Reviewerly/exHarmony huggingface dataset:
A Class of Narrow-Sense BCH Codes

BCH codes are an important class of cyclic codes which have applications in satellite communications, DVDs, disk drives, and two-dimensional bar codes. Although BCH codes have been widely studied, their parameters are known for only a few special classes. Recently, Ding et al. made some new progress in BCH codes. However, we still have very limited knowledge on the dimension of BCH codes, not to mention the weight distribution of BCH codes. In this paper, we generalize the results on BCH codes from several previous papers. 1) The dimension of narrow-sense BCH codes of length ((q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> -1)/λ) with designed distance 2 ≤ δ ≤ ((q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Γ(m+1)/2⌉</sup> - 1)/(λ) + 1) is settled, where λ is any factor of (q - 1). 2) The weight distributions of two classes of narrow-sense BCH codes of length ((q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> - 1)/2) with designed distance δ = (((q - 1)q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m-1</sup> - q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⌊(m-1)12⌋</sup> - 1)/2) and δ = (((q - 1)q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m-1</sup> - q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⌊(m+1)/2⌋</sup> - 1)/2) are determined. 3) The weight distribution of a class of BCH codes of length ((q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> - 1)/(q - 1)) is determined. In particular, a subclass of this class of BCH codes is optimal with respect to the Griesmer bound. Some optimal linear codes obtained from this class of BCH codes are characterized.
Dual residual attention module network for single image super resolution

Recent studies show that research on single image super-resolution (SISR) has achieved great success by using deep convolutional neural network (CNN). Different types of features obtained in deep CNN have different contribution. However, most of the previous models ignore the distinction between different features and deal with them in the same way, which affects the representational capacity of the models. On the other hand, receptive fields with different size capture diverse features from the input. Based on the above considerations, we propose a dual residual attention module (DRAM) network which concentrates on recovering the high-frequency details and sharing the information between two receptive fields of different sizes. We construct local information integration (LFI) module as the basic module to make full use of the local information. The LFI module is a cascade of several dual residual attention fusion (DRAF) blocks with a dense connection structure. The feature modulation can focus on important features and suppress unimportant ones. The evaluation results on five benchmark datasets demonstrate the superiority of our DRAM network against the state-of-the-art algorithms.
ANN-Based Outlier Detection for Wireless Sensor Networks in Smart Buildings

Wireless sensor network (WSN) is an emerging technology with a wide range of potential applications in smart buildings. The measuring process by using WSNs in the actual environment always introduces noise, errors, accidents, and other potential outliers to the data collected by the sensors. It is crucial to establish an effective approach for outlier detection and recovery in the real applications of WSNs. In this paper, we propose an outlier detection and recovery approach using artificial neural network (ANN), which can be used to determine whether the temperature values measured by the sensors in WSNs are outliers. The experimental results in real building show that the proposed ANN-based models can provide a reasonably good prediction of the temperature and high accuracy in buildings compared with the hidden Markov model (HMM)-based approach, which can potentially be used for outlier detecting and thermal controlling in the Internet of Things (IoT) applications.
At Home and Abroad: The Use of Denial-of-service Attacks during Elections in Nondemocratic Regimes

In this article, we study the political use of denial-of-service (DoS) attacks, a particular form of cyberattack that disables web services by flooding them with high levels of data traffic. We argue that websites in nondemocratic regimes should be especially prone to this type of attack, particularly around political focal points such as elections. This is due to two mechanisms: governments employ DoS attacks to censor regime-threatening information, while at the same time, activists use DoS attacks as a tool to publicly undermine the government’s authority. We analyze these mechanisms by relying on measurements of DoS attacks based on large-scale Internet traffic data. Our results show that in authoritarian countries, elections indeed increase the number of DoS attacks. However, these attacks do not seem to be directed primarily against the country itself but rather against other states that serve as hosts for news websites from this country.
A Generalized Gaussian Process Model for Computer Experiments With Binary Time Series

Non-Gaussian observations such as binary responses are common in some computer experiments. Motivated by the analysis of a class of cell adhesion experiments, we introduce a generalized Gaussian process model for binary responses, which shares some common features with standard GP models. In addition, the proposed model incorporates a flexible mean function that can capture different types of time series structures. Asymptotic properties of the estimators are derived, and an optimal predictor as well as its predictive distribution are constructed. Their performance is examined via two simulation studies. The methodology is applied to study computer simulations for cell adhesion experiments. The fitted model reveals important biological information in repeated cell bindings, which is not directly observable in lab experiments. Supplementary materials for this article are available online.
Learning restricted Boltzmann machines via influence maximization

Graphical models are a rich language for describing high-dimensional distributions in terms of their dependence structure. While there are algorithms with provable guarantees for learning undirected graphical models in a variety of settings, there has been much less progress in the important scenario when there are latent variables. Here we study Restricted Boltzmann Machines (or RBMs), which are a popular model with wide-ranging applications in dimensionality reduction, collaborative filtering, topic modeling, feature extraction and deep learning.
One-pass person re-identification by sketch online discriminant analysis

Person re-identification (re-id) is to match people across disjoint camera views in a multi-camera system, and re-id has been an important technology applied in smart city in recent years. However, the majority of existing person re-id methods assumes all data samples are available in advance for training. However, in a real-world scenario person images detected from multi-camera system are coming sequentially, and thus these methods are not designed for processing sequential data in an online way. While there is a few work on discussing online re-id, most of them require considerable storage of all passed labelled data samples that have been ever observed. In this work, we present an one-pass person re-id model that adapts the re-id model based on each newly observed data and no passed data are required for each update. More specifically, we develop a Sketch online Discriminant Analysis (SoDA) by embedding sketch processing into Fisher discriminant analysis (FDA). SoDA can efficiently keep the main data variations of all passed samples in a low rank matrix when processing sequential data samples, and estimate the approximate within-class variance (i.e. within-class covariance matrix) from the sketch data information. We provide theoretical analysis on the effect of the estimated approximate within-class covariance matrix. In particular, we derive upper and lower bounds on the Fisher discriminant score (i.e. the quotient between between-class variation and within-class variation after feature transformation) in order to investigate how the optimal feature transformation learned by SoDA sequentially approximates the offline FDA that is learned on all observed data. Extensive experimental results have shown the effectiveness of our SoDA and empirically support our theoretical analysis.
A combined binary defocusing technique with multi-frequency phase error compensation in 3D shape measurement

Binary defocusing technology is widely used in three-dimensional (3D) structured light measurement technologies because of its high-speed measurement capability. Owing to the limitations regarding the type of binary fringe and the degree of defocus, the accuracy and speed of 3D measurement may not be satisfactory for certain industrial applications. This paper addresses this problem and proposes a new binary defocusing technique through a combination of a 1D fringe modulation technique with a 2D fringe modulation technique. First, we select a square binary fringe modulation technique for high frequency fringes generation and use an improved Stucki error diffusion dithering technique with serpentine scanning for low frequency fringes generation. Based on the analysis of the theoretical model of the phase error caused by binary fringes, a double three-step phase shifting method is adopted to compensate the phase error in the wrapped phase map. The compensated phase is then unwrapped using a multi-frequency method. The proposed method was verified experimentally through a white board, a real complex object and a standard block with a depth of 1 mm. The measurement errors were shown to be reduced by 16.9% or more with the proposed method.
Latent Network Summarization

Motivated by the computational and storage challenges that dense embeddings pose, we introduce the problem of latent network summarization that aims to learn a compact, latent representation of the graph structure with dimensionality that is independent of the input graph size (\i.e., #nodes and #edges), while retaining the ability to derive node representations on the fly. We propose Multi-LENS, an inductive multi-level latent network summarization approach that leverages a set of relational operators and relational functions (compositions of operators) to capture the structure of egonets and higher-order subgraphs, respectively. The structure is stored in low-rank, size-independent structural feature matrices, which along with the relational functions comprise our latent network summary. Multi-LENS is general and naturally supports both homogeneous and heterogeneous graphs with or without directionality, weights, attributes or labels. Extensive experiments on real graphs show 3.5-34.3% improvement in AUC for link prediction, while requiring 80-2152x less output storage space than baseline embedding methods on large datasets. As application areas, we show the effectiveness of Multi-LENS in detecting anomalies and events in the Enron email communication graph and Twitter co-mention graph.
Distortion-aware image retargeting based on continuous seam carving model

The seam-carving algorithm is a classic context-aware image retargeting method and has been extensively studied for years; however, distortions exist as the discrete iteration of least- energy computation. We propose a continuous seam carving model through the just noticeable distortion (JND) detection at every iteration and accumulative energy weight. The JND of every pixel is calculated by the minimal just-noticeable distortion energy of adjacent-pixel conflicting displacements between seam carving iterations. The mean field approximation is used to efficiently solve the problem. In this way, the proposed energy weight can accumulatively calculate the JND information of recent k iterations and passed down to the next iteration for distortion avoidance. The superior performance of the proposed continuous seam carving model, as compared with state-of-art image retargeting approaches in RetargetMe database, was demonstrated experimentally.
Estimating Uncertainty in Deep Learning for Reporting Confidence to Clinicians when Segmenting Nuclei Image Data

Deep Learning, which involves powerful black box predictors, has achieved a state-of-the-art performance in medical image analysis such as segmentation and classification for diagnosis. However, in spite of these successes, these methods focus exclusively on improving the accuracy of point predictions without assessing the quality of their outputs. Knowing how much confidence there is in a prediction is essential for gaining clinicians' trust in the technology. Monte-Carlo dropout in neural networks is equivalent to a specific variational approximation in Bayesian neural networks and is simple to implement without any changes in the network architecture. It is considered state-of-the-art for estimating uncertainty. However, in classification, it does not model the predictive probabilities. This means that we are not capturing the true underlying uncertainty in the prediction. In this paper, we propose an uncertainty estimation framework for classification by decomposing predictive probabilities into two main types of uncertainty in Bayesian modelling: aleatoric and epistemic uncertainty (representing uncertainty in the quality of the data and in the model parameters, respectively). We demonstrate that the proposed uncertainty quantification framework using the Bayesian Residual U-Net (BRUNet) provides additional insight for clinicians when analysing images with help from deep learners. In addition, we demonstrate how the resulting uncertainty depends on the dropout rates using images from nuclei in divergent medical images.
AR Pointer: Advanced Ray-Casting Interface Using Laser Pointer Metaphor for Object Manipulation in 3D Augmented Reality Environment

In this paper, we propose AR Pointer, a new augmented reality (AR) interface that allows users to manipulate three-dimensional (3D) virtual objects in AR environment. AR Pointer uses a built-in 6-degrees of freedom (DoF) inertial measurement unit (IMU) sensor in an off-the-shelf mobile device to cast a virtual ray that is used to accurately select objects. It is also implemented using simple touch gestures commonly used in smartphones for 3D object manipulation, so users can easily manipulate 3D virtual objects using the AR Pointer, without a long training period. To demonstrate the usefulness of AR Pointer, we introduce two use-cases, constructing an AR furniture layout and AR education. Then, we conducted two experiments, performance tests and usability tests, to represent the excellence of the designed interaction methods using AR Pointer. We found that AR Pointer is more efficient than other interfaces, achieving 39.4% faster task completion time in the object manipulation. In addition, the participants gave an average of 8.61 points (13.4%) on the AR Pointer in the usability test conducted through the system usability scale (SUS) questionnaires and 8.51 points (15.1%) on the AR Pointer in the fatigue test conducted through the NASA task load index (NASA-TLX) questionnaire. Previous AR applications have been implemented in a passive AR environment where users simply check and pop up the AR objects those are prepared in advance. However, if AR Pointer is used for AR object manipulation, it is possible to provide an immersive AR environment for the user who want/wish to actively interact with the AR objects.
An Analysis of Non-standard Transactions

In Bitcoin, the most common kind of transactions is in the form “Bob pays Alice”, and it is based on the Pay to-Public Key Hash(P2PKH) script, which are resolved by sending the public key and a digital signature created by the corresponding private key. P2PKH transactions are just one among many standard classes: a transaction is standard if it passes Bitcoin Core’s IsStandard() and IsStandardTx() tests. However, the creation of ad-hoc scripts to lock (and unlock) transactions allows for also generating non-standard transactions, which can be nevertheless broadcast and mined as well. In this work, we explore the Bitcoin block-chain with the purpose to analyze and classify standard and non-standard transactions, understanding how much the standard behaviour is respected.
Partially Covered Face Detection in Presence of Headscarf for Surveillance Applications

In the past few years, the application of surveillance for security and smart cities are growing rapidly. The human detection based on the surveillance videos is a complex task and traditional clothing such as headscarf makes this task even more difficult. The surveillance systems designed for many countries are required to be able to recognize the people with these traditional clothing. In this paper, a computer vision system for partially covered face detection in low resolution surveillance videos containing traditional Middle Eastern clothing including the headscarf is presented. The proposed framework uses a combination of Haar cascade and Locally Binary Patterns Histogram (LBPH) for feature extraction and the Support Vector Machine (SVM) algorithm for face classification. A large dataset of a crowded office environment in Middle East is collected and used for evaluation of the proposed model. The experimental results show that the proposed method has acceptable results for face detection in complex surveillance scenarios.
Reducing Automotive Counterfeiting Using Blockchain: Benefits and Challenges

Counterfeiting constitutes a major challenge in current supply chains leading to millions of dollars of lost revenue for the involved parties every year. Hardware-based authentication solutions built upon Physically Unclonable Functions (PUF) and RFID tags prevent counterfeiting in a multiparty supply chain context. Unfortunately, these solutions cannot prevent counterfeiting and duplication attacks by supply chain parties themselves, as they can simply equivocate by duplicating products in their local and unique activity ledger. In this work, we study the benefits and challenges of using distributed ledger technology (or blockchain) to prevent counterfeiting even in the presence of malicious supply chain parties. In particular, we show that the provision of a distributed and append-only ledger jointly governed by supply chain parties themselves, by means of a distributed consensus algorithm, makes permissioned blockchains such as Hyperledger Fabric a promising approach towards mitigating counterfeiting. At the same time, the distributed nature of the ledger also possesses a privacy challenge as competing supply chain parties strive to protect their businesses from the prying eyes of competitors. Additionally, we show our efforts to build a blockchain-based counterfeiting prevention system for automotive supply chains, albeit the lessons learned are seamlessly applied to other supply chains. From our experience, we highlight two lessons: (i) the requirement of adding identities other than supply chain entities themselves to facilitate the tracking of goods; and (ii) the challenges derived from privacy enforcement in such a permissioned scenario. We thus finalize this work with a set of challenges that need to be overcome to achieve the best of both worlds: a solution to the counterfeiting problem using distributed ledger technology while providing the privacy notions of interest for supply chain parties.
Learning Convolutional Neural Networks for Object Detection with Very Little Training Data

In recent years, convolutional neural networks have shown great success in various computer vision tasks such as classification, object detection, and scene analysis. These algorithms are usually trained on large datasets consisting of thousands or millions of labeled training examples. The availability of sufficient data, however, limits possible applications. While large amounts of data can be quickly collected, supervised learning further requires labeled data. Labeling data, unfortunately, is usually very time-consuming and literally expensive. This chapter addresses the problem of learning with very little labeled data for extracting information about the infrastructure in urban areas. The aim is to recognize particular traffic signs in crowdsourced data to collect information which is of interest to cyclists. The presented system for object detection is trained with very few training examples. To achieve this, the advantages of convolutional neural networks and random forests are combined to learn a patch-wise classifier. In the next step, the random forest is mapped to a neural network and the classifier is transformed to a fully convolutional network. Thereby, the processing of full images is significantly accelerated and bounding boxes can be predicted. Finally, GPS-data is integrated to localize the predictions on the map and multiple observations are merged to further improve the localization accuracy. In comparison to faster R-CNN and other networks for object detection or algorithms for transfer learning, the required amount of labeled data is considerably reduced.
An Interactive Chart of Biography

Joseph Priestley's Chart of Biography is a masterpiece of hand-drawn data visualization. He arranged the lifespans of around 2,000 individuals on a timeline, and the chart obtained great value for teaching purposes. We present a generic, interactive variant of the chart adopting Priestley's basic design principles. Our proposed visualization allows for dynamically defining person groups to be visually compared on different zoom levels. We designed the visualization in cooperation with musicologists having multifaceted research interests on a biographical database of musicians. On the one hand, we enable deriving new relationships between musicians in order to extend the underlying database, and on the other hand, our visualization supports analyzing time-dependent changes of musical institutions. Various usage scenarios outline the benefit of the Interactive Chart of Biography for research in musicology.
Engineering Knowledge Graph for Keyword Discovery in Patent Search

Abstract Patent retrieval and analytics have become common tasks in engineering design and innovation. Keyword-based search is the most common method and the core of integrative methods for patent retrieval. Often searchers intuitively choose keywords according to their knowledge on the search interest which may limit the coverage of the retrieval. Although one can identify additional keywords via reading patent texts from prior searches to refine the query terms heuristically, the process is tedious, time-consuming, and prone to human errors. In this paper, we propose a method to automate and augment the heuristic and iterative keyword discovery process. Specifically, we train a semantic engineering knowledge graph on the full patent database using natural language processing and semantic analysis, and use it as the basis to retrieve and rank the keywords contained in the retrieved patents. On this basis, searchers do not need to read patent texts but just select among the recommended keywords to expand their queries. The proposed method improves the completeness of the search keyword set and reduces the human effort for the same task.