Reviewerly/exHarmony huggingface dataset:
Attacking a CNN-based Layout Hotspot Detector Using Group Gradient Method

Deep neural networks are being used in disparate VLSI design automation tasks, including layout printability estimation, mask optimization, and routing congestion analysis. Preliminary results show the power of deep learning as an alternate solution in state-of-the-art design and sign-off flows. However, deep learning is vulnerable to adversarial attacks. In this paper, we examine the risk of state-of-the-art deep learning-based layout hotspot detectors under practical attack scenarios. We show that legacy gradient-based attacks do not adequately consider the design rule constraints. We present an innovative adversarial attack formulation to attack the layout clips and propose a fast group gradient method to solve it. Experiments show that the attack can deceive the deep neural networks using small perturbations in clips which preserve layout functionality while meeting the design rules. The source code is available at https://github.com/phdyang007/dlhsd/tree/dct_as_conv.
Two-stage Recognition and Beyond for Compound Facial Emotion Recognition

Facial emotion recognition is an inherently difficult problem, not only due to individual facial feature diversity but also racial and cultural differences. Compound facial emotion recognition makes the problem even more difficult because the discrimination between the dominant emotion and the complementary emotion is usually weak. To mitigate this problem, we propose the two-stage recognition method. The first stage is a coarse recognition stage and the second stage is a fine recognition stage. By doing this the classification for symmetrical emotion labels is enhanced. Beyond this, we make good use of the context information existing in the same label to correct some false-classification so that the recognition can be more robust. In this contest, our method can achieve very promising results.
Maximum Joint Probability With Multiple Representations for Clustering

Classical generative models in unsupervised learning intend to maximize p(X) . In practice, samples may have multiple representations caused by various transformations, measurements, and so on. Therefore, it is crucial to integrate information from different representations, and lots of models have been developed. However, most of them fail to incorporate the prior information about data distribution p(X) to distinguish representations. In this article, we propose a novel clustering framework that attempts to maximize the joint probability of data and parameters. Under this framework, the prior distribution can be employed to measure the rationality of diverse representations. K -means is a special case of the proposed framework. Meanwhile, a specific clustering model considering both multiple kernels and multiple views is derived to verify the validity of the designed framework and model.
From Culture to Clothing: Discovering the World Events Behind A Century of Fashion Images

Fashion is intertwined with external cultural factors, but identifying these links remains a manual process limited to only the most salient phenomena. We propose a data-driven approach to identify specific cultural factors affecting the clothes people wear. Using large-scale datasets of news articles and vintage photos spanning a century, we present a multi-modal statistical model to detect influence relationships between happenings in the world and people’s choice of clothing. Furthermore, on two image datasets we apply our model to improve the concrete vision tasks of visual style forecasting and photo timestamping. Our work is a first step towards a computational, scalable, and easily refreshable approach to link culture to clothing.
Overview of SQL Injection Defense Mechanisms

Today, in the era of rapidly changing and evolving IT Technology, the Internet Network has become one of the main daily needs of mankind. However, web applications such as online banking, web-based emails, social networking that are instant means of communication, are targeted by attackers where their main goal is to get important information about users and use them for their own purpose. One of the methods of targeting web applications and web-based information systems is using the SQL injection attacks. In this work main subject of the research is analysis of the methods of SQL injection attacks and creating an overview of the best defense mechanisms for detection and prevention of SQL injection attacks. Furthermore, practical simulation of SQL injection attack is done using Kali- inux platform for sensing the real threat from this kind of cyber-attack. Analysis of the best practices for defense from this kind of attacks is presented after the practical simulation. Aim of this is users to be more aware against this kind of cyber security attacks.
Subgraph Matching on Multiplex Networks

An active area of research in computational science is the design of algorithms for solving the subgraph matching problem to find copies of a given template graph in a larger world graph. Prior works have largely addressed single-channel networks using a variety of approaches. We present a suite of filtering methods for subgraph isomorphisms for multiplex networks (with different types of edges between nodes and more than one edge within each channel type). We aim to understand the entire solution space rather than focusing on finding one isomorphism. Results are shown on several classes of datasets: (a) Sudoku puzzles mapped to the subgraph isomorphism problem, (b) Erdös-Rényi multigraphs, (c) real-world datasets from Twitter and transportation networks, (d) synthetic data created for the DARPA MAA program.
When Smart Systems Fail: The Ethics of Cyber–Physical Critical Infrastructure Risk

Critical infrastructure (CI) remains an important part of daily life in support of providing basic goods and services. Cyber-physical connections between CI systems and sectors continue to increase with the development and integration of new technology. We study the emerging threat landscape and ethical implications of cyber-physical connections within CI. Using both a comprehensive literature review and a coupled epistemic-ethical analysis, we provide evidence that new approaches in science, technology, and policy are necessary. We propose future research directions and emphasize the need for value transparency throughout the science policy landscape.
Event-driven programming-based path planning and navigation of UAVs around a complex urban environment

This chapter focuses on path planning techniques for autonomous navigation of small unmanned aerial vehicles (UAVs) in complex urban environments. It is difficult for UAVs to navigate at low altitudes in dynamic or complex urban environments because they encounter large structures, unpredictable or unrecognized environments, or other UAVs or obstacles flying in their path. Path planning is the core capability of an autonomous UAV to adapt dynamically in changing environments. This chapter will study and implement different path planning algorithms, slowly increasing complexity from optimal A⁎ search algorithms to voxmap, probabilistic roadmap, and Rapidly Exploring Random Tree 2D grids to 3D motion planning using random sampling, heuristic, collinear, and path pruning methods. Differences in performance characteristics such as time of execution, cost, and waypoint count of different path planning algorithms are also discussed. Udacity Flying Car Nanodegree is used to design, build, and check motion planning methods for UAVs in a complex and dynamic environment. All of the path planning algorithms used in this chapter are written in a different way of logic based on the events taking place during the UAV flight, which is called event-driven programming. This chapter provides a number of effective route planning methodologies that ensure secure paths for UAVs by considering potential contingencies and taking into account various uncertainties. The simulation platform and software are open-sourced for community use.
Speech Age-Gender Classification Using Long Short-Term Memory

Recognizing the age and gender of a person with certainty from a media has a significant advantage. For example, the perpetrators recorded on a CCTV camera can be easily recognized, or someone used to lie about age on social media or job application applications can be easily detected. However, detecting the exact age of a person is still a tricky thing because of the quality media and the characteristics of the person who seems deceptive. In machine learning, the neural-based methods are commonly used for classification and recognition. However, age and gender classifications still produce unsatisfactory results, even age and gender classifications by speech are rarely discussed. Hence, the right approach is needed to create a good age and gender classification model. One of the solutions is using Recurrent Neural Network (RNN), which is made for sequential data like speech. In this paper, a speech age-gender classification model is developed using one of the popular RNN models called Long Short-Term Memory (LSTM). The experimental results show that the proposed model is trapped on the overfitting problem so that the accuracy of the testing set is lower than the training set. Regularization can reduce the difference between the accuracies of both training and testing sets but it cannot increase them. The data augmentation is able to slightly solve the overfitting problem.
The quantum marginal problem for symmetric states: applications to variational optimization, nonlocality and self-testing

Abstract In this paper, we present a method to solve the quantum marginal problem for symmetric d -level systems. The method is built upon an efficient semi-definite program that uses the compatibility conditions of an m -body reduced density with a global n -body density matrix supported on the symmetric space. We illustrate the applicability of the method in central quantum information problems with several exemplary case studies. Namely, (i) a fast variational ansatz to optimize local Hamiltonians over symmetric states, (ii) a method to optimize symmetric, few-body Bell operators over symmetric states and (iii) a set of sufficient conditions to determine which symmetric states cannot be self-tested from few-body observables. As a by-product of our findings, we also provide a generic, analytical correspondence between arbitrary superpositions of n -qubit Dicke states and translationally-invariant diagonal matrix product states of bond dimension n .
Player Behavior Modeling for Enhancing Role-Playing Game Engagement

Role-playing games (RPGs) are one of the most exciting and most rapidly expanding genres of online games. Virtual characters that are not controlled by players, have become an integral part, which helps to advance narratives of RPGs. Believable characters can enhance game engagement and further improve player retention. However, game players easily find that most characters' behaviors are limited and improbable, resulting in a less meaningful game experience. In this work, we propose a framework to model game behaviors to learn behavior patterns of human players. Based on the learned behavior patterns, it generates human-like action sequences that can be used for the design of believable virtual characters in RPGs, so as to enhance game engagement. Specifically, considering the influence of game context in behavior patterns, we integrate game context (players' levels and game classes) with actions together to model behaviors. We propose a long-term memory cell on actions and game context to learn the hidden representations. We also introduce an attention mechanism to measure the contribution of the actions previously performed to the next action. Given only one action, our model can generate action sequences by predicting the succeeding action based on the previously generated actions. The model was evaluated on a real-world data set of over 22 000 players and more than 51 million action logs of an RPG game in 21 days. The results demonstrate the state-of-the-art performance.
What does making produce? Posthuman insights into documenting relationalities in maker education for teachers

(2021). What does making produce? Posthuman insights into documenting relationalities in maker education for teachers. Professional Development in Education: Vol. 47, Special Issue: Non-linear perspectives on teacher development: complexity in professional learning and practice, pp. 493-509.
Multiple Faces Tracking Using Feature Fusion and Neural Network in Video

Face tracking is one of the most challenging research topics in computer vision. This paper proposes a framework to track multiple faces in video sequences automatically and presents an improved method based on feature fusion and neural network for multiple faces tracking in a video. The proposed method mainly includes three steps. At first, it is face detection, where an existing method is used to detect the faces in the first frame. Second, faces tracking with feature fusion. Given a video that has multiple faces, at first, all faces in the first frame are detected correctly by using an existing method. Then the wavelet packet transform coefficients and color features from the detected faces are extracted. Furthermore, we design a backpropagation (BP) neural network for tracking the occasional faces. At last, a particle filter is used to track the faces. The main contributions are. Firstly, to improve face tracking accuracy, the Wavelet Packet Transform coefficients combined with traditional color features are utilized in the proposed method. It efficiently describes faces due to their discrimination and simplicity. Secondly, to solve the problem in occasional face tracking, and improved tracking method for robust occlusion tracking based on the BP neural network (PFT_WPT_BP) is proposed. Experimental results have been shown that our PFT_WPT_BP method can handle the occlusion effectively and achieve better performance over other methods.
Logisticschain: A Blockchain-Based Secure Storage Scheme for Logistics Data

With the rapid development of information technology, logistics systems are developing towards intelligence. The Internet of Things (IoT) devices throughout the logistics network could provide strong support for smart logistics. However, due to the limited computing and storage resources of IoT devices, logistics data with user sensitive information are generally stored in a centralized cloud center, which could easily cause privacy leakage. In this paper, we propose Logisticschain, a blockchain-based secure storage scheme for logistics data. In this scheme, the sensing data from IoT devices should be encrypted for fine-grained access control, and a customized blockchain structure is proposed to improve the storage efficiency of systems. Also, an efficient consensus mechanism is introduced to improve the efficiency of the consensus process in the blockchain. Specific to the logistics process, the sensing data generated from IoT devices will be encrypted and aggregated into the blockchain to ensure data security. Moreover, the stored logistics records can be securely audited by leveraging the blockchain network; both IoT data and logistics demands cannot be deleted or tampered to avoid disputes. Finally, we analyze the security and privacy properties of our Logisticschain and evaluate its performance in terms of computational costs by developing an experimental platform.
Coral reefs optimization algorithms for agent-based model calibration

Calibrating agent-based models involves estimating multiple parameter values. This can be performed automatically using automatic calibration but its success depends on the optimization method’s ability for exploring the parameter search space. This paper proposes to carry out this process using coral reefs optimization algorithms, a new branch of competitive bio-inspired metaheuristics that, beyond its novel metaphor, has shown its good behavior in other optimization problems. The performance of these metaheuristics for model calibration is evaluated by conducting an exhaustive experimentation against well-established and recent evolutionary algorithms, including their hybridization with local search procedures. The study analyzes the calibration accuracy of the metaheuristics using an integer coding scheme over a benchmark of 12 problem instances of an agent-based model with an increasing number of decision variables. The outstanding performance of the memetic coral reefs optimization is reported after performing statistical tests to the results.
Paragraph summarization based on word frequency using NLP techniques

Extracting the main points from a long passage is a big problem. We come across hundreds of passages in our professional life daily, if they are small and concise then that's good but if there is a passage of 100 plus lines which could be condensed and delivered or explained it in a few lines it would be more excellent time-saving effortless stress-free work. We all know the essence of any paragraph can be described in just a few necessary sentences whereas the passages need more grammatical content and sentence formation which makes the passages so long. So in this paper, a new method for paragraph summarization is presented. There is continuous research and new methods are being implemented and tested to make the machine to be able to understand the text and take respective actions. In this paper, we used word frequency algorithms and NLTK methods and functions to extract the main points from a paragraph and made it available in desired language audio formats. Several novels/books are being released in audio format for ease of understanding and some websites, as well as YouTube channels, are present over the internet to explain the essence of the novels/books. There are so many passages which have a lot to read and understand but could be made simple.
An efficient computational algorithm for Hausdorff distance based on points-ruling-out and systematic random sampling

This paper proposes a novel algorithm for fast and accurate Hausdorff distance (HD) computation. The Hausdorff distance is used to measure the similarity between two point sets in various applications. However, it is hard to compute the HD algorithm efficiently between very large-scale point sets while ensuring the accuracy of the HD. The directed HD algorithm has two loops (called the outer loop and the inner loop) for calculating MAX-MIN distance, and the state-of-the-art algorithms, such as the Early break method and the Diffusion search method, focused on reducing the iterations of the inner loop. Our algorithm, however, concentrates on reducing the iterations of the outer loop. The proposed method simultaneously computes the temporary HD and temporary minimum distances of points corresponding to the outer loop using the opposite HD computation with very small systematic samples. Thereafter, a strategy of ruling out is employed to exclude non-contributing points. The new approach reduces the problems of different grid sizes and highly overlapping point sets as well as the very large-scale point sets. 3-D point clouds and real brain tumor segmentation (MRI 3-D volumes) are used for comparing the performance of the proposed algorithm and the state-of-the-art HD algorithms. In experimental results with 3-D point clouds, the proposed method is more than at least 1.5 times as faster as the compared algorithms. And, in experimental results with MRI 3-D volumes, the proposed method achieves a better performance than the compared algorithms over all pairs regardless of the grid size. Thus, as a whole, the proposed algorithm outperforms the compared algorithms.
Intent Classification for a Management Conversational Assistant

Intent classification is an essential step in processing user input to a conversational assistant. This work investigates techniques of intent classification of chat messages used for communication among software development teams with the aim of building an intent classifier for a management conversational assistant integrated into modern communication platforms used by developers. Experiments conducted using rule-based and common ML techniques have shown that careful choice of classification features has a significant impact on performance, and the best performing model was able to obtain a classification accuracy of 72%. A set of techniques for extracting useful features for text classification in the software engineering domain was also implemented and tested.
An IoT based System for Public Transport Surveillance using real-time Data Analysis and Computer Vision

The need for a real-time surveillance system in public transport in Bangladesh along with the Indian subcontinent is skyrocketing. The increasing number of working and school-going citizens of Bangladesh need safety in the way of their destination. The Internet of things (IoT) is widely used in our society to provide safety and security to our homes and organization. Ensure the safety of passengers and monitoring drivers and local buses is getting the primary concern of the bus owner. With the collaboration of IoT, Hardware, computer vision, and AI we build an internet-based system that can provide an end-to-end solution to all problems in public transport. The movement of local transport is also a concerning matter in our country like over speeding the vehicle which causes a massive amount of road accidents. Integrating GPS tracking system with our automation safety system, we can create a geofencing to control and monitor the local buses of our country. This paper will delineate the design and the prototype of our surveillance system using computer vision and real-time data analysis.
Non-frontal facial expression recognition based on salient facial patches

Abstract Methods using salient facial patches (SFPs) play a significant role in research on facial expression recognition. However, most SFP methods use only frontal face images or videos for recognition, and they do not consider head position variations. We contend that SFP can be an effective approach for recognizing facial expressions under different head rotations. Accordingly, we propose an algorithm, called profile salient facial patches (PSFP), to achieve this objective. First, to detect facial landmarks and estimate head poses from profile face images, a tree-structured part model is used for pose-free landmark localization. Second, to obtain the salient facial patches from profile face images, the facial patches are selected using the detected facial landmarks while avoiding their overlap or the transcending of the actual face range. To analyze the PSFP recognition performance, three classical approaches for local feature extraction, specifically the histogram of oriented gradients (HOG), local binary pattern, and Gabor, were applied to extract profile facial expression features. Experimental results on the Radboud Faces Database show that PSFP with HOG features can achieve higher accuracies under most head rotations.