Reviewerly/exHarmony huggingface dataset:
Attribute Centered Multimodal Response Generation in a Dialogue System

Dialogue system has become a prominent platform for human-machine interactions. The ongoing research in vision and language has opened new frontiers for building multimodal dialogue systems that incorporate information from the various complementary sources such as text, images, audios, and videos. For interactive systems, multimodal knowledge in the form of different modalities need to be presented to the user for effective communication. Recent research has mainly focused upon textual generation in a multimodal setting, thereby providing incomplete information to the users. Hence, in our current work, we present an attribute centered image generation framework in a multimodal system that is capable of generating images, conditioned upon the textual features with the help of the taxonomy-attribute combined tree. The visual features interact with multi-head attended textual features through attention based factorized bilinear pooling approach for fine-grained representation. Further, the multimodal representation is extended to generate images using the adversarial network. The loss functions encourage the network to generate images that are very similar to the natural image. We perform our experiments on the Multimodal Dialog Dataset (MMD) to create contextualized images using the fashion attribute features. Empirical studies show that the generated attribute centered images help in making the dialogue more engaging to the users.
Elliptic Curve Algorithms for a New PKI Hierarchy in the Peruvian State

In this paper we determine the elliptic curve algorithms with its respective parameters and technical specifications for a new PKI (Public Key Infrastructure) hierarchy in the Peruvian state considering the following aspects: the provisions of accreditation guide for Certification Entities of the IOFE, cryptographic modules of the hierarchy ECERNEP PERU CA ROOT 4, elliptic curves of the SafeCurves initiatives and elliptic curves utilized in Public Key Infrastructure (PKI) for digital signature and encryption. The results of this paper were validated by RENIEC (Spanish acronym of National Registry of Identification and Civil Status) specialist personnel and were used for the implementation of the new PKI hierarchy for the Peruvian state.
Identification Model of Chinese Students Negative Emotion Based on CNN and BIGRU

Colleges should pay more attention to students difficulties and help them to solve the problems or carry out necessary psychological counseling. It is a good method to monitor the change of students emotion through network comment and identity the negative emotion, but at present, it is mostly realized by artificial means. An identification model of Chinese students negative emotion based on the convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) in the deep learning model is proposed. Comparative experiments have shown that the model has higher accuracy and lower resource consumption. The model has a good application prospect.
Makanuai river flood control study at Jayapura district

Abstract The location of this research was carried out in the Makanuai River, Sentani Timur district, Jayapura Regency. Analysis of the planned flood discharge on the Makanuai river using the Rational method for calculating the planned flood discharge. Based on the results of the analysis of the calculation of flood plans with the method that has been done, it can be seen the flood discharge of the Sungai Makanuai plan, the return period Q 2 years = 123,358 m 3 / sec, Q 5 years = 155,098 m 3 / sec, Q 10 Years = 177,421 m 3 / sec, Q 25 Years = 207,294 m 3 / sec and Q 50 Years = 230,698 m 3 / sec. Based on the analysis of the flow hydraulics on the cross-section of the Makanuai River in the HEC-RAS Program, it was found that the elevation of the floodwater level exceeded the capacity of the river. Therefore, it is necessary to make a Dividing Channel (Corner) accommodate the remaining flow of airflow that cannot be accommodated in the Main Canal so that the capacity of the Makanuai river flow will be reduced when maximum rainfall occurs. The operated Shaving Channel is capable of flowing discharge of 204,259 m 3 / sec for a 50 year return period.
Wavelet-Based Face Inpainting with Channel Relation Attention

As the underlying characteristic distribution differs among different frequencies, existing face inpainting methods on RGB domain can cause some artificial boundaries and blurred details when the masks are large. To reduce this interference, we propose to inpaint face images in wavelet domain with two branches to capture global structure topology and local detailed texture separately. Meanwhile, we adopt the channel relation attention module to learn different weights for different feature channels, with the aim of ensuring the consistency between structure and texture features. In addition, a new wavelet loss is designed to constrain the generated wavelet coefficients closer to the ground truth. Experimental results on CelebA-HQ and Helen datasets demonstrate that our Wavelet Prediction Network outperforms current state-of-the-art face inpainting techniques both qualitatively and quantitatively, especially when handling face images with large masks.
Model Performance Inspection of Deep Neural Networks by Decomposing Bayesian Uncertainty Estimates

Deep neural networks (DNNs) are capable of nonlinear learning and achieve high accuracy in many machine learning tasks. However, their performance could degrade due to noisy data, insufficient data amount, or data distributional shifts. In this work, we propose a novel analysis approach to interpret DNNs performance degradation by employing Bayesian uncertainty estimates. In essence, we derive a new probabilistic modeling method to separate and measure three major sources that cause performance degradation, namely data uncertainty, model uncertainty and distributional uncertainty. By performing sensitivity analysis on the decomposed uncertainty estimates, we can analyze which features influence each type of uncertainty. We validate and demonstrate the usefulness of our analysis on real-world datasets in both classification and regression tasks. Our method improves the interpretability of black-box DNNs.
An Improved Fast Fuzzy Clustering Image Segmentation Algorithm

Fuzzy C-means (FCM) clustering algorithm for image segmentation is simple, intuitive and easy to implement, However, there are some problems such as large amount of computation, slow computation speed and poor anti-noise ability. For this reason, this paper proposes an improved fast FCM algorithm (FFCM), This method firstly integrates spatial information into standard FCM algorithm, The image is mapped from pixel space to its gray histogram feature space, and fast clustering is realized. Then, on the basis of fast clustering, the neighborhood characteristics of pixels are fully utilized, and the classification of image pixels is divided according to the principle of maximum membership degree, and the membership degree function is improved to some extent. The experimental results show that the method can segment images quickly and effectively, and has good anti-noise capability.
A Scoring Model Assisted by Frequency for Multi-Document Summarization

While position information plays a significant role in sentence scoring of single document summarization, the repetition of content among different documents greatly impacts the salience scores of sentences in multi-document summarization. Introducing frequencies information can help identify important sentences which are generally ignored when only considering position information before. Therefore, in this paper, we propose a scoring model, SAFA (Self-Attention with Frequency Graph) which combines position information with frequency to identify the salience of sentences. The SAFA model constructs a frequency graph at the multi-document level based on the repetition of content of sentences, and assigns initial score values to each sentence based on the graph. The model then uses the position-aware gold scores to train a self-attention mechanism, obtaining the sentence significance at its single document level. The score of each sentence is updated by combing position and frequency information together. We train and test the SAFA model on the large-scale multi-document dataset Multi-News. The extensive experimental results show that the model incorporating frequency information in sentence scoring outperforms the other state-of-the-art extractive models.
DSP Fast Processing Technology Based on BP Algorithm

Image recognition has always been an important research topic in the field of pattern recognition. This article uses the printed character recognition of bills as the research background. Through the detailed analysis of the recognition process, some preprocessing algorithms are selected and implemented, including image enhancement, binarization, filtering, character cutting, skew correction, etc. Finally, transplant each algorithm to the simulation platform, mainly for BP algorithm, according to the characteristics of DSP, optimize from two aspects of compilation options and source code to meet the requirements of fast processing. Finally, the requirements of better recognition effect and fast processing speed are realized.
Learning New Word Semantics with Conceptual Text

In this paper, we consider the embedding problem of Chinese new word with respect to its conceptual definition or description, which is especially important for understanding specialty documents. We present a two-stage model to learn the Chinese new word embedding, where the first encodes the information of character components and context, and the second aggregates the semantics of multiple texts. We perform extensive experiments to verify the proposed method and the results outperform the state of art methods on both direct semantics verification and advanced NLP tasks. Comparing with previous methods that require a corpus or an elaborately designed dataset for learning a new word embedding, our method requires only a few pieces of text and supports the evolution of meanings. We also experimentally verify the effects of different parts of model, the number and types of conceptual texts. Finally, we present some biology texts to illustrate whether the specialty semantics are encoded in the word embedding.
Integrated Optimization Method of Hidden Parameters in Incremental Extreme Learning Machine

Incremental Extreme Learning Machine is one of the constructive neural networks and provides a fast architecture building mechanism by adding the hidden layer neurons incrementally. There are two phases in constructing the newly added nodes. One is to assign the weights of the hidden nodes quickly with different methods, and the other is to compute the output weights by the least squares methods after obtaining the parameters in the previous phase. Nevertheless, it has a basic deficiency in the aforementioned construction scheme that there is no guarantee on the simultaneous optimization of the weights in the hidden and output layers, respectively, which may produce a lot of redundant nodes in the final models. In this paper, a new integrated optimization method is proposed to construct the simultaneously optimized weights in the first and second phases, and the corresponding integrated optimization incremental-ELM (termed as IOI-ELM) is established. Furthermore, a novel convergence analysis method is developed to provide an upper constraint bound for the selection of the parameters. The simulation results demonstrate and verify that the performance of our approach is much better than other constructive learner models.
Mining Frequent and Rare Itemsets With Weighted Supports Using Additive Neural Itemset Embedding

Over the past two decades, itemset mining techniques have become an integral part of pattern mining in large databases. We present a novel system for mining frequent and rare itemsets simultaneously with supports weighted by cardinality in transactional datasets. Based on our neural item embedding with additive compositionality, the original mining problems are approximately reduced to polynomial-time convex optimization, namely a series of vector subset selection problems in Euclidean space. The numbers of transactions and items are no longer exponential factors of the time complexity under such reduction, except only the Euclidean space dimension, which can be assigned arbitrarily for a trade-off between mining speed and result quality. The efficacy of our method reveals that additive compositionality can be represented by linear translation in the itemset vector space, which resembles the linguistic regularities in word embedding by similar neural modeling. Experiments show that our learned embedding can bring pattern itemsets with higher accuracy than sampling-based lossy mining techniques in most cases, and the scalability of our mining approach triumphs over several state-of-the-art distributed mining algorithms.
An Empirical Comparison of Quantization, Pruning and Low-rank Neural Network Compression using the LC Toolkit

Compression of machine learning models, and of neural networks in particular, has become an essential problem among practitioners. Many different approaches including quantization, pruning, low-rank and tensor decompositions have been proposed in the literature to solve the problem. Despite this, an important unanswered question remains: what is the best compression scheme for a model? As a step towards answering this question objectively and fairly, we empirically compare quantization, pruning, and low-rank compressions in the algorithmic footing of the Learning-Compression (LC) framework. This allows us to explore the compression schemes systematically and perform an apples-to-apples comparison along the entire error-compression tradeoff curves. We describe our methodology, the framework, experimental setup, and present our comparisons. Based on our experiments, we conclude that the choice of compression is strongly model-dependent: for example, VGG16 is better compressed with pruning, while quantization is more suitable for the ResNets. This, once again, underlines the need for a common benchmark of compression schemes with fair and objective comparisons of the models of interest.
Local Tangent Generative Adversarial Network for Imbalanced Data Classification

Learning and classification of imbalanced data is a quite common challenge in machine learning. In order to generate more high-quality minority samples in imbalanced data and help solve the classification process, this paper proposes a novel local tangent generative adversarial network (LT-GAN). In LT-GAN, a local tangent based generator is designed to generate realistic and diverse minority class samples by learning the local tangent space of original minority samples. Meanwhile, a two-function discriminator is explored to judge the authenticity of samples and distinguish the majority samples from the minority samples. With the synthesis of minority class samples, the generator and discriminator are trained together by using adversarial learning. Experiments and comparisons show that our proposed LT-GAN outperforms other techniques and significantly improves the classification performance of imbalanced data.
Bilateral-brain-like Semantic and Syntactic Cognitive Network for Aspect-level Sentiment Analysis

Aspect-level sentiment analysis (ALSA) is a fine-grained task for classifying the sentiment polarity of a specific aspect in a sentence. In spite of the progress in deep-learning algorithms within natural language processing domain, the methods in line with human cognition are absent. Inspired by the processing principle of our brain, we propose a Bilateral-brain-like Semantic and Syntactic Cognitive Network (BSSCN) for aspect-level sentiment analysis. There are four major modules established in BSSCN, which are left hemisphere semantic activation (LH-SA), semantic selection (SS) module, semantic integration (SI) module and right hemisphere semantic activation (RH-SA). Experimental results on a variety of datasets show that our model stably outperforms the widely-used methods, which establishes a strong evidence of the effectiveness in ALSA tasks.
An Offline-Online Strategy for Goal-Oriented Coverage Path Planning using A Priori Information

Recent times are witnessing the emergence of indoor sites with extenuating circumstances that place a strict time constraint on mobile robots to reach a target while covering a given area. This has created a global demand to equip mobile robots with the ability to autonomously plan a coverage path to reach the static target effectively and efficiently. The current approaches to achieve such tasks, however, are either time-consuming or human-operator dependent. To this end, an offline-online strategy is proposed to meet the speeding-up challenge by efficiently modelling the environment using a priori information. In the ‘offline’ stage of the strategy, the layout of the environment is segmented into a set of regions. The corners and dead-ends are identified based on the spatial mobility of the regions. The global path is then computed by deriving a graph-structured, road map using the segmented regions. In the ‘online’ stage, the global path is traversed by selecting frontiers which concurrently minimizes the covered area and time. In case the path is obstructed, a re-planning strategy is deployed. The proposed strategy is evaluated by various experiments against two baseline search approaches in three simulated environments. The results manifest a significant reduction in time to reach the goal and coverage area which caters to the strict time constraint for mobile robots.
GMM Performance Evaluation through Centroid Initialization of k-Means in Text-Independent Speaker Identification

One of the most common methods used in the process of identifying speakers is the Gaussian Mixture Model (GMM) method. The quality of GMM depends on the method selected to train the Gaussian. One method that the researcher has chosen is to use k-Means. In this study, an evaluation process was performed on the k-Means GMM using three centroid initialization methods: randomization, seeding and density analysis. The application of seeding uses the k-Means method, whereas the application of density analysis uses the histogram method. We applied two evaluation criteria, namely the complexity of the training process and the accuracy of the speaker identification process. Experiments were conducted over three types of voice test duration: 2, 4 and 6 seconds. We also used nine types of Gaussian components, ranging from 4 to 20 components, with an increasing scale of 2+n. Our proposed method using density analysis has a clustering process time of 33.7% lower, but with the highest accuracy of 95.5%.
Security Analysis of Even-Mansour Structure Hash Functions

In this paper, we mainly focus on the security of Even-Mansour structure hash functions, including preimage attack resistance and multi-block collision attack resistance.Firstly, we focus on the Even-Mansour structure hash function with two iterations. Basing on the permutation used in the Even-Mansour structure hash function we construct two new functions \(f_{1}\) and \(f_{2}\), and find the partial invariables of input-output in one function \(f_{1}\). Then using the partial invariables of input-output and the meet-in-the-middle techniques, we present a preimage attack on the Even-Mansour structure hash function with two iterations, with the time complexity of \({2}^{{a(2^{a} - 1)}} { + 2}^{a} + {2}^{n - 2a}\) functional operations of \(f_{1}\) or \(f_{2}\) and the memory is \({2}^{a}\) a-bit values, where \(a{2}^{a} \le n\) and n is the size of hash value.Secondly, we extend the Even-Mansour structure hash function to the one with arbitrary iterations. Utilizing the property that the beginning and the ending of every iteration in the Even-Mansour structure both need XOR the message or the transform result of the message, we construct many chaining values with relations in each iteration, which makes that the number of the final chaining values is equal to the product of the number of output chaining values in each iteration, and thereby propose our multi-block collision attack on the Even-Mansour structure hash functions with the time complexity of \(t2^{\frac{s}{2t}}\) queries of F permutation and memory complexity of \(O(2^{s/2} )\), where t is the block number of collision message and s is the size of truncated hash value.
Project 412Connect: Bridging Students and Communities

In this work, we describe some of the challenges Black-owned businesses face in the United States and specifically in the city of Pittsburgh. Taking into account local dynamics and the communicated desires of Black-owned businesses in the Pittsburgh region, we determine that university students represent an under-utilized market for these businesses. We investigate the root causes for this inefficiency and design and implement a platform, 412Connect (https://www.412connect.org), to increase online support for Pittsburgh Black-owned businesses from students in the Pittsburgh university community. The site operates by coordinating interactions between student users and participating businesses via targeted recommendations. We describe the project from its conception, paying special attention to our motivation and design choices. These choices are aided by two simple models for badge design and recommendation systems that may be of theoretical interest. Along the way, we highlight challenges and lessons from coordinating a grassroots volunteer project working in conjunction with community partners and the opportunities and pitfalls of engaged scholarship.
DRENet: Giving Full Scope to Detection and Regression-Based Estimation for Video Crowd Counting

Currently existing deep learning-based video crowd counting methods mainly involve leveraging the temporal correlation to improve the model. Despite their comparable results, most of these counting methods disregard the fact that crowd density varies enormously in the spatial and temporal domains of videos. This thus hinders the improvement in performance of video crowd counting. To overcome that issue, a new detection and regression estimation network, named DRENet, is proposed, which starts with estimating the crowd density by generating a video object detection-, and a mixed 3D-2D convolution-based (regression-based) density maps separately, in which the detection- and regression-based methods function well in sparse and congested scenes, respectively. Moreover, a multi-column attention-based fusion block is proposed to perceive the crowd density in a frame, and to adaptively allocate the relative weights for the video detection- and regression-based estimations. Furthermore, the optimal crowd counts are obtained with guidance from the attention block. The experimental results demonstrate that our method achieves state-of-the-art performance on three public video crowd counting datasets.
Designing Convolutional Neural Networks Using Neuroevolution for Traffic Sign Datasets

Image recognition systems are critical components in numerous applications, often requiring real-time implementations that are both fast and accurate. Convolutional Neural Networks (CNNs) are an emerging tool used to meet these conditions. However, in image recognition, CNNs are often designed to fit general image datasets leading to implementations that may have more layers and nodes than are warranted in particular applications. In this paper, a neuroevolution algorithm is developed to reduce a CNN architecture’s complexity by determining the minimal CNN structure and hyperparameters needed to fit a traffic sign dataset. A neuroevolution algorithm is employed to tune the CNN’s parameters and topology to enable a more efficient parameter space search. Results show that despite reducing complexity, the system still maintains high recognition accuracy compared to popular CNNs, such as AlexNet, VGGNet 16, VGGNet 19, GoogleNet, ResNet 50, and ResNet 101.