and space. The improvement comes from a simple change to the Indyk-Motwani algorithm, which can easily be implemented in existing software packages.
Stable Style Transformer: Delete and Generate Approach with Encoder-Decoder for Text Style Transfer
Venue: arXiv cs.CL 2020
Year: 2020

Text style transfer is the task that generates a sentence by preserving the content of the input sentence and transferring the style. Most existing studies are progressing on non-parallel datasets because parallel datasets are limited and hard to construct. In this work, we introduce a method that follows two stages in non-parallel datasets. The first stage is to delete attribute markers of a sentence directly through a classifier. The second stage is to generate a transferred sentence by combining the content tokens and the target style. We experiment on two benchmark datasets and evaluate context, style, fluency, and semantic. It is difficult to select the best system using only these automatic metrics, but it is possible to select stable systems. We consider only robust systems in all automatic evaluation metrics to be the minimum conditions that can be used in real applications. Many previous systems are difficult to use in certain situations because performance is significantly lower in several evaluation metrics. However, our system is stable in all automatic evaluation metrics and has results comparable to other models. Also, we compare the performance results of our system and the unstable system through human evaluation. Our code and data are available at the link (https://github.com/rungjoo/Stable-Style-Transformer).
AutoMSC: Automatic Assignment of Mathematics Subject Classification Labels
Venue: arXiv cs.DL 2020
Year: 2020

Authors of research papers in the fields of mathematics, and other math-heavy disciplines commonly employ the Mathematics Subject Classification (MSC) scheme to search for relevant literature. The MSC is a hierarchical alphanumerical classification scheme that allows librarians to specify one or multiple codes for publications. Digital Libraries in Mathematics, as well as reviewing services, such as zbMATH and Mathematical Reviews (MR) rely on these MSC labels in their workflows to organize the abstracting and reviewing process. Especially, the coarse-grained classification determines the subject editor who is responsible for the actual reviewing process. In this paper, we investigate the feasibility of automatically assigning a coarse-grained primary classification using the MSC scheme, by regarding the problem as a multi-class classification machine learning task. We find that our method achieves an (F_1)-score of over 77%, which is remarkably close to the agreement of zbMATH and MR ((F_1)-score of 81%). Moreover, we find that the method's confidence score allows for reducing the effort by 86% compared to the manual coarse-grained classification effort while maintaining a precision of 81% for automatically classified articles.
Learnability of Timescale Graphical Event Models
Venue: arXiv cs.LG 2020
Year: 2020

This technical report tries to fill a gap in current literature on Timescale Graphical Event Models. I propose and evaluate different heuristics to determine hyper-parameters during the structure learning algorithm and refine an existing distance measure. A comprehensive benchmark on synthetic data will be conducted allowing conclusions about the applicability of the different heuristics.
User behavior and token adoption on ERC20
Venue: arXiv cs.SI 2020
Year: 2020

Cryptocurrencies and Blockchain-based technologies are disrupting all markets. While the potential of such technologies remains to be seen, there is a current need to understand emergent patterns of user behavior and token adoption in order to design future products. In this paper we analyze the social dynamics taking place during one arbitrary day on the ERC20 platform. We characterize the network of token transactions among agents. We show heterogeneous profiles of user behavior, portfolio diversity, and token adoption. While most users are specialized in transacting with a few tokens, those that have diverse portfolios are bridging across large parts of the network and may jeopardize the system stability. We believe this work to be a foundation for unveiling the usage dynamics of crypto-currencies networks.
Keyed Non-Parametric Hypothesis Tests
Venue: arXiv cs.CR 2020
Year: 2020

The recent popularity of machine learning calls for a deeper understanding of AI security. Amongst the numerous AI threats published so far, poisoning attacks currently attract considerable attention. In a poisoning attack the opponent partially tampers the dataset used for learning to mislead the classifier during the testing phase. This paper proposes a new protection strategy against poisoning attacks. The technique relies on a new primitive called keyed non-parametric hypothesis tests allowing to evaluate under adversarial conditions the training input's conformance with a previously learned distribution $\mathfrak{D}$. To do so we use a secret key $κ$ unknown to the opponent. Keyed non-parametric hypothesis tests differs from classical tests in that the secrecy of $κ$ prevents the opponent from misleading the keyed test into concluding that a (significantly) tampered dataset belongs to $\mathfrak{D}$.
Speaker and Posture Classification using Instantaneous Intraspeech Breathing Features
Venue: arXiv cs.SD 2020
Year: 2020

Acoustic features extracted from speech are widely used in problems such as biometric speaker identification and first-person activity detection. However, the use of speech for such purposes raises privacy issues as the content is accessible to the processing party. In this work, we propose a method for speaker and posture classification using intraspeech breathing sounds. Instantaneous magnitude features are extracted using the Hilbert-Huang transform (HHT) and fed into a CNN-GRU network for classification of recordings from the open intraspeech breathing sound dataset, BreathBase, that we collected for this study. Using intraspeech breathing sounds, 87% speaker classification, and 98% posture classification accuracy were obtained.
Verification of the IBOS Browser Security Properties in Reachability Logic
Venue: arXiv cs.LO 2020
Year: 2020

This paper presents a rewriting logic specification of the Illinois Browser Operating System (IBOS) and defines several security properties, including the same-origin policy (SOP) in reachability logic. It shows how these properties can be deductively verified using our constructor-based reachability logic theorem prover. This paper also highlights the reasoning techniques used in the proof and three modularity principles that have been crucial to scale up and complete the verification effort.
A Big Data Based Framework for Executing Complex Query Over COVID-19 Datasets (COVID-QF)
Venue: arXiv cs.DB 2020
Year: 2020

COVID-19's rapid global spread has driven innovative tools for Big Data Analytics. These have guided organizations in all fields of the health industry to track and minimized the effects of virus. Researchers are required to detect coronaviruses through artificial intelligence, machine learning, and natural language processing, and to gain a complete understanding of the disease. COVID-19 takes place in different countries in the world, with which only big data application and the work of NOSQL databases are suitable. There is a great number of platforms used for processing NOSQL Databases model like: Spark, H2O and Hadoop HDFS/MapReduce, which are proper to control and manage the enormous amount of data. Many challenges faced by large applications programmers, especially those that work on the COVID-19 databases through hybrid data models through different APIs and query. In this context, this paper proposes a storage framework to handle both SQL and NOSQL databases named (COVID-QF) for COVID-19 datasets in order to treat and handle the problems caused by virus spreading worldwide by reducing treatment times. In case of NoSQL database, COVID-QF uses Hadoop HDFS/Map Reduce and Apache Spark. The COVID-QF consists of three Layers: data collection layer, storage layer, and query Processing layer. The data is collected in the data collection layer. The storage layer divides data into collection of data-saving and processing blocks, and it connects the Connector of the spark with different databases engine to reduce time of saving and retrieving. While the Processing layer executes the request query and sends results. The proposed framework used three datasets increased for time for COVID-19 data (COVID-19-Merging, COVID-19-inside-Hubei and COVID-19-ex-Hubei) to test experiments of this study. The results obtained insure the superiority of the COVID-QF framework.
A Physical Channel Model for Wired Nano-Communication Networks
Venue: arXiv cs.ET 2020
Year: 2020

In this paper, we propose a new end-to-end system for wired nano-communication networks using a self-assembled polymer. The self-assembly of a polymer creates a channel between the transmitter and the receiver in the form of a conductive nanowire that uses electrons as carriers of information. We derive the channel's analytical model and its master equation to study the dynamic process of the polymer self-assembly. We validate the analytical model with numerical and Monte-Carlo simulations. Then, we approximate the master equation by a one-dimensional Fokker-Planck equation and we solve this equation analytically and numerically. We formulate the expressions of the polymer elongation rate, its diffusion coefficient and the nullcline to study the distribution and the stability of the self-assembled nanowire. This study shows promising results for realizing stable polymer-based wired nanonetworks that can achieve high throughput.
Incidental Supervision: Moving beyond Supervised Learning
Venue: arXiv cs.LG 2020
Year: 2020

Machine Learning and Inference methods have become ubiquitous in our attempt to induce more abstract representations of natural language text, visual scenes, and other messy, naturally occurring data, and support decisions that depend on it. However, learning models for these tasks is difficult partly because generating the necessary supervision signals for it is costly and does not scale. This paper describes several learning paradigms that are designed to alleviate the supervision bottleneck. It will illustrate their benefit in the context of multiple problems, all pertaining to inducing various levels of semantic representations from text.
Path Imputation Strategies for Signature Models of Irregular Time Series
Venue: arXiv cs.LG 2020
Year: 2020

The signature transform is a 'universal nonlinearity' on the space of continuous vector-valued paths, and has received attention for use in machine learning on time series. However, real-world temporal data is typically observed at discrete points in time, and must first be transformed into a continuous path before signature techniques can be applied. We make this step explicit by characterising it as an imputation problem, and empirically assess the impact of various imputation strategies when applying signature-based neural nets to irregular time series data. For one of these strategies, Gaussian process (GP) adapters, we propose an extension~(GP-PoM) that makes uncertainty information directly available to the subsequent classifier while at the same time preventing costly Monte-Carlo (MC) sampling. In our experiments, we find that the choice of imputation drastically affects shallow signature models, whereas deeper architectures are more robust. Next, we observe that uncertainty-aware predictions (based on GP-PoM or indicator imputations) are beneficial for predictive performance, even compared to the uncertainty-aware training of conventional GP adapters. In conclusion, we have demonstrated that the path construction is indeed crucial for signature models and that our proposed strategy leads to competitive performance in general, while improving robustness of signature models in particular.
Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness
Venue: arXiv cs.LG 2020
Year: 2020

Machine learning models are increasingly being used in important decision-making software such as approving bank loans, recommending criminal sentencing, hiring employees, and so on. It is important to ensure the fairness of these models so that no discrimination is made based on protected attribute (e.g., race, sex, age) while decision making. Algorithms have been developed to measure unfairness and mitigate them to a certain extent. In this paper, we have focused on the empirical evaluation of fairness and mitigations on real-world machine learning models. We have created a benchmark of 40 top-rated models from Kaggle used for 5 different tasks, and then using a comprehensive set of fairness metrics, evaluated their fairness. Then, we have applied 7 mitigation techniques on these models and analyzed the fairness, mitigation results, and impacts on performance. We have found that some model optimization techniques result in inducing unfairness in the models. On the other hand, although there are some fairness control mechanisms in machine learning libraries, they are not documented. The mitigation algorithm also exhibit common patterns such as mitigation in the post-processing is often costly (in terms of performance) and mitigation in the pre-processing stage is preferred in most cases. We have also presented different trade-off choices of fairness mitigation decisions. Our study suggests future research directions to reduce the gap between theoretical fairness aware algorithms and the software engineering methods to leverage them in practice.