Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Graph Encryption for Approximate Constrained Shortest Distance Queries

Constrained shortest distance (CSD) queries are a valuable extension of the traditional pairwise shortest distance computation over graph-structured data, where the answers to the queries should fulfill a cost constraint (e.g., the toll payment in road networks). With the popularity of cloud computing, data owners have a strong desire to migrate their privacy-sensitive graphs to remote servers without losing the ability to query them. Existing graph encryption schemes cannot provide security guarantees for CSD queries. In this paper, we present Acro, a graph encryption scheme, which executes approximate CSD queries securely. The homomorphic encryption and the symmetric-key primitives are applied to our scheme. Through a security analysis, we prove that Acro meets the security definition of CQA2-security. The prototype of Acro is implemented and evaluated using real datasets. The results show that our proposal outperforms a state-of-the-art baseline in terms of query accuracy at the cost of enlarging query completion time.
Urban Talent Demand Analysis Based on Deep Learning and Wavelet Threshold Denoising

Based on the relevant data of the talent market in A city, this paper analyzes the local talent demand situation. Firstly, all industries are divided into seven categories, and the local talent demand situation is analyzed from the three aspects of employment expectation, expected occupation and expected education background. The ratio of report ratio, industry employment demand and high academic qualifications are selected. The data is processed using wavelet threshold denoising and a NAR neural network model is established for prediction. In terms of employment, the relevant literature was consulted, and the employment rate, average salary and employment satisfaction data of Chinese students were collected, and the employment situation was predicted by combining the GM (1, 1) prediction model. Finally, starting from the number of applicants, the number of admissions, academic qualifications and the seven fast-growing industries in the city A, the SOM neural network model is established to compare the above-mentioned indicators of the city A with other cities in China, matching the same characteristics as the city A. Building an “intelligent forecasting and decision-making evaluation system” to help government departments and related institutions predict future urban development related information.
Objective Edge Similarity Metric for denoising applications in MR images

Edge Similarity Metrics (ESMs) are necessary to objectively quantify the inadvertent blur at the edge pixels which occurs during denoising. They are helpful for evaluating edge-preserving capability of nonlinear filters. Most of the ESMs in literature, consider similarity of either strength of the edges or their direction individually. They lag in terms of concordance with subjective edge similarity ratings. An Objective Edge Similarity Metric (OESM) which considers all three attributes of edges; strength, direction and width together, is proposed in this paper. Pearson’s Correlation shown by Gradient Magnitude Similarity Deviation (GMSD), Gradient Similarity Measure (GSM), Edge Strength Similarity Index Metric (ESSIM) and OESM with Subjective Edge Similarity Score (SESS) are −0.9669 ± 0.0028, 0.9566 ± 0.0053, 0.9507 ± 0.0057 and 0.9848 ± 0.0038, respectively. OESM is able to measure the degree of edge similarity between images more efficiently than GMSD, GSM and ESSIM. It reflects the perceptual edge similarity between images more accurately than GMSD, GSM and ESSIM.
Convolutional Neural Network Long Short-Term Memory (CNN + LSTM) for Histopathology Cancer Image Classification

Deep learning algorithm such as Convolutional Neural Networks (CNN) is popular in image recognition, object recognition, scene recognition and face recognition. Compared to traditional method in machine learning, Convolutional Neural Network (CNN) will give more efficient results. This is due to Convolutional Neural Network (CNN) capabilities in finding the strong feature while training the image. In this experiment, we compared the Convolutional Neural Network (CNN) algorithm with the popular machine learning algorithm basic Artificial Neural Network (ANN). The result showed some improvement when using Convolutional Neural Network Long Short-Term Memory (CNN + LSTM) compared to the multi-layer perceptron (MLP). The performance of the algorithm has been evaluated based on the quality metric known as loss rate and classification accuracy.
A Local Type-2 Fuzzy Set Based Technique For He Stain Image Enhancement

The proposed approach in the paper comes under “Advanced Soft Computing Based Medical Image Processing Research” and the work has been conducted by Dr. Dibya Jyoti Bora (Assistant Professor), School of Computing Sciences, The Assam Kaziranga University, Jorhat, Assam in the year 2018-2019.&#x0D; Introduction: HE stain images, although considered as the golden standard for medical image diagnosis, are still found to suffer from poor contrast and degradation in color quality. In this paper, a Type-2 fuzzy set-based enhancement technique is proposed for HE stain image enhancement with special care towards color-based computations and measurements.&#x0D; Methods: This paper introduces a new approach based on Type-2 fuzzy set for HE stain image enhancement where Bicubic Interpolation plays an important part. Unsharp Masking is also employed as a post enhancement factor.&#x0D; Results: From the results, it is clearly visible that cell nuclei and other cell bodies are easily distinguishable from each other in the enhanced result produced by our proposed approach. It implies that vagueness in the edges surrounding the objects in the original image is removed to an acceptable level.&#x0D; Conclusions: The proposed approach is found to be, through both subjective and objective evaluations, an efficient preprocessing technique for a better HE stain image analysis.&#x0D; Originality: The ideas involved in this paper are original. If work by other researchers are mentioned in any part of the paper, then they are cited properly.&#x0D; Limitation: The relatively high time complexity is the only limitation associated with the proposed approach.
Hierarchical-Document-Structure-Aware Attention with Adaptive Cost Sensitive Learning for Biomedical Document Classification

Biomedical document classification is a fundamental task in biomedical field. Existing methods do not make full use of the hierarchically semantic structures in biomedical documents which can be utilized to improve the performance of biomedical document classification. In this paper, according to the hierarchical structures in given biomedical documents, we propose two models for biomedical document classification, which are based on the semantically hierarchical attention mechanism. Specifically, we utilize a hierarchical attention mechanism to model biomedical documents, taking into account simultaneously multiple-level semantic relationships in documents. In addition, an adaptive cost sensitive learning method is proposed to address the data imbalance issue. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed methods.
Steady Model for Classification of Handwritten Digit Recognition

Handwritten digit recognition plays an important role not only in computer vision but also in pattern recognition. Handwritten digit recognition is the competence of a machine to receive, calculate and decipher a human handwritten input from sources such as handwritten manuscripts, especially created before the advent of a digital revolution and digital images.This work implements the system to read the handwritten digits with a custom novel method identical to the amalgamation of different techniques, including principal component analysis, support vector machine and K-nearest neighbours to recognize and classify handwritten digits into their respective labels. PCA algorithm finds out the best linear combinations of the original features so that the variance along the new feature is maximum. Recognition of characters is done using KNN nonparametric machine learning algorithm, and SVM lowers the generalization error of the overall classifier. The proposed work does the analysis on digit data set having a total of 70,000 image samples. The performance of the system is analysed using different measurement metrics like precision, recall, f1 score and support, and the recognition of the patterns in the images shows the result with classification accuracy of 97%.
OASM: An overload‐aware workload scheduling method for cloud computing based on biogeographical optimization

Summary With the daily increase in the number of cloud users and the volume of submitted workloads, load balancing (LB) over clouds followed by a reduction in users' response time is emerging as a vital issue. To successfully address the LB problem, we have optimized workload distribution among virtual machines (VMs). This approach consists of two parts: Firstly, a meta‐heuristic method based on biogeographical optimization for workload dispatching among VMs is introduced; secondly, we propose an innovative heuristic algorithm inspired by the “Banker algorithm” that runs in core scheduler to control and avoid VM overloads. The combination of these two (meta‐)heuristic algorithms constitutes an LB approach through which we have been able to reduce the value of the makespan to a reasonable time frame. Moreover, an information base repository (IBR) is introduced to maintain the online processing status of physical machines (PMs) and VMs. In our approach, data stored in IBR are retrieved when needed. This approach is compared with well‐known (non‐)evolutionary approaches, such as round‐robin, max‐min, MGGS, and TBSLB‐PSO. Experimental results reveal that our proposed approach outperforms its counterparts in a heterogeneous environment when the resources are smaller than the workloads. Moreover, the utilization of physical resources gradually increases. Therefore, optimal workload scheduling, as well as the lack of overload occurrence, results in a reduction in makespan.
An Architecture Principle Measurement Instrument Tested in Real-Life

A high percentage of information system projects still fails due to poor implementation of requirements. Over the years, investigations by numerous scientists suggest that architecture principles are important in the successful implementation of those information systems requirements. However, these investigations are of a theoretical nature; until now, no validation in practice has taken place. Our research stresses this empirical validation: do architecture principles work in real-life situations? To find this evidence, we need an instrument to measure architecture principles, in order to establish the connection between principle and project success. The focus of this paper is such an architecture principle measurement instrument. We describe the results of a literature study, yielding both the definition and the characteristics of the architecture principle. Besides the measurement instrument, we describe the related measurement method, including the test in a real-life case. Based on the outcome of the case study, we extend the instrument with additional architecture principles characteristics and attributes, and we improve the measurement method.
Policy based Ensembles for applying ML on Big Data

When creating real-world machine learning applications, system developers have to deal with the challenges of a dynamic environment where conditions change frequently, data has uncertainty, and new unanticipated situations are encountered. This requires a flexible approach in deciding how to use, adapt and create an AI model. Ensemble learning, where multiple models are trained, and use concurrently provides one way to address some of the issues. However, ensembles as used within the AI literature have primarily focused on creating a better model in a static environment. If we couple ensemble models with the concept of policy based control, we can create a system that is able to deal better with real-world scenarios. If we further augment the system so that it can generate its own policies, we can make progress towards the goal of a broad AI which can dynamically adapt itself. In this paper, we present an architecture for policy based ensemble, and show how it can lead to an approach towards broad AI.
PPD: Permutation Phase Defense Against Adversarial Examples in Deep Learning

Deep neural networks have demonstrated cutting edge performance on various tasks including classification. However, it is well known that adversarially designed imperceptible perturbation of the input can mislead advanced classifiers. In this paper, Permutation Phase Defense (PPD), is proposed as a novel method to resist adversarial attacks. PPD combines random permutation of the image with phase component of its Fourier transform. The basic idea behind this approach is to turn adversarial defense problems analogously into symmetric cryptography, which relies solely on safekeeping of the keys for security. In PPD, safe keeping of the selected permutation ensures effectiveness against adversarial attacks. Testing PPD on MNIST and CIFAR-10 datasets yielded state-of-the-art robustness against the most powerful adversarial attacks currently available.
Saliency and Tracking based Semi-supervised Learning for Orbiting Satellite Segmentation

The trajectory and boundary of an orbiting satellite are fundamental information for on-orbit repairing and manipulation by space robots. This task, however, is challenging owing to the freely and rapidly motion of on-orbiting satellites, the quickly varying background and the sudden change in illumination conditions. Traditional segmentation usually relies on a large annotated dataset and needs to be pre-trained for each target, which exhausts much time in both training and testing due to the large number and resolution of the images. In this paper, we proposed a STSS (Saliency and Tracking based Semi-supervised Learning for Segmentation) algorithm that provides the segmentation binary mask of target satellites at 12 frames per second without requirement of annotated data. Our method, STSS, improves the segmentation performance by generating a saliency map based semi-supervised on-line learning approach within the initial bounding box estimated by tracking. Experiment is evaluated on our generated dataset, which contains various challenges including variation in target, background and illumination condition.
Learning to Generate Diverse and Authentic Reviews via an Encoder-Decoder Model with Transformer and GRU

Fake reviews automatically generated by machine learning models can be manipulated to influence the customers opinions, which is a great threat to online review platforms like social networks and E-commerce websites. Previous review generation methods generally adopt either businesses information (e.g. location and products) or existing review texts from consumers as inputs, while currently no approach that utilizes both types of information has been reported. As business information can help generated reviews gain relevance, and existing user reviews help improve the diversity of generated reviews, we envision that an integration of these two types of information is likely to result in a better review generator. To this end, we propose an encoder-decoder model to produce authentic and diverse reviews, which applies Transformer and mutative Gated Recurrent Unit (GRU) to encode the business information and the customer reviews, respectively. In addition, to address the lack of suitable metrics for evaluating the diversity of reviews, we developed a novel text diversity metric called DMet. Our experiments on Yelp dataset demonstrate that the model we developed can produce reviews with better quality and diversity as compared to existing methods, and DMet is able to closely match human judgment in evaluating text diversity.
Autonomous Garage Parking of a Car-Like Robot Using a Fuzzy PD + I Controller

In this work a path following algorithm for autonomous garage parking was designed. The main objective of the algorithm is to park a vehicle avoiding collisions. We designed a fuzzy PD + I controller to decrease the error generated between the real position and the previously generated objective position. We present simulations results to validate the analysis and to demonstrate how the fuzzy controller solved this tracking problem.
The financial motivation to keep information secure

The number one driver of the cyber security industry right now is the global push to digitally transform businesses. As a result, the way that companies need to protect their data and assets is changing and the security challenges they are facing are often situations with which they have very little experience.
Towards Faster Distributed Deep Learning Using Data Hashing Techniques

Nowadays, deep learning is a crucial part of a variety of big data applications. Both the vast amount of data and the high complexity of the state-of-the-art neural networks have led to perform the network training in a distributed manner accross clusters. Since synchronization overheads are usually fatal for the training's performance, asynchronous training is usually preferred in such cases. However, this training mode is sensitive to conflicting updates. Such updates most commonly occur when the workers train on a totally different part of the data. To reduce this phenomenon, in this paper, we propose the use of hashing schemes when distributing training data across workers.
A Five-Year Retrospective on an Applied Baccalaureate Program in Software Development

This experience report documents an intervention that started five years ago, in 2014. Green River College, a community college in a suburb of Seattle, Washington, launched a new applied baccalaureate program in Software Development, with a goal of preparing place-bound, underrepresented, and underserved students for employment as entry-level software developers. At program inception, community college baccalaureate degrees were relatively new across the nation and there was little precedent established in terms of program design, particularly as these new degree programs were not intended to emulate traditional undergraduate Computer Science programs, but rather provide an alternative pathway to the baccalaureate and workforce. The Bachelor of Applied Science (BAS) in Software Development is designed as a "2+2" program, where students enter the program at third-year/junior-level standing after completing their two-year applied associate degree. The BAS Software Development curriculum includes courses in both "computer science fundamentals" and industry-focused, project-based learning courses. Since program inception, the curriculum has evolved based on evidence and feedback from students, instructors, alumni, and industry partners. Initial key results at the five-year mark include a review of enrollment, completion, diversity, and placement data. As of June 2019, the program has 132 graduates (degrees conferred), with 52 new students on track to enter the program in the 2019-2020 academic year. A recent snapshot of currently enrolled students indicates the median age is 24, 23% are women, 40% are students of color, 39% are first generation college students, and 40% are receiving need-based financial aid. Graduates of the program have placed into roles that include Software Development Engineers, Solutions Engineers, and Technical Program Managers.
Towards Tracking Trajectory of Planar Quadrotor Models

The paper introduces the basis to control a simple planar quadrotor model in the tracking trajectory problem. The dynamics of the model is developed and a control system is designed and implemented to tracking two different trajectories without obstacles. General control system contains a PD controller to drive altitude (motion in z direction), and a cascade control scheme to drive y position by controlling orientation of roll angle. Results of the error position and command variables on two different trajectories are analyzed.
Clustering of Riding in Tandem Incidents using K-means: A Case Study in the Philippines

Riding in tandem crimes is increasing every year and have doubled over the past five years in the Philippines. However, scant research on analyzing riding in tandem data is available. This study aims to cluster riding in tandem data in the Philippines using k-means algorithm. This study shows four major crime clusters such as shooting, car-napping, robbery and others. These four major crime was categorized into three such as killed, wounded and unharmed and most reported crime category, which is robbery unharmed having the highest crime rates between 2011 to 2013 in the Philippines. The results also show a decreasing trend of riding in tandem crimes in the Philippines. This contributes by providing an understanding of riding in tandem crime in the Philippines.
An Interactive Online Audience Extension System

In online advertising market, advertisers aim to target the right audience through media channels such as display, mobile, video, or social with limited budget. Usually the advertiser can collect high valuable users from their existing customs, whose size however is usually small. Hence, it is very important for advertisers to reach more audiences who can potentially be turned into future customers. How to induce the preferable attributes from the seed audience and hence identify more similar users from billions of cookie-able users within the runtime of a few minutes is a challenge to data science techniques. This is an important problem and many researchers have proposed several solutions [1]-[6] In this paper, we propose an efficient approach which can return a set of Boolean rules that defines the extended audience by analyzing the seed audience. As an additional feature, the proposed system also provides an option for advertisers to participate in the decision making process which could potentially improve the user reachability as well as the campaign performance. The experiment results show the superior performance of the proposed system compared with other methods in terms of efficiency, user reachability, action rate, click through rate, etc.
<i>Collective Currents</i>: Exploring Sustainability through a Collaborative and Interactive Installation

Abstract Water issues in the western United States include a long history of struggle, controversy and politics. Collaboration and compromise are required to achieve desirable outcomes in water quality and water rights. Collective Currents is an interactive art installation, developed collaboratively by a computer engineer and a multimedia artist, that explores the idea of cooperative experience in both literal and conceptual ways and creates an environment that makes reference to and engages the viewer to think about our ability to understand and solve environmental issues, specifically water quality and conservation, through collaboration.
Robustness Evaluation of Deep Learning Models Based on Local Prediction Consistency

It is important to estimate the performance gap of a given deep learning model on the target data set, since discrepancy or bias between source and target domains is a common and fundamental problem in the practice of machine learning techniques. Without any assumptions on data bias, such as label shift or covariate shift and without target data labels, we propose a robustness estimation method based on prediction consistency evaluation between source and target data in the neighborhood of the source samples. Considering outliers and whether the user provided model is fully trained, a variety of variant methods are also tried, including setting neighborhood threshold to average intra-class distance for each category and relative robustness. Furthermore, the time complexity of this method is O(nlogn), which is applicable for large datasets. Experiments on the handwritten digit recognition and Japanese handwriting recognition show that the proposed methods are effective.