Reviewerly/exHarmony huggingface dataset:
Machine and Deep Learning

Machine learning along with data mining comprise sub-disciplines under data science. There are several schools of machine learning, including symbolists, connectionists, revolutionaries, Bayesians, and analogizers. Machine learning with its sometimes tedious workflow differs significantly from conventional programming. Classical machine learning consists of supervised (classification and regression) and unsupervised (clustering and generalization) learning but also semi-supervised and ensemble learning. Deep learning consists of a range of methods including convolutional neural networks, recurrent neural networks, generative adversarial networks, and their derivatives. Deep reinforcement learning (such as deep Q network, or DQN) is becoming a valuable deep learning tool in biomedicine. Evaluation of these models includes methods such as receiver operating characteristic, precision-recall curve, and the F-1 measure in the confusion matrix. Finally, issues such as explainability, bias and variance, fitting, curse of dimensionality, and correlation vs causation are discussed.
A Machine Learning Early Warning System: Multicenter Validation in Brazilian Hospitals

Early recognition of clinical deterioration is one of the main steps for reducing inpatient morbidity and mortality. The challenging task of clinical deterioration identification in hospitals lies in the intense daily routines of healthcare practitioners, in the unconnected patient data stored in the Electronic Health Records (EHRs) and in the usage of low accuracy scores. Since hospital wards are given less attention compared to the Intensive Care Unit, ICU, we hypothesized that when a platform is connected to a stream of EHR, there would be a drastic improvement in dangerous situations awareness and could thus assist the healthcare team. With the application of machine learning, the system is capable to consider all patient's history and through the use of high-performing predictive models, an intelligent early warning system is enabled. In this work we used 121,089 medical encounters from six different hospitals and 7,540,389 data points, and we compared popular ward protocols with six different scalable machine learning methods (three are classic machine learning models, logistic and probabilistic-based models, and three gradient boosted models). The results showed an advantage in AUC (Area Under the Receiver Operating Characteristic Curve) of 25 percentage points in the best Machine Learning model result compared to the current state-of-the-art protocols. This is shown by the generalization of the algorithm with leave-one-group-out (AUC of 0.949) and the robustness through cross-validation (AUC of 0.961). We also perform experiments to compare several window sizes to justify the use of five patient timestamps. A sample dataset, experiments, and code are available for replicability purposes.
On the Security and Usability Implications of Providing Multiple Authentication Choices on Smartphones

The latest smartphones have started providing multiple authentication options including PINs, patterns, and passwords (knowledge based), as well as face, fingerprint, iris, and voice identification (biometric-based). In this article, we conducted two user studies to investigate how the convenience and security of unlocking phones are influenced by the provision of multiple authentication options. In a task-based user study with 52 participants, we analyze how participants choose an option to unlock their smartphone in daily life. The user study results demonstrate that providing multiple biometric-based authentication choices does not really influence convenience, because fingerprint had monopolistic dominance in the usage of unlock methods (111 of a total of 115 unlock trials that used a biometric-based authentication factor) due to users’ habitual behavior and fastness in unlocking phones. However, convenience was influenced by the provision of both knowledge-based and biometric-based authentication categories, as biometric-based authentication options were used in combination with knowledge-based authentication options—pattern was another frequently used unlock method. Our findings were confirmed and generalized through a follow-up survey with 327 participants. First, knowledge-based and biometric-based authentication options are used interchangeably. Second, providing multiple authentication options for knowledge-based authentication may influence convenience—both PINs (55.7%) and patterns (39.2%) are quite evenly used. Last, in contrast to knowledge-based authentication, providing multiple authentication choices for biometric-based authentication has less influence on choosing unlock options—fingerprint scanner is the most frequently used option (134 of 187 unlock methods used among biometric-based authentication options).
Fuzzy Asymmetric Password-Authenticated Key Exchange

Password-Authenticated Key Exchange (PAKE) lets users with passwords exchange a cryptographic key. There have been two variants of PAKE which make it more applicable to real-world scenarios: Supporting fuzzy password matches eases the use of higher entropy passwords and enables using biometrics and environmental readings (both of which are naturally noisy). Until now, both variants of PAKE have been considered only in separation. In this paper, we consider both of them simultaneously. We introduce the notion of Fuzzy Asymmetric PAKE (fuzzy aPAKE), which protects against untrusted servers and supports noisy passwords. We formulate our new notion in the Universal Composability framework of Canetti (FOCS'01), which is the preferred model for password-based primitives. We then show that fuzzy aPAKE can be obtained from oblivious transfer and some variant of robust secret sharing (Cramer et al, EC'15). We achieve security against malicious parties while avoiding expensive tools such as non-interactive zero-knowledge proofs. Our construction is round-optimal, with message and password file sizes that are independent of the schemes error tolerance.
Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow

An adaptive task difficulty assignment method which we reckon as balanced difficulty task finder (BDTF) is proposed in this paper. The aim is to recommend tasks to a learner using a trade-off between skills of the learner and difficulty of the tasks such that the learner experiences a state of flow during the learning. Flow is a mental state that psychologists refer to when someone is completely immersed in an activity. Flow state is a multidisciplinary field of research and has been studied not only in psychology, but also neuroscience, education, sport, and games. The idea behind this paper is to try to achieve a flow state in a similar way as Elo's chess skill rating (Glickman in Am Chess J 3:59-102) and TrueSkill (Herbrich et al. in Advances in neural information processing systems, 2006) for matching game players, where "matched players" should possess similar capabilities and skills in order to maintain the level of motivation and involvement in the game. The BDTF draws analogy between choosing an appropriate opponent or appropriate game level and automatically choosing an appropriate difficulty level of a learning task. This method, as an intelligent tutoring system, could be used in a wide range of applications from online learning environments and e-learning, to learning and remembering techniques in traditional methods such as adjusting delayed matching to sample and spaced retrieval training that can be used for people with memory problems such as people with dementia.
A Novel Sentiment Classification of Product Reviews using Levenshtein Distance

E-commerce platform has brought about a revolutionary change in the way people conduct shopping. In India, the number of digital buyers in 2016 was 130.4 million and by 2021 the number is estimated to increase to around 400 million. In this paper we propose a Levenshtein distance based sentiment classification engine that reads the reviews across different websites for a given product, eventually providing a metric that would help the user in making an informed choice of product.
Human Action Recognition Based on Improved Fusion Attention CNN and RNN

The attention mechanism based models for computer vision and natural language processing are widely utilized, and action recognition in videos is no exception. In this paper, we develop a novel convolutional and recurrent network for action recognition which is "doubly deep" in spatial and temporal layers. First, in the feature extraction stage, we propose an improved p-non-local operations as a simple and effective component to capture long-distance dependencies with deep convolutional neural networks. Second, in the class prediction stage, we propose Fusion KeyLess Attention combining with the forward and backward bidirectional LSTM to learn the sequential nature of the data more efficiently and elegantly, which uses multi-epoch models fusion based on confusion matrix. Experiments on two heterogeneous datasets, HMDB51 and Hollywood2 show that our model has distinct advantages over traditional models also only utilizing RGB features for action recognition based on CNN and RNN.
Intentional Linguistic Summaries for Collaborative Business Model Radars

We propose the use of linguistic summarization concept to support business model design evaluation. In contrast to traditional linguistic summarization, we do not infer these linguistic summaries from data, but use their structure to express intentions or conditions for stakeholders to participate in the designed business model. Early phase business model design is highly uncertain, for which it is often difficult to quantify under what conditions the business model design would be acceptable for the stakeholders involved. Through generation of intentional linguistic summaries (ILS), stakeholders can make explicit under what conditions a certain business model design would be acceptable. Therefore ILS can allow a discussion on benefits and costs without focusing too much on details, which are rarely available in the early phases of business model design. These intentional linguistic summaries can consequently be used as soft KPIs to be validated once the business model is operational.
Crawler by Inference

With a million new pages getting added every single day, the already gigantic web is growing exponentially. While it challenges the search engine and traditional information retrieval methods in producing the relevant results, so does the crawler, which does the background job of traversing the web with hyperlink structure to obtain the web snapshot. The traditional crawlers throw challenges of maintaining the right traversal data structure and tracking the already visited pages. Contemporary applications require context and domain-specific crawlers that harvest the right set of pages and data. A focused crawler needs to have domain-specific evaluation parameters to evaluate and crawl the right set of pages based on relevance. In this paper, we propose a novel model - Crawler by Inference to achieve the said objectives using semantic similarity, paradigmatic similarity, and rules of inference. The proposed methodology prioritizes the links based on the number of new rules built or discovered. The model proposes an efficient data structure - an intelligent queue, which holds the links on a priority basis. The resulting analysis data of a page can also act as a meta-data of the page. The paper also presents the results in comparison with the traditional crawler. The model promises to produce better results by avoiding the crawl of irrelevant pages.
A compact deep architecture for real-time saliency prediction

Saliency computation models aim to imitate the attention mechanism in the human visual system. The application of deep neural networks for saliency prediction has led to a drastic improvement over the last few years. However, deep models have a high number of parameters which makes them less suitable for real-time applications. Here we propose a compact yet fast model for real-time saliency prediction. Our proposed model consists of a modified U-net architecture, a location-dependent fully-connected layer, and central difference convolutional layers. The modification we made on U-Net architecture, promoted compactness and efficiency of the final model. Our location-dependent fully-connected layer facilitates implicit capturing of the location-dependent information. Using the central difference convolutional layers at different scales enables capturing more robust and biologically motivated features. We compare our model with state-of-the-art saliency models using traditional saliency scores as well as our newly devised scheme. Experimental results over four challenging saliency benchmark datasets demonstrate the effectiveness of our approach in striking a balance between accuracy and speed. Our model can be run in real-time which makes it appealing for edge devices and video processing.
An Attention-Based Deep Learning Method for Schizophrenia Patients Classification Using DNA Methylation Data

In this paper, the classification problem of schizophrenia patients from healthy controls is considered, whose goal is to explore the relationship between DNA characteristics and schizophrenia. However, the DNA methylation data has the properties of small samples in high dimension and non-Gaussian distribution which makes it hard to do classification with DNA methylation data. Hence a classification method based on deep learning is designed. We propose a feature selection method based on attention mechanism which embeds a weight gated layer in the network structure to get a task-related sparse representation of the DNA methylation data. The performance of proposed method outperforms existing feature selection methods. On a real-world data set, the classification with proposed method achieves a high accuracy.
Intelligent Trainer for Dyna-Style Model-Based Deep Reinforcement Learning

Model-based reinforcement learning (MBRL) has been proposed as a promising alternative solution to tackle the high sampling cost challenge in the canonical RL, by leveraging a system dynamics model to generate synthetic data for policy training purpose. The MBRL framework, nevertheless, is inherently limited by the convoluted process of jointly optimizing control policy, learning system dynamics, and sampling data from two sources controlled by complicated hyperparameters. As such, the training process involves overwhelmingly manual tuning and is prohibitively costly. In this research, we propose a “reinforcement on reinforcement” (RoR) architecture to decompose the convoluted tasks into two decoupled layers of RL. The inner layer is the canonical MBRL training process which is formulated as a Markov decision process, called training process environment (TPE). The outer layer serves as an RL agent, called intelligent trainer, to learn an optimal hyperparameter configuration for the inner TPE. This decomposition approach provides much-needed flexibility to implement different trainer designs, referred to “train the trainer.” In our research, we propose and optimize two alternative trainer designs: 1) an unihead trainer and 2) a multihead trainer. Our proposed RoR framework is evaluated for five tasks in the OpenAI gym. Compared with three other baseline methods, our proposed intelligent trainer methods have a competitive performance in autotuning capability, with up to 56% expected sampling cost saving without knowing the best parameter configurations in advance. The proposed trainer framework can be easily extended to tasks that require costly hyperparameter tuning.
Towards Provable Timing-Channel Prevention

We describe our ongoing research that aims to eliminate microarchitectural timing channels through time protection, which eliminates the root cause of these channels, competition for capacity-limited hardware resources. A proof-ofconcept implementation of time protection demonstrated the approach can be effective a nd l ow o verhead, b ut also that present hardware fails to support the approach in some aspects and that we need an improved hardXare-software contract to achieve real security. We have demonstrated that these mechanisms are not hard to provide, and are working on their inclusion in the RISC-V ISA. Assuming compliant hardware, we outline how we think we can then formally prove that timing channels are eliminated.
Scalable Recommendation Using Large Scale Graph Partitioning With Pregel and Giraph

Social Big Data is generated by interactions of connected users on social network. Sharing of opinions and contents amongst users, reviews of users for products, result in social Big Data. If any user intends to select products such as movies, books, etc., from e-commerce sites or view any topic or opinion on social networking sites, there are a lot of options and these options result in information overload. Social recommendation systems assist users to make better selection as per their likings. Recent research works have improved recommendation systems by using matrix factorization, social regularization or social trust inference. Furthermore, these improved systems are able to alleviate cold start and sparsity, but not efficient for scalability. The main focus of this article is to improve scalability in terms of locality and throughput and provides better recommendations to users with large-scale data in less response time. In this article, the social big graph is partitioned and distributed on different nodes based on Pregel and Giraph. In the proposed approach ScaleRec, partitioning is based on direct as well as indirect trust between users and comparison with state-of-the-art approaches proves that statistically better partitioning quality is achieved using proposed approach. In ScaleRec, hyperedge and transitive closure are used to enhance social trust amongst users. Experiment analysis on standard datasets such as Epinions and LiveJournal proves that better locality and recommendation accuracy is achieved by using ScaleRec.
Convolutional Neural Network Based Malaria Parasite Infection Detection Using Thin Microscopic Blood Smear Samples

Malaria stands as a major health issue across the globe even though it is both preventable and treatable. Malaria is usually diagnosed by an expert microbiologist through the examination of a microscopic blood smear sample. Malaria can be easily cured if diagnosed at an early stage and followed up with proper medical treatment. Computer-aided diagnosis is gaining popularity nowadays as it can be effectively used as a primary screening test in the absence of an expert microbiologist. Deep learning is an artificial intelligence technique where the machine is trained to mimic the thought process of a human brain. This paper focuses on building a deep convolutional neural network that can predict the malaria parasite infection from thin blood smear samples. The malaria dataset is obtained from the National Library of Medicine hosted by Lister Hill National Center for Biomedical Communications, USA. The convolutional neural network model is optimized against overfitting and attains an F1 score of over 94%.
Good things come in threes

Writing a test case reproducing a reported software crash is a common practice to identify the root cause of an anomaly in the software under test. However, this task is usually labor-intensive and time-taking. Hence, evolutionary intelligence approaches have been successfully applied to assist developers during debugging by generating a test case reproducing reported crashes. These approaches use a single fitness function called Crash Distance to guide the search process toward reproducing a target crash. Despite the reported achievements, these approaches do not always successfully reproduce some crashes due to a lack of test diversity (premature convergence). In this study, we introduce a new approach, called MO-HO, that addresses this issue via multi-objectivization. In particular, we introduce two new Helper-Objectives for crash reproduction, namely test length (to minimize) and method sequence diversity (to maximize), in addition to Crash Distance. We assessed MO-HO using five multi-objective evolutionary algorithms (NSGA-II, SPEA2, PESA-II, MOEA/D, FEMO) on 124 non-trivial crashes stemming from open-source projects. Our results indicate that SPEA2 is the best-performing multi-objective algorithm for MO-HO. We evaluated this best-performing algorithm for MO-HO against the state-of-the-art: single-objective approach (Single-Objective Search) and decomposition-based multi-objectivization approach (De-MO). Our results show that MO-HO reproduces five crashes that cannot be reproduced by the current state-of-the-art. Besides, MO-HO improves the effectiveness (+10% and +8% in reproduction ratio) and the efficiency in 34.6% and 36% of crashes (i.e., significantly lower running time) compared to Single-Objective Search and De-MO, respectively. For some crashes, the improvements are very large, being up to +93.3% for reproduction ratio and -92% for the required running time.
PHND: Pashtu Handwritten Numerals Database and deep learning benchmark

In this paper we introduce a real Pashtu handwritten numerals dataset (PHND) having 50,000 scanned images and make publicly available for research and scientific use. Although more than fifty million people in the world use this language for written and oral communication, no significant efforts are devoted to the Pashtu Optical Character Recognition (POCR). We present a new approach for Pahstu handwritten numerals recognition (PHNR) based on deep neural networks. We train Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) on high-frequency numerals for feature extraction and classification. We evaluated the performance of the proposed algorithm on the newly introduced Pashtu handwritten numerals database PHND and Bangla language number database CMATERDB 3.1.1. We obtained best recognition rate of 98.00% and 98.64% on PHND and CMATERDB 3.1.1. respectively.
Post-Quantum Verification of Fujisaki-Okamoto

We present a computer-verified formalization of the post-quantum security proof of the Fujisaki-Okamoto transform (as analyzed by Hövelmanns, Kiltz, Schäge, and Unruh, PKC 2020). The formalization is done in quantum relational Hoare logic and checked in the qrhl-tool (Unruh, POPL 2019).
The Biometric Cardless Transaction with Shuffling Keypad Using Proximity Sensor

Nowadays cards like ATM, credit, debit and other cards are used for bank transactions and play a crucial role in the banking area. This paper proposes a secure, robust and flexible biometric authentication system which combines two methods that use a Biometric and a proximity sensor. To increase the security level in ATM transaction this proposal integrates a biometric fingerprint technique along with a shuffling keypad method. Here the card is replaced with the fingerprint, which is registered during the opening of a bank account and PIN number is entered in a shuffling keypad. To avoid the shoulder-surfing attacks with or without concealed cameras in PIN entry, this approach uses a shuffling keypad which uses a proximity sensor to shuffle the keypad during the PIN entry. The system is tested with multiple users and has obtained 100% accuracy. This system avoids the misuse of electronic cards and supports a secure transaction.