Reviewerly/exHarmony huggingface dataset:
Zero-Shot Predicate Prediction for Scene Graph Parsing

The scene graph is a structured semantic representation of an image, which represents objects and relationships with vertices and edges, respectively. Since it is impossible to manually label all potential relationships in the real world, some previous methods try to apply the zero-shot method for scene graph generation. However, existing methods take triplet (i.e., hsubject-predicate-objecti) as the basic unit of a relationship. Each element (i.e., subject, predicate, or object) of the unseen relationship is actually seen in the training data. Therefore, they ignore the unseen predicate. To predict the unseen predicate, we introduce a novel task named zero-shot predicate prediction, which is crucial to extending existing scene graph generation methods to recognize more relationship classes. The new task is challenging and cannot be simply resolved through conventional zero-shot learning methods because there is a large intra-class variation of each predicate. Firstly, the large intra-class variation leads to the difficulty of computing the discriminative instancelevel feature of the predicate class. Secondly, the large intraclass variation also brings more difficulties when knowledge is transferred from seen classes to unseen classes. For the first challenge, we propose distilling lexical knowledge of different objects and construct multi-modal representations of pairwise objects to reduce the intra-class variation of the predicate. To respond to the second challenge, we build a compact semantic space where the representations of unseen classes are reconstructed based on the seen classes for zero-shot predicate classification. We evaluate the proposed method on the public dataset Visual Genome. The extensive experiment results under the zeroshot/few-shot/supervised settings demonstrate the effectiveness of the proposed method.
Terminal Embeddings in Sublinear Time

Recently (Elkin, Filtser, Neiman 2017) intro-duced the concept of a terminal embedding from one met-ric space to another with a set of designated terminals. In the case where both metric spaces are Euclidean, recently (Narayanan, Nelson 2019), following work of (Mahabadi, Makarychev, Makarychev, Razenshteyn 2018), constructed a terminal embedding with optimal embedding dimension. This generalizes the Johnson-Lindenstrauss lemma, which only preserves distances within the terminal set and not to the set from the rest of space. The downside is that contructing the embedding for a new point required solving a semidefinite program incurring large runtime. Our main contribution in this work is to give a new data structure for computing terminal embeddings. We show how to pre-process the terminal set to obtain an almost linear-space data structure that supports computing the terminal embedding image of any input point in sublinear time. To accomplish this, we leverage tools developed in the context of approximate nearest neighbor search.
A Reconfigurable Architecture for Improvement and Optimization of Advanced Encryption Standard Hardware

This paper contains an in-depth investigation into a modularized and parameterized AES implementation with options for addition of many different AES optimizations. This implementation takes advantage of the low complexity and high malleability of the AES standard’s hardware implementation and its ability to integrate with different block modes of operation. Through investigation of the various available optimizations and implementation methodologies, a high frequency, low power, and low area AES implementation is presented. Results are delivered in a SoC 90nm technology using a combination of multiple industry standard tools. Results compare the different optimizations and Power, Performance and Area (PPA) compromises available to AES hardware as well as different options for implementation configuration. This work also presents a new design optimization for AES that provides a significant decrease in the critical path of the hardware design of the operation.
Information construction of construction project management based on internet of things blockchain

Abstract Because of the complexity of construction projects and the frequency of project transactions, smart project management has a huge amount of transaction and operation data. Massive transaction and operation data urgently need a safe, efficient and low–cost storage and data analysis to realize fine management of buildings, low–cost transaction and interconnection of project information. Therefore, the construction project management system based on internet of things blockchain has been gradually improved, and more and more researches have been made on it. This article proposes a parallel design scheme. Parallel algorithms disperse the repeated calculation work on each node, reduce the calculation load and calculation time of a single node, effectively improve the efficiency of the algorithm, and enable it to process large–scale data of different sizes in parallel. Improve the algorithm according to the experiment, and analyze the performance of the parallel algorithm according to the experimental results. Experiments show that Apriori algorithm and K ‐means algorithm based on MapReduce are more than 85° optimized than traditional data mining.
Filter-based photonic reservoir computing as a key-enabling platform for all-optical, high-speed processing of time-stretched images and telecomm data

We present an integrated photonic reservoir computing scheme that relies on the use of passive optical filters inside waveguide loops. The filters-in-a-loop nodes aim to provide an alternative all-optical non-linear activation function, circumventing previous power-hungry or bandwidth-limiting approaches. The non-linear transformation of the incoming optical signal relies, firstly on multiple spectral slicing and secondly on the non-linear mapping of phase information to the outputs’ amplitude, through the existence of resonant optical cavities. The efficiency of the approach has been evaluated numerically, through two demanding applications. Transmission impairment amendment of 224 Gbps PAM signals with superior to HD FEC performance for short-reach networks and secondly, MHz rate all-optical classification of time-stretched scans depicting objects with different spatial features.
Evolutionary Teaching-Learning Based Modified Polynomial Classifier

Development of a new and robust classification method is yet a hot area of research. The polynomial neural network(PNN) is one such method. However, its model complexity and high computation time restrict its usage. This paper proposes an evolutionary teaching-learning base modified polynomial classifier(ETL-PC), a modified PNN model which initially starts with a polynomial neural network and then uses an artificial neural network to find the output. The model uses a binary encoded genetic algorithm to select a subset of features and partial descriptions(PDs) relevant for classification and uses teaching-learning based optimization method to determine the appropriate weights. The performance of ETL-PC is compared with the performance of few other classifiers using different metrics and it is observed that ETL-PC performs either better or as other methods in many cases and may be considered as a competitive model.
Topic Oriented Hate Speech Detection

Online social media is a very popular platform nowadays where people can easily make virtual connections with each other and can freely express their opinions and interests on various topics over time. The ability to freely express oneself often results in the spread of hate speech in the virtual world. Thus, it is very important to detect automatically hate speech to reduce its spread on social media. Most of the existing research works in this area paid less attention to the topical hate speech detection. In this paper, we addressed topic-oriented hate speech detection using machine learning classifiers. Experimental results on a real dataset demonstrate the efficacy of the proposed model.
A Survey of Procedural Content Generation of Natural Objects in Games

The natural environment is one of the important research fields in game design and development. A good game environment is a key factor in the process of game development and player experience. Procedural Content Generation (PCG) is currently a wide range of fully automatic game environment generation technology. This paper introduces some algorithms and experimental results of PCG for natural objects (such as vegetation, river, and terrain), with special attention to the applicability and aesthetic visualization. It is found that the appearance of PCG greatly reduces the time needed to design large-scale natural landscape for game levels. Furthermore, PCG is able to adjust the natural landscape in real time to improve the overall game development efficiency.
Pre-trained Transformer-based Classification for Automated Patentability Examination

Patentability examination, which means checking whether claims of a patent application meet the requirements for being patentable, is highly reliant on experts&#x2019; arduous endeavors entailing domain knowledge. Therefore, automated patentability examination would be the immediate priority, though under-appreciated. In this work, being the first to cast deep-learning light on automated patentability examination, we formulate this task as a multi-label text classification problem, which is challenging due to learning cross-sectional characteristics of abstract requirements (labels) from text content replete with inventive terms. To address this problem, we fine-tune downstream multi-label classification models over pre-trained transformer variants (BERT-BaseLarge, RoBERTa-BaseLarge, and XLNet) in light of their state-of-the-art achievements on many tasks. On a large USPTO patent database, we assess the performance of our models and find the model outperforming others based on the metrics, namely micro-precision, micro-recall, and micro-F1.
Characterizing duplicate bugs: Perceptions of practitioners and an empirical analysis

Abstract Bug handling is an essential part of the software development process. Ideally, in a bug‐tracking system, bugs are reported, fixed, verified, and closed. In some cases, bugs have to be reopened mostly due to an incorrect fix. However, instead of reopening the existing bug report, users may submit a new report for a previously reported bug, which results in duplicate bug reports. Users might report duplicate bugs if they miss the previous bug report or if the previous bug is unresolved (i.e., in progress) and therefore cannot be reopened. These duplicate bug reports may cost extra maintenance efforts in triaging and bug fixing. There have been several studies on characterizing reopened bugs and duplicate bug reports; however, to the best of our knowledge, there has been no prior work on understanding the dynamics of their interaction, which is missed reopen bugs. Our study is based on analyzing the difference between duplicate and nonduplicate bugs and further categorizing the duplicate bugs. In this regard, we categorize duplicate bugs according to the original bug's resolution status at the duplicate's creation time as Master‐Unresolved bugs and Master‐Resolved (Missed Reopen) bugs to distinguish their properties. We compare these two types of bugs in terms of their relationship to their master bug, bug surface time, bug fix time, bug's severity, and the number of users involved. We perform case studies using the Eclipse and Mozilla projects' bug repositories that include more than 165,500 and 394,000 bug reports, respectively. Additionally, we investigate the perceived importance, impact, and causes of duplicate bugs, as well as the difference between nonduplicate and duplicate bugs and its categories for practitioners in the software industry by conducting a survey.
Lifelong Age Transformation With a Deep Generative Prior

In this paper, we consider the lifelong age progression and regression task, which requires to synthesize a persons appearance across a wide range of ages. We propose a simple yet effective learning framework to achieve this by exploiting the prior knowledge of faces captured by well-trained generative adversarial networks (GANs). Specifically, we first utilize a pretrained GAN to synthesize face images with different ages, with which we then learn to model the conditional aging process in the GAN latent space. Moreover, we also introduce a cycle consistency loss in the GAN latent space to preserve a persons identity. As a result, our model can reliably predict a person's appearance for different ages by modifying both shape and texture of the head. Both qualitative and quantitative experimental results demonstrate the superiority of our method over concurrent works. Furthermore, we demonstrate that our approach can also achieve high-quality age transformation for painting portraits and cartoon characters without additional age annotations.
FPGA Implementation of Radio Frequency Neural Networks

Recent advances in Neural Network (NN) models for the Radio Frequency (RF) domain have made them a dominant force in realizing robust architectures that generalize well to novel operating conditions. While the performance of NN models when running on a Graphics Processing Unit (GPU) are generally very good, many applications require lower latency and higher throughput to be edge deployable. We have recently developed physics-driven NN models to perform Digital Signal Processing (DSP) functions for a Long Term Evolution (LTE) receiver application, demonstrating equal or better performance than their DSP equivalents. This paper discusses moving some of these NN models to Field Programmable Gate Array (FPGA) to tackle the latency and throughput goals and evaluate the performance at different quantization levels. We compare the FPGA performance results at different quantization levels with their GPU performance counterpart and discuss the path forward towards an RF edge solution.
Teaching Classroom Expectations Through Cooperative Learning Activities

Teachers play a critical role in student learning, however effective teaching and learning cannot take place without established rules, procedures, and routines for the classroom. If your goal as a teacher is to help students understand classroom expectations and how to apply these expectations within different situations then just presenting the rules is not enough. Participating in cooperative learning activities is one way for students to learn to create meaning regarding the expectations and to transfer their meaning into other parts of the classroom environment. Applying cooperative learning activities to learn classroom expectations could enable the classroom to run smoothly, efficiently, and provide a positive classroom environment for all.
Agent's Feedback in Preference Elicitation

A generic decision-making (DM) agent specifies its preferences partially. The studied prescriptive DM theory, called fully probabilistic design (FPD) of decision strategies, has recently addressed this obstacle in a new way. The found preference completion and quantification exploits that: <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\blacktriangleright$</tex> FPD models the closed DM loop and the agent's preferences by joint probability densities (pds); there is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a$</tex> preference-elicitation (PE) principle, which maps the agent's model of the state transitions and its incompletely expressed wishes on an ideal pd quantifying them. The gained algorithmic quantification provides ambitious but potentially reachable DM aims. It suppresses demands on the agent selecting the preference-expressing inputs. The remaining PE options are: <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\blacktriangleright$</tex> a parameter balancing exploration with exploitation; <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\blacktriangleright$</tex> a fine specification of the ideal (desired) sets of states and actions; <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\blacktriangleright$</tex> relative importance of these ideal sets. The current paper makes decisive steps towards a systematic and realistic choice of such inputs by solving a meta-DM task. The algorithmic “meta-agent” observes the user's satisfaction, expressed by school-type marks, and tunes the free PE inputs to improve these marks. The solution requires a suitable formalisation of such a meta-task. This is done here. The proposed way copes with the danger of infinite regress and the dimensionality curse. Non-trivial simulations illustrate the results.
NTRU and LASH for a Quantum Resistant Blockchain

Blockchain technology has permeated society and is being applied to numerous industries. However, the advent of quantum computing poses a threat to current blockchain implementations. Most widely used blockchain implementations depend on algorithms that are known to be vulnerable to quantum computing attacks. This necessitates the exploration of quantum resistant blockchain implementations. The current study provides such a proposal for quantum resistant blockchain.
Real-time Scale and Rotation Invariant Multiple Template Matching

Object detection is an important component in the field of computer vision. Real-time detection of objects of any scale or rotation is a major challenge facing the industry today. In this study, we proposed a real-time size and orientation invariant template matching algorithm and hardware structure. In addition, we proposed image pyramid generation, patch orientation detection, descriptor generation, and descriptor matching methods. First, Scale invariance is achieved by generating a pyramid of nine images from the input image to simultaneously detect objects of different scales. Then, construct a window equal to the size of the template image from the original image to obtain the center point and direction of the window. We achieve rotation invariance by creating and rotating a descriptor based on the orientation of the window. Finally, the object is detected by matching it with the descriptor of the template. The proposed algorithm was implemented in Xilinx Virtex-7 xc7v2000tflg1925-1 FPGA. Throughput was 187 Frames/s regardless of the number of objects.
Iterative Multiple Bounding-Box Refinements for Visual Tracking

Single-object visual tracking aims at locating a target in each video frame by predicting the bounding box of the object. Recent approaches have adopted iterative procedures to gradually refine the bounding box and locate the target in the image. In such approaches, the deep model takes as input the image patch corresponding to the currently estimated target bounding box, and provides as output the probability associated with each of the possible bounding box refinements, generally defined as a discrete set of linear transformations of the bounding box center and size. At each iteration, only one transformation is applied, and supervised training of the model may introduce an inherent ambiguity by giving importance priority to some transformations over the others. This paper proposes a novel formulation of the problem of selecting the bounding box refinement. It introduces the concept of non-conflicting transformations and allows applying multiple refinements to the target bounding box at each iteration without introducing ambiguities during learning of the model parameters. Empirical results demonstrate that the proposed approach improves the iterative single refinement in terms of accuracy and precision of the tracking results.
Smart Helmet with Rear View and Accident Detection System for Increased Safety

This paper presents a smart helmet to show the rider his rear view using a raspberry pi camera. A speed limit indicator will help the rider stay within safe speed limits. Accident detection system is implemented by means of a heart rate sensor. In case of an accident an automated SMS will be sent to the relevant authorities using a GPS and GSM module. The heart rate sensor is also used to detect drowsiness and a vibrator alerts the rider to stay awake. A smart turn indicator warns the vehicles behind motorcycle of the rider's intention to turn. This proposed smart helmet aims to make motorcycle riding safer.
An Accurate and Fast Computational Python Based Module for Linear Regression Analysis in Data Science Applications

Due to the increase in computational power of digital electronics in past decades, Data Science has become an emerging field and attracted much research interest for development in various sectors. The usage of Python programming language is tremendously growing due to the large community and its ease of use and its support with powerful scripting algorithms for many engineering applications. One of the simple and basic technique in Data Science is Linear Regression. Traditional Libraries like ‘scikit-learn’ and ‘SciPy’ are built on top of other libraries like ‘NumPy’ and ‘pandas’. In this study, a python module is created for performing simple linear regression named “LinReg” using only python's built-in functions and data types. This allows faster computation of the regression coefficients and the evaluation metrics like slope, intercept, and mean absolute error. A test is also conducted to compare the computational time of the module with the above mentioned two libraries. However, the analogies are given to compare and pros and cons of this work and its future scope is discussed on such modules for various other algorithms in Data Science.
Does OpenBSD and Firefox’s Security Improve With Time?

Ozment and Schechter (USENIX Security’2006) analyzed the evolution of OpenBSD vulnerabilities over the span of 7 years (1998-2005) and concluded that its security increases with age. In this paper, we extend their study by analyzing the evolution of OpenBSD vulnerabilities over the span of 22 years (1998-2020) and Firefox vulnerabilities over the span of 9 years (2011-2020). Our empirical study leads to a number of insights, including the following: both OpenBSD and Firefox get more secure (i.e., less vulnerable) with time, but today’s developers do not necessarily produce more secure code; OpenBSD and Firefox developers tend to make similar security mistakes, but Firefox vulnerabilities are easier to exploit; finally, Firefox’s vulnerability density is almost one order of magnitude higher than OpenBSD’s, meaning Firefox is more vulnerable.