Reviewerly/exHarmony huggingface dataset:
Recurrent Event Network : Global Structure Inference Over Temporal Knowledge Graph

Modeling dynamically-evolving, multi-relational graph data has received a surge of interests with the rapid growth of heterogeneous event data. However, predicting future events on such data requires global structure inference over time and the ability to integrate temporal and structural information, which are not yet well understood. We present Recurrent Event Network (RE-Net), a novel autoregressive architecture for modeling temporal sequences of multi-relational graphs (e.g., temporal knowledge graph), which can perform sequential, global structure inference over future time stamps to predict new events. RE-Net employs a recurrent event encoder to model the temporally conditioned joint probability distribution for the event sequences, and equips the event encoder with a neighborhood aggregator for modeling the concurrent events within a time window associated with each entity. We apply teacher forcing for model training over historical data, and infer graph sequences over future time stamps by sampling from the learned joint distribution in a sequential manner. We evaluate the proposed method via temporal link prediction on five public datasets. Extensive experiments demonstrate the strength of RE-Net, especially on multi-step inference over future time stamps. Code and data can be found at this https URL .
A BLSTM with Attention Network for Predicting Acute Myeloid Leukemia Patient’s Prognosis using Comprehensive Clinical Parameters

The prognosis management is crucial for highrisk disease like Acute Myeloid Leukemia (AML) in order to support decisions of clinical treatment. However, the challenges of accurate and consistent forecasting lie in the high variability of the disease outcomes and the complexity of the multiple clinical measurements available over the course of the treatment. In order to capture the multi-dimensional and longitudinal aspect of these comprehensive clinical parameters, we utilize an attention-based bi-directional long shortterm memory (Att-BLSTM) network to predict AML patient's survival and relapse. Specifically, we gather a 10-year worth of real patient's clinical data including blood test, medication, HSCT status, and gene mutation information. Our proposed Att-BLSTM framework achieves 77.1% and 67.3% AUC in tasks of predicting the next 2-year mortality and disease relapse with these comprehensive clinical parameters, and our further analysis demonstrates that a next 0 to 3 months prediction performs equally well, i.e., 74.8% and 67% AUC for mortality and relapse respectively.
Coherent-state quantum cryptography using pseudorandom number generators

Abstract Quantum key distribution plays an important role in modern cryptography, since the security of the transmitted keys is guaranteed by fundamental laws of nature. A method using pseudorandom number generators well known from classical cryptography is considered. It is shown that their use in quantum cryptography makes it possible to increase the key generation rate under very weak assumptions about the capabilities of the eavesdropper. A practical scheme of a coherent-state quantum key distribution protocol using pseudorandom sequences is proposed. The cryptographic strength of the proposed protocol against a beam-splitting attack is considered.
On Anonymizing Medical Microdata with Large-Scale Missing Values - A Case Study with the FAERS Dataset

As big data analysis becomes one of the main driving forces for productivity and economic growth, the concern of individual privacy disclosure increases as well, especially for applications accessing medical or health data that contain personal information. Most contemporary techniques for privacy preserving data publishing follow a simple assumption–the data of concern is complete, i.e., containing no missing values, which however is not the case in the real world. This paper presents our endeavors on inspecting the effect of missing values upon medical data privacy. In particular, we inspected the US FAERS dataset, a public dataset containing adverse drug events released by US FDA. Following the presumption of current anonymization paradigm–the data should contain no missing values, we investigated three intuitive strategies, including or excluding missing values or executing imputation, to anonymize the FAERS dataset. Our results demonstrate the awkwardness of these intuitive strategies in handling data with a massive amount of missing values. Accordingly, we propose a new strategy, consolidation, and the corresponding privacy protection model and anonymization algorithm. Experimental results show that our method can prevent privacy disclosure and sustain the data utility for ADR signal detection.
Ultimate password: is voice the best biometric to beat hackers?

Citrix, a company that works with the likes of the FBI and US military, recently hit the headlines when it fell victim to hackers 1 . The cyber-criminals allegedly stole terabytes of sensitive documents including reports, blueprints and business papers, using a technique called password spraying. This approach, which exploits weak passwords, could be used to infiltrate many more companies like Citrix – and it's one more example of why the traditional username and password combination is no longer fit for purpose, in both our personal and business lives.
New Semantical Insights Into Call-by-Value λ-Calculus

Despite the fact that call-by-value λ-calculus was defined by Plotkin in 1977, we believe that its theory of program approximation is still at the beginning. A problem that is often encountered when studying its operational semantics is that, during the reduction of a λ-term, some redexes remain st uck (waiting for a value). Recently, Carraro and Guerrieri proposed to endow this calculus with permutation rules, naturally arising in the context of linear logic proof-nets, that succeed in unblocking a certain number of such redexes. In the present paper we introduce a new class of models of call-by-value λ-calculus, arising from non-idempotent intersection type systems. Beside satisfying the usual properties as soundness and adequacy, these models validate the permutation rules mentioned above as well as some reductions obtained by contracting suitable λI-redexes. Thanks to these (perhaps unexpected) features, we are able to demonstrate that every model living in this class satisfies an Approximation Theorem with respect to a refined notion of syntactic approximant. While this kind of results often require impredicative techniques like reducibility candidates, the quantitative information carried by type derivations in our system allows us to provide a combinatorial proof.
Automated Ethereum Smart Contract for Block Chain Based Smart Home Security

Presence of Internet of Things (IoT) based applications has been increasing in various domains including transportation, logistics, health care, and smart homes. Such applications involve deploying an enormous number of IoT devices, which generally lacks from security and often associates several vulnerabilities. These IoT devices need to communicate and synchronize with each other, which also increase the security and privacy challenges. Traditional security models are based on centralized and often include complicated approaches which, tend to be inapplicable and have some limitations. Therefore, one proposed solution is to use blockchain technology which could provide decentralize, secure, and peer-to-peer networks. In this paper, private blockchain implementation using Ethereum smart contract is developed for the smart home to ensure only the home owner can access and monitor home appliances. Simple smart contracts are designed to allow devices to communicate without the need for trusted third party. Our prototype demonstrates three key elements of blockchain-based smart security solution for smart home applications such as smart contract, blockchain-based access control and performance evaluation of the proposed scheme.
Extracting core questions in community question answering based on particle swarm optimization

Purpose A large number of questions are posted on community question answering (CQA) websites every day. Providing a set of core questions will ease the question overload problem. These core questions should cover the main content of the original question set. There should be low redundancy within the core questions and a consistent distribution with the original question set. The paper aims to discuss these issues. Design/methodology/approach In the paper, a method named QueExt method for extracting core questions is proposed. First, questions are modeled using a biterm topic model. Then, these questions are clustered based on particle swarm optimization (PSO). With the clustering results, the number of core questions to be extracted from each cluster can be determined. Afterwards, the multi-objective PSO algorithm is proposed to extract the core questions. Both PSO algorithms are integrated with operators in genetic algorithms to avoid the local optimum. Findings Extensive experiments on real data collected from the famous CQA website Zhihu have been conducted and the experimental results demonstrate the superior performance over other benchmark methods. Research limitations/implications The proposed method provides new insight into and enriches research on information overload in CQA. It performs better than other methods in extracting core short text documents, and thus provides a better way to extract core data. The PSO is a novel method used for selecting core questions. The research on the application of the PSO model is expanded. The study also contributes to research on PSO-based clustering. With the integration of K-means++, the key parameter number of clusters is optimized. Originality/value The novel core question extraction method in CQA is proposed, which provides a novel and efficient way to alleviate the question overload. The PSO model is extended and novelty used in selecting core questions. The PSO model is integrated with K-means++ method to optimize the number of clusters, which is just the key parameter in text clustering based on PSO. It provides a new way to cluster texts.
Learning Interpretable Disentangled Representations Using Adversarial VAEs

Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice. It has been recently shown that learning a disentangled feature representation is important for a more compact and explainable representation of the data. In this paper, we introduce a novel adversarial variational autoencoder with a total correlation constraint to enforce independence on the latent representation while preserving the reconstruction fidelity. Our proposed method is validated on a publicly available dataset showing that the learned disentangled representation is not only interpretable, but also superior to the state-of-the-art methods. We report a relative improvement of $$81.50\%$$ in terms of disentanglement, $$11.60\%$$ in clustering, and $$2\%$$ in supervised classification with a few amount of labeled data.
Effects of Subjective Norms and Security on Online Banking Adoption: Multilevel Linear Model Analysis

Robust macroeconomic performance is essential for an economic growth and development alongside challenging technology advancements. Within the service sector of the economy, one of the broadly studied areas of technological transformation is the retail financial services which includes consumer banking activities. Online banking enables customers to experience in a vast array of financial services through e-banking websites. Nevertheless, in practice; this phenomenon varies depending on the particular context. Hence, this research addresses the significance of subjective norms and security of e-services towards adoption of consumer online banking in the specified context. Sample was drawn from the students of the Kelaniya University those who are participating for the week-end study programs. The students of the doctor of business administration attached to the university were designated as enumerators of the data collection process. Data collection has been executed by way of self-administered questionnaires which were completed by the respective respondents. Initially 400 questionnaires were distributed and 287 duly completed questionnaires were considered for the final data analysis. TAM has been extended using the subjective norms and security of e-services variables and dimensions of the variables were measured by five-point Lickert scale. Reliability analysis was conducted to measure internal consistencies using Cronbach’s alpha. In order to test the hypotheses anova, ancova and multilevel linear model used. The empirical evidence supported three hypotheses indicating the significance of the variables/constructs on adoption of online banking. In conclusion, researcher has presented several suggestions related to future researches on online banking adoption field.
Offline Handwritten New Tai Lue Characters Recognition Using CNN-SVM

In this paper, the classical convolutional neural network VGG19 is used to recognize the offline handwritten New Tai Lue characters firstly. Then, according to the recognized image features, the CNN suitable for this data set is constructed repeatedly, and 83 kinds of offline handwritten characters are experimentally tested. Finally, the features extracted from CNN are classified by Support Vector Machine (SVM) and the recognition rate is improved compared with VGG19 and CNN constructed in this paper.
Estimation of clear sky global solar radiation in Algeria

The paper presents the evaluation of performance of three models at three sites for estimating instantaneous clear-sky global solar radiation on a horizontal surface in Algeria. Additionally, recommend it to be used for estimating solar radiation at many locations in similar climates where radiometric measurements are not available and which might be helpful in the selection of the most suitable locations for solar power installations. The results in general exhibit that for global radiation, the daily correlation coefficient is higher than 0.99, whereas the mean absolute percentage error is less than 5%. The daily mean bias error ranges between -3 and +3%. The daily root mean square error is less than 7%. These results represent a precision that indicates that Atwater &amp; Ball and Bird &amp; Hulstrom models can be used successfully to predict solar radiation over three stations in the studied sites.
Unidimensional Continuous-Variable Quantum Key Distribution with Untrusted Detection under Realistic Conditions

A unidimensional continuous-variable quantum key distribution protocol with untrusted detection is proposed, where the two legitimate partners send unidimensional modulated or Gaussian-modulated coherent states to an untrusted third party, i.e., Charlie, to realize the measurement. Compared with the Gaussian-modulated coherent-state protocols, the unidimensional modulated protocols take the advantage of easy modulation, low cost, and only a small number of random numbers required. Security analysis shows that the proposed protocol cannot just defend all detectors side channels, but also achieve great performance under certain conditions. Specifically, three cases are discussed in detail, including using unidimensional modulated coherent states in Alice’s side, in Bob’s side, and in both sides under realistic conditions, respectively. Under the three conditions, we derive the expressions of the secret key rate and give the optimal gain parameters. It is found that the optimal performance of the protocol is achieved by using unidimensional modulated coherent states in both Alice’s and Bob’s side. The resulting protocol shows the potential for long-distance secure communication using the unidimensional quantum key distribution protocol with simple modulation method and untrusted detection under realistic conditions.
A Brief Review on Cycle Generative Adversarial Networks

Image-to-image translation is an important topic in the field of computer vision. It aims to learn the mapping between input image and output image by training the datasets, and finally translates the image style from one domain to another. In terms of the form of translation, it can be divided into the translation between two domains and multiple domains from different datasets. And it is also divided into pairs and unpaired by the training datasets. As a successful representation of the translation of an unpaired image between two domains, the CycleGAN model is of great significance to the research and application. Starting from the application background of the CycleGAN model, this paper compares it with the basic deep learning model GAN, the paired image translation model pix2pix and other unpaired image-to-image translation models separately, and gives their own model structure diagram. Then, the further application of the CycleGAN in other fields of computer vision is analyzed, especially in the field of Person re-recognition and face change. Finally, according to the actual running results of each model, the existing problems are analyzed and summarized. The further research work is given.
Dynamic Hand Gesture Recognition from Multi-modal Streams Using Deep Neural Network

Hand gesture is an efficient mean of human computer interaction. However, hand gesture recognition faces many challenges such as low hand resolution, phase variation and viewpoint. As a result, deployment of hand gesture in a practical application of human machine interaction is still very limited. This work aims to increase performance of hand gestures recognition by using multi-modal streams. We propose a method that combines depth, RGB and optical flow in a unified recognition framework. Each stream will go first into a feature extraction component, which is a deep learning model. We then investigate different fusion techniques to combine features from multi-modal streams for final classification. The proposed method is validated on a dataset of twelve gestures collected by ourselves from five different viewpoints. Experimental results show that accuracy of the proposed method using multi-modal streams outperforms ones that use a single stream, particularly for difficult viewpoints.
Random Restrictions of High Dimensional Distributions and Uniformity Testing with Subcube Conditioning

We give a nearly-optimal algorithm for testing uniformity of distributions supported on {–1, 1}n, which makes many queries to a subcube conditional sampling oracle (Bhattacharyya and Chakraborty (2018)). The key technical component is a natural notion of random restrictions for distributions on {–1, 1}n, and a quantitative analysis of how such a restriction affects the mean vector of the distribution. Along the way, we consider the problem of mean testing with independent samples and provide a nearly-optimal algorithm.
A Systematic Review on Load Balancing Issues in Cloud Computing

Cloud computing is gaining popularity day by day due to its various qualities. It offers web services and computing resources on demand basis at a very low cost. Demand for cloud services is increasing everyday which brings a lot of load traffic with it, which need to be tackled carefully to maintain the performance of the cloud. To avoid overloaded situations, Load balancing is used to distribute load to all the available nodes efficiently. However, there are several Load balancing issues which requires special attention to give best possible solution to cloud technology. This survey highlights load balancing issues which need to be handled carefully for better performance of cloud computing. Also, we discussed the goals, need and techniques of cloud load balancing.
Decentralized Distributed PPO: Solving PointGoal Navigation

We present Decentralized Distributed Proximal Policy Optimization (DD-PPO), a method for distributed reinforcement learning in resource-intensive simulated environments. DD-PPO is distributed (uses multiple machines), decentralized (lacks a centralized server), and synchronous (no computation is ever stale), making it conceptually simple and easy to implement. In our experiments on training virtual robots to navigate in Habitat-Sim, DD-PPO exhibits near-linear scaling -- achieving a speedup of 107x on 128 GPUs over a serial implementation. We leverage this scaling to train an agent for 2.5 Billion steps of experience (the equivalent of 80 years of human experience) -- over 6 months of GPU-time training in under 3 days of wall-clock time with 64 GPUs. This massive-scale training not only sets the state of art on Habitat Autonomous Navigation Challenge 2019, but essentially solves the task --near-perfect autonomous navigation in an unseen environment without access to a map, directly from an RGB-D camera and a GPS+Compass sensor. Fortuitously, error vs computation exhibits a power-law-like distribution; thus, 90% of peak performance is obtained relatively early (at 100 million steps) and relatively cheaply (under 1 day with 8 GPUs). Finally, we show that the scene understanding and navigation policies learned can be transferred to other navigation tasks -- the analog of ImageNet pre-training + task-specific fine-tuning for embodied AI. Our model outperforms ImageNet pre-trained CNNs on these transfer tasks and can serve as a universal resource (all models and code are publicly available).
Fully experiment-based evaluation of few digital volume correlation techniques

Digital Volume Correlation (DVC) is a powerful set of techniques used to compute the local shifts of 3D images obtained, for instance, in tomographic experiments. It is utilized to analyze the geometric changes of the investigated object as well as to correct the corresponding image misalignments for further analysis. It can therefore be used to evaluate the local density changes of the same regions of the inspected specimens, which might be shifted between measurements. In recent years, various approaches and corresponding pieces of software were introduced. Accuracies for the computed shift vectors of up to about 1‰ of a single voxel size have been reported. These results, however, were based either on synthetic datasets or on an unrealistic setup. In this work, we propose two simple methods to evaluate the accuracy of DVC-techniques using more realistic input data and apply them to several DVC programs. We test these methods on three materials (tuff, sandstone, and concrete) that show different contrast and structural features.
Side channel attack on smartphone sensors to infer gender of the user

Smartphones incorporate a plethora of diverse and powerful sensors that enhance user experience. Two such sensors are accelerometer and gyroscope, which measure acceleration in all three spatial dimensions and rotation along the three axes of the smartphone, respectively. These sensors are used primarily for screen rotations and advanced gaming applications. However, they can also be employed to gather information about the user's activity and phone positions. In this work, we investigate using accelerometer and gyroscope as a side-channel to learn highly sensitive information, such as the user's gender. We present an unobtrusive technique to determine the gender of a user by mining data from the smartphone sensors, which do not require explicit permissions from the user. A preliminary study conducted on 18 participants shows that we can detect the user's gender with an accuracy of 80%.