Reviewerly/exHarmony huggingface dataset:
A blockchain course for accounting and other business students

We describe how accounting and other business professors can add blockchain as a separate course or an addition to an existing course. Based upon a popular, online course at Wayne State University (WSU), we provide examples of a course syllabus, suggested readings, group and individual assignments materials, and student comments. This course addresses two key features that Iansiti & Lakhan (2017) identified about blockchain: novelty and complexity. Given blockchain’s complexity and growing prominence in business, this course develops students’ knowledge, starting with understanding blockchain’s basic concepts and advancing to focus on business applications.
Multi-class feature selection by exploring reliable class correlation

High-dimensional multi-class data are ubiquitous in many domains and often have complicated but semantic relevance among classes. Preserving these semantic relationships in the process of feature selection is crucial to learn the nature of the objects under study. Many existing similarity preserving-based feature selection methods employ fixed similarities learned from the original feature space. However, the existence of redundant and irrelevant features in the original feature space may make the similarities unreliable to guide feature selection. In this paper, we propose to learn a dynamic similarity matrix P and a feature weighting matrix W with a two-stage method. P is calculated in the dimension reduction feature space according to W, then fixed and used to guide the next round of feature selection. Similarity matrix P and feature weighting matrix W will gradually become reliable after some repeat of the two stages. In this way, the proposed approach tries to capture reliable class correlation and select target features. Experiments on the data with noisy features and various types of public data show the attractiveness of the proposed method.
Bayesian Correlation Filter Learning With Gaussian Scale Mixture Model for Visual Tracking

Correlation filters (CF), a popular tool for visual tracking, suffer from unwanted boundary effects due to the periodic assumption needed for FFT implementation. To address this issue, spatially regularized discriminative correlation filters (SRDCF) have been proposed by introducing a weighting matrix to the regularization term. However, the existing design of spatial weighting matrix is often heuristic and non-adaptive. Inspired by recent advances in joint discrimination and reliability learning for correlation tracking, we propose a principled Bayesian correlation filter learning method using Gaussian scale mixture (GSM) model. The key idea is to decompose each CF coefficient into the product of a positive scalar multiplier and a Gaussian random variable. Treating positive multipliers as weighting coefficients, GSM-based modeling of CFs leads to a spatially adaptive regularization strategy with improved capability of handling various appearance-related uncertainty factors (e.g., scale variation, out-of-plane rotation, and motion blur). Moreover, by imposing a sparse prior over the multipliers, we can jointly learn multipliers and CFs under a unified Bayesian estimation framework. Structured GSM model allows us to better exploit the spatial correlations among CFs and further improve the tracking performance. Experimental results on OTB-2013, OTB-2015, Temple Color-128, VOT-2016, and VOT-2017 show that our tracking method performs favorably when compared with current state-of-the-art methods.
Broadcast Encryption with Size $$N^{1/3}$$ and More from k-Lin

We present the first pairing-based ciphertext-policy attribute-based encryption (CP-ABE) scheme for the class of degree 3 polynomials with compact parameters: the public key, ciphertext and secret keys comprise O(n) group elements, where n is input length for the function. As an immediate corollary, we obtain a pairing-based broadcast encryption scheme for N users with $$O(N^{1/3})$$ -sized parameters, breaking the long-standing $$\sqrt{N}$$ barrier for pairing-based broadcast encryption. All of our constructions achieve adaptive security against unbounded collusions, and rely on the (bilateral) k-Lin assumption in prime-order bilinear groups.
Earthquake Alert Device Using a Low-Cost Accelerometer and its Services

With the recent increase in the number of earthquakes in Korea, research efforts have been directed toward the real-time detection of earthquakes and the formulation of evacuation plans. Traditional seismometers can precisely record earthquakes but are incapable of processing them on-site to initiate an alert and response mechanism. By contrast, internet of things (IoT) devices equipped with accelerometers and CPUs can record and detect earthquake signals in real time and send out alert messages to nearby users. However, the signals recorded on IoT devices are noisy because of two main factors: the urban buildings and structures these devices are installed in and their cost-quality trade-off. Therefore, in this work, we provide an effective mechanism to deal with the problem of false alarms in IoT devices. We test our previously proposed artificial neural network (ANN) with different feature window sizes ranging from 2 seconds to 6 seconds and with various earthquake intensities. We find that setting the size of the feature window to a certain interval (i.e., 4-5 seconds) can improve model performance. Moreover, an evacuation route guidance platform that considers user location is proposed. The proposed platform provides and visualizes information to user devices in real time through the communication between server and user devices. In the event of a disaster, safe shelters are selected on the basis of the information entered from the server, and pedestrian paths are provided. As a result, the direct and secondary damages caused by earthquakes can be avoided.
Game-Theoretic Decision Support for Cyber Forensic Investigations

The use of anti-forensic techniques is a very common practice that stealthy adversaries may deploy to minimise their traces and make the investigation of an incident harder by evading detection and attribution. In this paper, we study the interaction between a cyber forensic Investigator and a strategic Attacker using a game-theoretic framework. This is based on a Bayesian game of incomplete information played on a multi-host cyber forensics investigation graph of actions traversed by both players. The edges of the graph represent players’ actions across different hosts in a network. In alignment with the concept of Bayesian games, we define two Attacker types to represent their ability of deploying anti-forensic techniques to conceal their activities. In this way, our model allows the Investigator to identify the optimal investigating policy taking into consideration the cost and impact of the available actions, while coping with the uncertainty of the Attacker’s type and strategic decisions. To evaluate our model, we construct a realistic case study based on threat reports and data extracted from the MITRE ATT&amp;CK STIX repository, Common Vulnerability Scoring System (CVSS), and interviews with cyber-security practitioners. We use the case study to compare the performance of the proposed method against two other investigative methods and three different types of Attackers.
Using double Weil sums in finding the c-boomerang connectivity table for monomial functions on finite fields

In this paper we characterize the c-Boomerang Connectivity Table (BCT), $$c\ne 0$$ (thus, including the classical $$c=1$$ case), for all monomial function $$x^d$$ in terms of characters and Weil sums on the finite field $${\mathbb F}_{p^n}$$ , for an odd prime p. We further simplify these expressions for the Gold functions $$x^{p^k+1}$$ for all $$1\le k<n$$ , and p odd. It is the first such attempt for a complete description for the classical BCT and its relative c-BCT, for all parameters involved, albeit in terms of characters.
Image Captioning based Smart Navigation System for Visually Impaired

For visually disabled people, navigating the streets is a challenging activity. It’s almost always difficult for them to comprehend basic street signboards or the surrounding environment. The proposed model suggests a system that can help visually impaired people understand better their surroundings. This research proposes a novel implementation of smart spectacles based on Image Captioning and Optical Character Recognition (OCR) to ease the navigation process. The system consists of a camera that is embedded in spectacles, an Image Captioning module, an OCR module, and Text-To-Speech (TTS) module. The Image Captioning module aids in the detection of notice or signboards in the immediate vicinity, while the OCR module aids in the reading of the text on the notice boards. Text-To- Speech is used primarily to translate this data into a format that is open to the visually impaired, such as voice notifications. The proposed research makes navigating on the street less challenging for visually impaired people. It also helps them to comprehend the signboards and to make optimum use of the available facilities. The presented model can successfully generate captions based on the surroundings and can read the text whenever required. The model can read texts in English, Hindi, and Marathi, making it adaptable to the needs of the vernacular population.
Social Fabric: Tubelet Compositions for Video Relation Detection

This paper strives to classify and detect the relationship between object tubelets appearing within a video as a 〈subject-predicate-object〉 triplet. Where existing works treat object proposals or tubelets as single entities and model their relations a posteriori, we propose to classify and detect predicates for pairs of object tubelets a priori. We also propose Social Fabric: an encoding that represents a pair of object tubelets as a composition of interaction primitives. These primitives are learned over all relations, resulting in a compact representation able to localize and classify relations from the pool of co-occurring object tubelets across all timespans in a video. The encoding enables our two-stage network. In the first stage, we train Social Fabric to suggest proposals that are likely interacting. We use the Social Fabric in the second stage to simultaneously finetune and predict predicate labels for the tubelets. Experiments demonstrate the benefit of early video relation modeling, our encoding and the two-stage architecture, leading to a new state-of-the-art on two benchmarks. We also show how the encoding enables query-by-primitive-example to search for spatio-temporal video relations. Code: https://github.com/shanshuo/Social-Fabric.
Blockchain Enabled Maintenance Management Framework for Military Equipment

Maintenance of military equipment plays a vital role in achieving higher war readiness. The vast number of military equipment and their deployment on distinct locations make their maintenance a challenging task. Decision-makers are always interested in knowing the latest status of equipment, develop effective maintenance strategies, and predict mission reliability as accurately as possible. To suffice this need, maintenance data of military equipment must be maintained with high granularity and accuracy, including maintenance information about every lowest maintainable unit of every military equipment. There is a profound need for a comprehensive maintenance management framework, which addresses several military-specific issues with equipment maintenance and stores the maintenance data in the utmost secure environment. In this article, we attempt to provide a novel blockchain enabled framework to make military maintenance management comprehensive and future ready. This article first provides a representation of the maintenance scenario of military equipment and later discusses the several choices made available within the blockchain technology, along with the architecture of the proposed blockchain. The proposed framework uses smart contracts to make monitoring and validation more uncompromising and with minimum human intervention. Some pragmatic analytics are presented in this article, along with the inherent blockchain advantages.
Fast &amp; Robust Image Interpolation Using Gradient Graph Laplacian Regularizer

In the graph signal processing (GSP) literature, it has been shown that signal-dependent graph Laplacian regularizer (GLR) can efficiently promote piecewise constant (PWC) signal reconstruction for various image restoration tasks. However, for planar image patches, like total variation (TV), GLR may suffer from the well-known “staircase” effect. To remedy this problem, we generalize GLR to gradient graph Laplacian regularizer (GGLR) that provably promotes piecewise planar (PWP) signal reconstruction for the image interpolation problem—a 2D grid with random missing pixels that requires completion. Specifically, we first construct two higher-order gradient graphs to connect local horizontal and vertical gradients. Each local gradient is estimated using structure tensor, which is robust using known pixels in a small neighborhood, mitigating the problem of larger noise variance when computing gradient of gradients. Moreover, unlike total generalized variation (TGV), GGLR retains the quadratic form of GLR, leading to an unconstrained quadratic programming (QP) problem per iteration that can be solved quickly using conjugate gradient (CG). We derive the means-square-error minimizing weight parameter for GGLR, trading off bias and variance of the signal estimate. Experiments show that GGLR outperformed competing schemes in interpolation quality for severely damaged images at a reduced complexity.
Fuzzy Cognitive Maps of Social-Ecological Complexity: Applying Mental Modeler to the Bonneville Salt Flats

Although often limited in terms of extent or accuracy, mental models—i.e., explanations of the surrounding world and how things work within it—provide confidence and frameworks to navigate life's uncertainties. Unfortunately, differing and yet similar mental models held collectively by groups can lead to problematic behavior, misunderstandings, and conflict on large scales. Such challenges are likely familiar to natural resource managers who, in the course of their work, must consider issues that are neither simple nor exclusively ecological or social in nature. Building mental models of various groups' understanding of a complex natural resource may help managers address the impacts of resource-related behaviors but can be a difficult task when collecting modeling data from large and diverse user groups. Using a sequential, exploratory approach, our study addresses the utility of surrogate mental modeling to explore (a) mental models held by key players from six stakeholder groups associated with Utah's Bonneville Salt Flats (US), and (b) whether these key players were confident that their personal subjective models represented their own group's thinking about Bonneville. We sought to illuminate and compare stakeholder groups' mental models of subjectively important social and ecological concepts related to Bonneville through the use of fuzzy cognitive maps (FCMs; i.e., semi-quantitative representations of mental models) constructed in Mental Modeler. Analysis revealed differences among groups' FCMs and levels of perceived complexity, as well as areas of agreement regarding the strength, direction, and character of certain social-ecological relationships. Intersections and divergences in stakeholder mental models may provide logical starting points for communal knowledge-building that can perhaps lessen tension among groups attributable to conceptual misunderstandings of resource-specific complexity.
Board-Level Functional Fault Identification Using Streaming Data

High integration densities and design complexity of printed-circuit boards make board-level functional fault identification extremely difficult. Machine learning provides an opportunity to identify functional faults with high accuracy and thereby reduce repair cost. However, the large volume of manufacturing data comes in a streaming format and exhibits time-dependent concept drift in a production environment. These drawbacks limit the effectiveness of traditional machine-learning algorithms. We propose a diagnosis workflow that utilizes online learning to train classifiers incrementally with a small chunk of data at each step. These online-learning algorithms adapt to concept drift quickly with carefully designed update rules. A hybrid algorithm is also proposed to handle the scenario that data for varying numbers of boards are collected at different times. This hybrid algorithm concurrently implements two basic models. For each data chunk, this algorithm chooses the better model with high probability. The experimental results using two boards in high-volume production show that, with the help of online learning and the proposed hybrid algorithm, the F1-score for diagnosis based on binary classifiers can be improved from 57.3% to 81.0%. The top-3 accuracy for diagnosis based on multiclass classifiers can be improved from 78.3% to 91.4%.
Inverse Interpolation and Its Application in Robust Image Steganography

Traditional steganography methods are usually designed on a lossless channel; thus, messages are often not extracted correctly from an image transmitted over a lossy channel that includes attacks such as scaling. To address this issue, in recent years, the field of robust steganography has emerged. In this paper, the process of image scaling by interpolation is first observed and serves as the basis for proposing the idea of inverse interpolation. Subsequently, the idea of constructing an inverse interpolation equation set is proposed to solve the problem of intersectional blocks during the inverse interpolation process. Then, the scaling factor’s valid range of inverse interpolation is analyzed. Next, the inverse interpolation is successfully applied in robust image steganography. A method that combines antiscaling and antidetection is proposed. Afterward, actual tests on the top 9 mobile phone brands with 28 models and 2 social communication apps that are currently popular in China are done. The scaling factor’s valid range of the proposed method is verified to match the actual lossy channel. The experimental results show that the proposed method achieves a reliable extraction of embedded messages for common interpolation scaling attacks while maintaining high statistical detection resistance.
Complex artificial neural network with applications to wireless communications

We have developed a complex artificial neural network based radio receiver technology. This is based on novel complex neurons and deep networks of them. This type of radio does not have MIMO decoder or demodulator in a classical sense but instead the radio learns to demodulate through training - and can potentially adapt to jamming through a learning process. This type of radio can continuously adapt to changing environment and can potentially increase interference resistance spectral efficiency of modern radio networks. Complex neurons can natively operate on complex numbers, including multiplying (scale, rotation) of the input data. These networks, however, have proven chaotic and the convergence has proven to be highly problematic. The difficulty of determining complex coefficients, i.e. training, has made the practical use of these networks difficult or impossible. In this research, a new type of neuron is used with a network architecture that avoids most of the issues plaguing the convergence and training of the network. Combination of a new neuron weight update algorithm and the use of layers with nonlinear selection functions and methods to avoid large single error values in backpropagation allow complex neural network to be trained with fast convergence.
Learning Multiscale Correlations for Human Motion Prediction

In spite of the great progress in human motion prediction, it is still a challenging task to predict those aperiodic and complicated motions. We believe that capturing the correlations among human body components is the key to understand the human motion. In this paper, we propose a novel multiscale graph convolution network (MGCN) to address this problem. Firstly, we design an adaptive multiscale interactional encoding module (MIEM) which is composed of two sub modules: scale transformation module (STM) and scale interaction module (SIM) to learn the human body correlations. Secondly, we apply a coarse-to-fine decoding strategy to decode the motions sequentially. We evaluate our approach on two standard benchmark datasets for human motion prediction: Human3.6M and CMU motion capture dataset. The experiments show that the proposed approach achieves the state-of-the-art performance for both short-term and long-term prediction especially in those complicated action category. We make codes publicly available at https://github.com/zhouhongh/MGCN.
Mobile Based Attendance System: Face Recognition and Location Detection using Machine Learning

Nowadays, student attendance has a significant impact on the class process as well as it affects student marks based on the authenticity of marking the attendance which expects high accuracy with more flexibility for both lecturers and students. Currently, most universities have some methods of marking student attendance, which consider either inefficient in terms of accuracy and security or bring up other issues such as time-consuming. In addition, another way of attendance requires an administrator to monitor the input of student data, which is difficult when dealing with a large number of students. This project proposes a mobile application to allow lecturers to generate class attendance and students to submit the attendance by scanning their faces using their mobile phone camera along with their location. The proposed system addressed three main components: First, automatic face detection and analysis using mobile phone cameras. Second, face recognition API that uses machine learning algorithm. Third, maps API. A testing process has been conducted through over 30 students to test the efficiency of the system in recognizing student faces as well as location. The result shows face recognition has achieved a high accuracy of detecting students’ faces even in a bad environment condition. It revealed that over 85% of the student are satisfied with the face recognition process. The system showed effective example responses by marking the student's attendance after recognizing the student's face and location, and the lecturer was able to download a report of submitted attendance.
A Partially-Sorted Concentric Layout for Efficient Label Localization in Augmented Reality

A common approach for Augmented Reality labeling is to display the label text on a flag planted into the real world element at a 3D anchor point. When there are more than just a few labels, the efficiency of the interface decreases as the user has to search for a given label sequentially. The search can be accelerated by sorting the labels alphabetically, but sorting all labels results in long and intersecting leader lines from the anchor points to the labels. This paper proposes a partially-sorted concentric label layout that leverages the search efficiency of sorting while avoiding the label display problems of long or intersecting leader lines. The labels are partitioned into a small number of sorted sequences displayed on circles of increasing radii. Since the labels on a circle are sorted, the user can quickly search each circle. A tight upper bound derived from circular permutation theory limits the number of circles and thereby the complexity of the label layout. For example, 12 labels require at most three circles. When the application allows it, the labels are presorted to further reduce the number of circles in the layout. The layout was tested in a user study where it significantly reduced the label searching time compared to a conventional single-circle layout.