Reviewerly/exHarmony huggingface dataset:
Distinguishing causation and correlation: Causal learning from time-series graphs with trends

Time-series graphs are ubiquitous in scientific and popular communications and in mobile health tracking apps. We studied if people can accurately judge whether there is a relation between the two variables in a time-series graph, which is especially challenging if the variables exhibit temporal trends. We found that, for the most part, participants were able to discriminate positive vs. negative relations even when there were strong temporal trends; however, when there is a positive causal relation but opposing temporal trends (one variable increases and the other decreases over time), people have difficulty inferring the positive causal relation. Further, we found that a simple dynamic presentation can ameliorate this challenge. The present finding sheds light on when people can and cannot accurately learn causal relations from time-series data and how to present graphs to aid interpretability.
WiP: Criminal Smart Contract for Private Key Theft in End to End Encrypted Applications

Cryptocurrencies such as Bitcoin [1], Ethereum [2] are becoming very popular among people due to their properties such as pseudo-anonymity which can be used for both good and bad. In this paper, we show how smart contracts can be used to build criminal applications. Here we construct an application that allows contractors to get the stolen private key of a target user from perpetrators in an end to end encrypted message application.
Scattering and Perturbation Theory for Discrete-Time Dynamics

We present a systematic treatment of scattering processes for quantum systems whose time evolution is discrete. We define and show some general properties of the scattering operator, in particular the conservation of quasienergy which is defined only modulo $2\ensuremath{\pi}$. Then we develop two perturbative techniques for the power series expansion of the scattering operator, the first one analogous to the iterative solution of the Lippmann-Schwinger equation, the second one to the Dyson series of perturbative quantum field theory. We use this formalism to compare the scattering amplitudes of a continuous-time model and of the corresponding discretized one. We give a rigorous assessment of the comparison for the case of bounded free Hamiltonian, as in a lattice theory with a bounded number of particles. Our framework can be applied to a wide class of quantum simulators, like quantum walks and quantum cellular automata. As a case study, we analyze the scattering properties of a one-dimensional cellular automaton with locally interacting fermions.
Convolutional Neural Network based Foreground Segmentation for Video Surveillance Systems

Convolutional Neural Networks (CNN) have shown astonishing results in the field of computer vision. This paper proposes a foreground segmentation algorithm based on CNN to tackle the practical challenges in the video surveillance system such as illumination changes, dynamic backgrounds, camouflage, and static foreground object, etc. The network is trained using the input of image sequences with respective ground-truth. The algorithm employs a CNN called VGG-16 to extract features from the input. The extracted feature maps are upsampled using a bilinear interpolation. The upsampled feature mask is passed through a sigmoid function and threshold to get the foreground mask. Binary cross entropy is used as the error function to compare the constructed foreground mask with the ground truth. The proposed algorithm was tested on two standard datasets and showed superior performance as compared to the top-ranked foreground segmentation methods.
Incentive for Historical Block Data Sharing in Blockchain

In blockchain, delivering block data, especially delivering all historical blocks to a new node, will cost lots of bandwidth consumption. This cost may dissuade nodes from sharing their blocks to new or rejoined peer. In this paper, we present some incentive schemes for this kind of data sharing behaviour. In our design, node participating in delivering their storage data to their peers can get some rewards from the node who request the data. We adopt coin-locking strategy to discourage dishonest behaviour in the sharing process. Nodes need not put a large deposit to big data sharing since the data sharing process can be divided into smaller ones, and micropayment channel is set to accelerate the speed of during each small transmitting process. We give a theoretical analysis of our mechanism and discuss its practical application of data sharing in some mainstream cryptocurrencies and blockchains in IoT industry.
Implementation of Augmented Reality (AR) android based in learning

Abstract Augmented reality is a technology that offers the ability to combine the virtual content with the real world. Augmented reality is divided into two, which is desktop based and mobile (android) can be used as a media in learning. The presence of augmented reality is a solution for abstract and complex learning. We are collected several journals related to augmented reality from a variety of sources, and acquired as many as 57 articles in the span of eight years between 2010 and 2018, and 28 of them in accordance with this research topic. We found a variety of research methods used to obtain the data for the implementation of augmented reality as a learning media, methods used include observation, questionnaires, surveys, pre-test and post-test, interview, video recording, and documentation. The resulting impact also varied, ranging from student motivation, student performance, student understanding, and the effectiveness of learning.
Energy Efficiency Optimization of FPGA-based CNN Accelerators with Full Data Reuse and VFS

While FPGA has been recognized as a promising platform to accelerate Convolutional Neural Networks (CNNs) in embedded computing given its high flexibility and power efficiency, two challenges still have to be addressed to enhance its applicability on the edge-computing paradigm. First, the power and performance of the CNN accelerator are still bounded by memory throughput, and a CNN-customized architecture is desirable to fully utilize the on-chip storage. Second, power optimization algorithms are insufficiently explored on CNN-targeted platforms. In this paper, we design a novel FPGA-based CNN accelerator architecture that makes full use of the on-chip storage resources leveraging data reuse and loop unrolling strategies. We also present an efficient FPGA-based voltage and frequency scaling (VFS) system that enables VFS of the CNN accelerator for power optimization. We devise a VFS policy that fully exploits the power efficiency potential of the FPGA. Experiment results show up to 40% energy can be saved with our VFS platform and policy.
Double Additive Margin Softmax Loss for Face Recognition

Learning large-margin face features whose intra-class variance is small and inter-class diversity is one of important challenges in feature learning applying Deep Convolutional Neural Networks (DCNNs) for face recognition. Recently, an appealing line of research is to incorporate an angular margin in the original softmax loss functions for obtaining discriminative deep features during the training of DCNNs. In this paper we propose a novel loss function, termed as double additive margin Softmax loss (DAM-Softmax). The presented loss has a clearer geometrical explanation and can obtain highly discriminative features for face recognition. Extensive experimental evaluation of several recent state-of-the-art softmax loss functions are conducted on the relevant face recognition benchmarks, CASIA-Webface, LFW, CALFW, CPLFW, and CFP-FP. We show that the proposed loss function consistently outperforms the state-of-the-art.
Flattenet: A Simple and Versatile Framework for Dense Pixelwise Prediction

In this paper, we focus on devising a versatile framework for dense pixelwise prediction whose goal is to assign a discrete or continuous label to each pixel for an image. It is well-known that the reduced feature resolution due to repeated subsampling operations poses a serious challenge to Fully Convolutional Network (FCN) based models. In contrast to the commonly-used strategies, such as dilated convolution and encoder-decoder structure, we introduce the Flattening Module to produce high-resolution predictions without either removing any subsampling operations or building a complicated decoder module. In addition, the Flattening Module is lightweight and can be easily combined with any existing FCNs, allowing the model builder to trade off among model size, computational cost and accuracy by simply choosing different backbone networks. We empirically demonstrate the effectiveness of the proposed Flattening Module through competitive results in human pose estimation on MPII, semantic segmentation on PASCAL-Context and object detection on PASCAL VOC. We hope that the proposed approach can serve as a simple and strong alternative of current dominant dense pixelwise prediction frameworks.
Impact of Solar Power and Load Demand Forecast Uncertainty on the Optimal Operation of Microgrid

Operation optimization of a microgrid is formulated based on the forecast of renewable energy resources and electricity demand. The forecast error will introduce uncertainties thereby affecting the accuracy and optimality of the solution. In this paper, the impact of this uncertainty is investigated as it receives little attention in the literature. First, an accurate forecasting model for solar irradiance and electricity demand using long short-term memory recurrent neural network and feedforward neural network is developed. To improve the forecasting accuracy, the k-means clustering algorithm is used to partition the datasets into a number of clusters. Second, MDSTool is used to simulate a one-year operation optimization of the microgrid using both the actual and forecasted data. MDSTool is a decision support tool that we developed in our previous work. We find that the forecast errors have a significant impact on the microgrid's annual energy savings.
Medium and Long-Term Parametric Temperature Forecasting using Real Meteorological Data

Temperature forecasting based on meteorological data is the key stage for an accurate estimation of PV power production and demand-side management leading to better grid stability. Typically, weather forecasting is the prediction of weather parameters for seconds until months ahead based on the historical weather database. Thus, researchers create several approaches to maximize the accuracy of these predictions and increase the period of estimation. This paper proposes a new medium and long-term temperature forecasting approach based on Multi Inputs Single Output (MISO) model base on empirical equations. The parameters of the proposed model are computed using a Recursive Least Squares (RLS) method. Using a set of real meteorological data, simulation results are presented to show the high accuracy of the proposed temperature forecasting approach.
Development of measures to ensure information security in structural division of the university

In this paper we have determined the place of information security in the national security system of the Russian Federation and analyzed information processed in higher education institutions. The categorization of the processed information in the information system of the Higher School of Technosphere Safety is carried out. The estimation of the potential violator of information security is executed and an improved threat model is made, on the basis of which we have formulated practical proposals for the protection of information in the structural unit of the University.
Robust sequential subspace clustering via ℓ1-norm temporal graph

Subspace clustering (SC) has been widely applied to segment data drawn from multiple subspaces. However, for sequential data, a main challenge in subspace clustering is to exploit temporal information. In this paper, we propose a novel robust sequential subspace clustering approach with a ℓ1-norm temporal graph. The ℓ1-norm temporal graph is designed to encode the temporal information underlying in sequential data. By using the ℓ2 norm, it can enforce well temporal similarity of neighboring frames with a sample-dependent weight, and mitigate the effect of noises and outliers on subspace clustering because large errors mixed in the real data can be suppressed. Under assumption of data self-expression, our clustering model is put forward by further integrating the classical Sparse Subspace Clustering and the ℓ1-norm Temporal Graph (SSC-L1TG). To solve the proposed model, we introduce a new efficient proximity algorithm. At each iteration, the sub-problem is solved by proximal minimization with closed-form solution. In contrast to the alternating direction method of multipliers (ADMM) employed in most existing clustering approaches without convergence guarantee, the proposed SSC-L1TG is guaranteed to converge to the desired optimal solution. Experimental results on both synthetic and real data demonstrate the efficacy of our method and its superior performance over the state-of-the-art methods.
Parallel quantum random number generation based on spontaneous emission of alkaline earth

We propose a parallel quantum random number generator based on spontaneous emission of strontium atoms. By directly detecting the spontaneously emitted photons in different directions, two strings of true random bits with no apparent correlation at rates up to 5.12 Mb s−1 are simultaneously extracted. Via post-processing, both output sequences pass the NIST tests. Taking advantage of the random directions of the emitted photons, the presented scheme has great scalability to be extended to arbitrary multiple channels without limitations of devices' demultiplexing configurations.
Is bitcoin a channel of capital inflow? Evidence from carry trade activity

We empirically identify carry trade activities under the disguise of bitcoin transactions on USD and CNY bitcoin exchanges during the post global financial crisis period. Relying on variations of bitcoin exchange pairs and two policy shocks, we find the evidence of inflow capital control evasion. We show that the relative CNY to USD bitcoin price, indicating capital inflow volume, of those with high excessive currency conversion profit respond significantly more negatively to carry trade returns. Moreover, this relationship becomes significantly weaker when China tightens its regulations on bitcoin transactions.
Vicarious Value Learning and Inference in Human-Human and Human-Robot Interaction

Among the biggest challenges for researchers of human-robot interaction is imbuing robots with lifelong learning capacities that allow efficient interactions between humans and robots. In order to address this challenge we are developing computational mechanisms for a humanoid robotic agent utilizing both system 1 and system 2-like cognitive processing capabilities. At the core of this processing is a Social Affective Appraisal model that allows for vicarious value learning and inference. Using a multi-dimensional reinforcement learning approach the robotic agent learns affective value-based functions (system 1). This learning can ground representations of affective relations (predicates) relevant to interacting agents. In this article we discuss the existing theoretical basis for developing our neural network model as a system 1-like process. We also discuss initial ideas for developing system 2-like top-down/generative affective (semantic relation-based) processing. The aim of the symbolic-connectionist architectural development is to promote autonomous capabilities in humanoid robots for interacting efficiently/intelligently (recombinant application of learned associations) with humans in changing and challenging environments.
Inferring Streaming Video Quality from Encrypted Traffic

Inferring the quality of streaming video applications is important for Internet service providers, but the fact that most video streams are encrypted makes it difficult to do so. We develop models that infer quality metrics (i.e., startup delay and resolution) for encrypted streaming video services. Our paper builds on previous work, but extends it in several ways. First, the models work in deployment settings where the video sessions and segments must be identified from a mix of traffic and the time precision of the collected traffic statistics is more coarse (e.g., due to aggregation). Second, we develop a single composite model that works for a range of different services (i.e., Netflix, YouTube, Amazon, and Twitch), as opposed to just a single service. Third, unlike many previous models, our models perform predictions at finer granularity (e.g., the precise startup delay instead of just detecting short versus long delays) allowing to draw better conclusions on the ongoing streaming quality. Fourth, we demonstrate the models are practical through a 16-month deployment in 66 homes and provide new insights about the relationships between Internet "speed'' and the quality of the corresponding video streams, for a variety of services; we find that higher speeds provide only minimal improvements to startup delay and resolution.
Comparison of Supervised and Unsupervised Fraud Detection

Fraud Detection is a challenging problems in Machine Learning. The most commonly used evaluation metric for fraud detection which is a binary classification Machine Learning problem is Area Under the Receiver Operating Characteristic curve (AUROC). In this paper we will show that AUROC is not always the correct metric to evaluate the performance of a classifier with high imbalance. We shall rather show that Area Under the Precision Recall curve (AUPR) is a better evaluation metric for the same. We will compare and contrast various supervised as well as unsupervised approaches to optimize the Area under PR curve for fraud detection problem.
Loading pattern optimization for a PWR using Multi-Swarm Moth Flame Optimization Method with Predator

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
A Classification Algorithm of Ultrasonic Thyroid Standard Planes Using LBP and HOG Features

To improve the performance of thyroid standard plane scanning, this paper proposes a classification algorithm for classifying thyroid ultrasound images using texture features. Firstly, the region of interest of ultrasound images is chosen in the preprocessing process step. Secondly, the local binary patterns (LBP) features and the histograms of oriented gradients (HOG) features of the images are extracted. Then the obtained feature vectors are spliced as the input of the support vector machine (SVM) which is used to classify the thyroid standard plane images. In the experiment, there are 4,574 thyroid standard plane images are used in this paper, which are divided into 8 categories, of which 3,655 pictures for training, and 919 pictures for testing. The experimental results show that the classification accuracy is up to 88.58%, which show a fact that the proposed algorithm has a good discriminating ability for the standard eight-category thyroid plane images, which can assist the doctor to diagnose to a certain extent.
Dynamic Searchable Symmetric Encryption with Forward and Backward Privacy: A Survey

Searchable symmetric encryption (SSE) has been proposed that enables the clients to outsource their private encrypted data onto the cloud server and later the data can be searched with limited information leakage. However, existing surveys have not covered most recent advances on SSE technique. To fill the gap, we make a survey on state-of-the-art representative SSE schemes in cloud environment. We mainly focus on dynamic SSE schemes with forward and backward privacy, two vital security elements to maintain query privacy during data update operations. Specifically, we discuss about SSE protocols based on query expressiveness, including single keyword search, conjunctive keyword search, range search, disjunctive keyword search and verifiable search. Finally, through comparison on query expressiveness, security and efficiency, we demonstrate the strengths and weaknesses of the existing SSE protocols.
Thai Dependency Parsing with Character Embedding

Dependency parsing (DP) becomes an important part of natural language processing (NLP) applications. However, most of DP methods have been developed for English language, but not for Thai language. In addition, the existing DP methods were still unsolved the problems of long and complex sentences. Therefore, this paper proposes seven Thai DP algorithms. Five different Thai DP algorithms was developed from transition-based parsing and the other two was developed from graph-based parsing. Based on Thai-PUD and English-PUD datasets, containing both long and complex sentences, the experimental results showed that all Thai DP algorithms bundled with character embedding can outperform the baselines.
Evaluating Automatic Fault Localization Using Markov Processes

Statistical fault localization (SFL) techniques are commonly compared and evaluated using a measure known as "Rank Score" and its associated evaluation process. In the latter process each SFL technique under comparison is used to produce a list of program locations, ranked by their suspiciousness scores. Each technique then receives a Rank Score for each faulty program it is applied to, which is equal to the rank of the first faulty location in the corresponding list. The SFL technique whose average Rank Score is lowest is judged the best overall, based on the assumption that a programmer will examine each location in rank order until a fault is found. However, this assumption oversimplifies how an SFL technique would be used in practice. Programmers are likely to regard suspiciousness ranks as just one source of information among several that are relevant to locating faults. This paper provides a new evaluation approach using first-order Markov models of debugging processes, which can incorporate multiple additional kinds of information, e.g., about code locality, dependences, or even intuition. Our approach, RT_rank, scores SFL techniques based on the expected number of steps a programmer would take through the Markov model before reaching a faulty location. Unlike previous evaluation methods, HT_rank can compare techniques even when they produce fault localization reports differing in structure or information granularity. To illustrate the approach, we present a case study comparing two existing fault localization techniques that produce results varying in form and granularity.
Blockchain based Data Access Control using Smart Contracts

The keystone of information security has been access control. Very often, User data is misused and users are oblivious to the use of their data by unauthorized parties. Current strategies to provide storage for confidential data and subsequent authentication involve relying on a trusted third party for the same, which could be victims of Denial of Service (DoS) attacks or technical failures. This paper examines a strategy where the underlying framework for providing Access Control is the blockchain, hence decentralizing the mechanism of providing access control. Further in this paper, we demonstrate and model the User Data access on the Ethereum framework. Personal Information of the user by a website or an application is retrieved on a need-to-know basis from the off-blockchain, as determined by the user, the true owner of the data. Personal data is highly protected and the different permissions to different websites or applications are determined by the Smart Contract.