Reviewerly/exHarmony huggingface dataset:
PipeBSW: A Two-Stage Pipeline Structure for Banded Smith-Waterman Algorithm on FPGA

With the development of bioinformatics, there is unprecedented progress and wide applications of DNA sequencing technology, especially in helping biological and medical researchers obtain and analyze complete DNA sequences. Smith-Waterman (S-W) algorithm is one of the critical algorithms used in this technology. However, the tremendous amount of DNA sequence data makes it very inefficient to implement the algorithm only with software, so many recent works have tried to accelerate it on hardware. Unfortunately, many of them implemented on the CPU+FPGA heterogeneous platform are likely to require high memory bandwidth and large hardware resource. To mitigate these drawbacks, in this paper we propose PipeBSW, an hardware acceleration implementation on FPGA based on the concept of banded S-W. Using the lookahead calculation technique in cells and a two-stage pipeline structure for processing elements and the backtracking module, the experimental results show that the proposed system can achieve a 720.9Mbps throughput with a 58.4% LUT consumption reduction, compared with the prior work without the pipeline structure.
Open-Set Source Attribution for Panchromatic Satellite Imagery

In the last few years, several companies started offering the possibility of buying different kinds of overhead images acquired by satellites orbiting around the planet. This market is interesting for several customers, from those who simply fancy a shot of their house from space, to those aiming to acquire strategic information on portions of land. Due to the sensitive nature of this data, which can be maliciously altered by anyone, the forensic community has started investigating methodologies to verify overhead imagery authenticity and integrity. Within this context, in this paper we investigate the possibility of using Convolutional Neural Networks (CNNs) to attribute a panchromatic satellite image to the satellite used to acquire it. In our investigation we tackle both closed-set and, adapting Deep Ensemble (DE) and Monte Carlo Dropout (MCD) techniques, open-set image attribution problems.
Detecting Emotions Through Machine Learning for Automatic UX Evaluation

Although User eXperience (UX) is widely acknowledged as an important aspect of software products, its evaluation is often neglected during the development of most software products, primarily because developers think that it is resource-demanding and complain about the fact that is scarcely automated. Various attempts have been made to develop tools that support and automate the execution of tests with users. This paper is about an ongoing research work that exploits Machine Learning (ML) for automatic UX evaluation, specifically for understanding users’ emotions by analyzing the log data of the users’ interactions with websites. The approach described aims at overcoming some limitations of existing proposals based on ML.
Finger Air Writing - Movement Reconstruction with Low-cost IMU Sensor

In this paper, we present and evaluate a method for trajectory reconstruction from IMU signals generated when a person ”air writes” text with a finger worn IMU to make the resulting text as human-readable as possible. The vision is to provide a virtual ”sticky note” allowing people to digitally attach simple texts to locations. Thus, for example, we envision a person walking by someone’s locked office door and simply air writing, ”let me know when you are back”. The other person would then have, for example, their phone vibrate when they come into the office and would see the message on their screen. The problem that we address is how to extract from such ”air writing”, performed without visual feedback or a real surface to write, de-noised 2D trajectories that can be later displayed on a screen in a way that is well readable to humans. We describe the sensor and its signals, the trajectory extraction algorithm, and a user study that shows that we can achieve a high degree of readability.
Application of Artificial Intelligence Technology in the Teaching of Mechanical Education Courses in Universities

Abstract With the continuous innovation of artificial intelligence technology, the teaching level of mechanical education courses in colleges and universities is also constantly improving. In the context of the era of big data, the teaching of mechanical education courses in colleges and universities has become more intelligent and informatized. This article mainly introduces BP neural network method and hill climbing algorithm. This paper uses BP neural network method to analyze the application of artificial intelligence technology in the teaching of mechanical education courses in colleges and universities, and establishes a potential mathematical model of BP neural network method. The model is solved by BP neural network method, and the current situation analysis and application status of the teaching mode of mechanical education courses in colleges and universities are analyzed, and the model is revised using historical data to improve the application research of artificial intelligence technology in the teaching of mechanical education courses in colleges and universities Accuracy. The experimental results of this paper show that the BP neural network method has increased the effect of artificial intelligence technology in the teaching of mechanical education courses in colleges and universities by 33%. Finally, by comparing the value analysis of the application of artificial intelligence technology in the teaching of mechanical education courses in colleges and universities, and the data analysis of artificial intelligence technology in the teaching of mechanical education courses in colleges and universities, the system shows that artificial intelligence technology is used in mechanical education courses in universities. Application in teaching.
Robust recognition of logo under makeover in the context of content piracy

Consumption of digital content through medium like mobile phones, television set and laptop has seen dramatic increase over recent years. Possible increase behind this rise of consumption is the availability of internet across different regions and drastic rise in availability of internet bandwidth at a nominal cost. Eventually preparation of digital content to cater various user entertainment taste profile has increased. Since there is an increase in consumption of the digital content, it opens new challenges on securing this content. Visual analytics of the digital content on periodic basis is in rise to keep a check on the security of the content. Broadcast logo availability as part of digital content is unique and tracking this logo is one of the methods to distinguish between original content and pirated content. In recent years there's significant increase in research work carried out on the problems associated with object detection and classification. This implies there is a significant increase in terms of recognition performance of objects of interest. In this paper, the discussion is on different TV broadcast channel logo dataset creation, enhancing this dataset using different data augmentation techniques to extend the logo corpus. Proposed system includes TV broadcast/broadband content logo detection and classification pipeline that demonstrate the application of state-of-the-art object detection algorithm Single shot detector (SSD) for logo detection and classification on TV broadcast channel logo dataset which has undergone significant makeover for representing different logo conditions. Experimental result shows the pipeline potential to robustly recognize the logos under makeover in the context of content piracy
An Efficient Hardware Architecture for Sparse Convolution using Linear Feedback Shift Registers

Deep convolutional neural networks (CNNs) have shown remarkable success in many computer vision tasks. However, their intensive storage, bandwidth and computational requirements limit their deployment to embedded platforms. Although several research efforts have shown that pruning redundant weights could significantly reduce storage and computations, working with sparse weights remains challenging. The irregular computation of sparse weights and the overhead of managing their representation limit the efficiency of the underlaying hardware. To address these issues, we propose a hardware-friendly pruning algorithm that generates structured sparse weights. In this algorithm, locations of non-zero weights are derived on-chip in real-time using Linear Feedback Shift Registers (LFSRs) to eliminate the overhead of managing sparse weight representations. In this paper, we also propose a hardware inference engine for sparse convolution on FPGAs. It uses LFSRs to localize non-zero weights within weights tensors and avoids copying sparse weights indices by generating them on-chip. Experimental results show that the proposed pruning method can reduce the size of VGG16, ResNet50, and InceptionV3 models by 80%, 76% and 65% with less than 2% accuracy loss. Experiments also demonstrate that our accelerator can achieve 456-534 effective GOP/s for the modern CNNs on Xilinx ZCU102, which provides a 1.2-2.7× speedup over previous sparse CNN accelerators on FPGAs.
Case Relation Transformer: A Crossmodal Language Generation Model for Fetching Instructions

There have been many studies in robotics to improve the communication skills of domestic service robots. Most studies, however, have not fully benefited from recent advances in deep neural networks because the training datasets are not large enough. In this letter, our aim is crossmodal language generation. We propose the Case Relation Transformer (CRT), which generates a fetching instruction sentence from an image, such as “Move the blue flip-flop to the lower left box.” Unlike existing methods, the CRT uses the Transformer to integrate the visual features and geometry features of objects in the image. The CRT can handle the objects because of the Case Relation Block. We conducted comparison experiments and a human evaluation. The experimental results show the CRT outperforms baseline methods.
On the Selection the Rule Membership Functions and Fuzzy Rule Interpolation

In many real physical systems based fuzzy inference systems the rulebase is sparse thus interpolation or the change of the shape of the rules become necessary if the rulebase parameters are selected according to physical parameters of the systems. Often measurements contain noise and outlines which can draw the statistics of the measured data. In the present article based on two independent examples, namely telecommunication line evaluation and colonoscopy image processing, we study the effect of the selection of the rulebase parameters on the effectiveness of stabilized fuzzy KH interpolation.
A second-order nonlocal regularized variational model for multiframe image super-resolution

Abstract In this paper, we propose a new nonlocal super-resolution (SR) model which is a combination of the nonlocal total variation (TV) regularization and the nonlocal p -Laplacian term (with p = 2). This choice is motivated by the success of the nonlocal TV term in preserving image edges and the efficiency of the nonlocal p -Laplacian term in preserving the image texture. To ensure the convergence of the proposed optimization SR problem, we prove the existence and uniqueness of a solution in a well-posed framework. In addition, to resolve the encountered minimization problem, we proposed a modified primal-dual algorithm and numerical results are also given to show the performance of the proposed approach.
Exploration of Effective Attention Strategies for Neural Automatic Post-editing with Transformer

Automatic post-editing (APE) is the study of correcting translation errors in the output of an unknown machine translation (MT) system and has been considered as a method of improving translation quality without any modification to conventional MT systems. Recently, several variants of Transformer that take both the MT output and its corresponding source sentence as inputs have been proposed for APE; and models introducing an additional attention layer into the encoder to jointly encode the MT output with its source sentence recorded a high-rank in the WMT19 APE shared task. We examine the effectiveness of such joint-encoding strategy in a controlled environment and compare four types of decoder multi-source attention strategies that have been introduced into previous APE models. The experimental results indicate that the joint-encoding strategy is effective and that taking the final encoded representation of the source sentence is the more proper strategy than taking such representation within the same encoder stack. Furthermore, among the multi-source attention strategies combined with the joint-encoding, the strategy that applies attention to the concatenated input representation and the strategy that adds up the individual attention to each input improve the quality of APE results over the strategy using the joint-encoding only.
Compact And Adaptive Multiplane Images For View Synthesis

Recently, learning methods have been designed to create Multiplane Images (MPIs) for view synthesis. While MPIs are extremely powerful and facilitate high quality renderings, a great amount of memory is required, making them impractical for many applications. In this paper, we propose a learning method that optimizes the available memory to render compact and adaptive MPIs. Our MPIs avoid redundant information and take into account the scene geometry to determine the depth sampling.
An Adaptive Mobile Robot Control for Autonomous Following in Front of a Person

This paper addresses the control of an autonomous mobile robot in a specific case, when the robot is following a person in front of him. Using a leader-follower formation approach combined with a look-ahead concept, a human-robot kinematic model in error coordinates with respect to a local Cartesian coordinate system is developed. A nonlinear feedback control is designed using local information from onboard sensor for the relative human-robot position and orientation. An adaptive control is proposed to estimate the unknown human linear and angular velocities. The effectiveness of the proposed robot control is validated via simulation tests and experiments.
Automatic topic labeling using graph-based pre-trained neural embedding

It is necessary to reduce the cognitive overhead of interpreting the native topic term list of the Latent Dirichlet Allocation (LDA) style topic model. In this regard, automatic topic labeling has become an effective approach to generate meaningful alternative representations of topics discovered for end-users. In this study, we introduced a novel two-phase neural embedding framework with the redundancy-aware graph-based ranking process. It demonstrated how pre-trained neural embedding could be usefully applied in topic terms, sentence presentations, and automatic topic labeling tasks. Moreover, reranking the topic terms optimized the discovered topics with fewer yet more representative terms while retaining the topic information integrality and fidelity. It further decreased the burden of computation caused by neural embedding and improved the overall effectiveness of the labeling system. Compared with the prevailing state-of-the-art and classical labeling systems, our efficient model boosted the quality of the topic labels generated and discovered more meaningful topic labels.
Learning Non-Linear Disentangled Editing For Stylegan

Recent work has demonstrated the great potential of image editing in the latent space of powerful deep generative models such as StyleGAN. However, the success of such methods relies on the assumption that a linear hyperplane may separate the latent space into two subspaces for a binary attribute. In this work, we show that this hypothesis is a significant limitation and propose to learn a non-linear, regularized and identity-preserving latent space transformation that leads to more accurate and disentangled manipulations of facial attributes.
Smoother Entropy for Active State Trajectory Estimation and Obfuscation in POMDPs

We study the problem of controlling a partially observed Markov decision process (POMDP) to either aid or hinder the estimation of its state trajectory. We encode the estimation objectives via the smoother entropy, which is the conditional entropy of the state trajectory given measurements and controls. Consideration of the smoother entropy contrasts with previous approaches that instead resort to marginal (or instantaneous) state entropies due to tractability concerns. By establishing novel expressions for the smoother entropy in terms of the POMDP belief state, we show that both the problems of minimising and maximising the smoother entropy in POMDPs can surprisingly be reformulated as belief-state Markov decision processes with concave cost and value functions. The significance of these reformulations is that they render the smoother entropy a tractable optimisation objective, with structural properties amenable to the use of standard POMDP solution techniques for both active estimation and obfuscation. Simulations illustrate that optimisation of the smoother entropy leads to superior trajectory estimation and obfuscation compared to alternative approaches.
Spectral properties of symmetric quantum states and symmetric entanglement witnesses

We introduce and explore two questions concerning spectra of operators that are of interest in the theory of entanglement in symmetric (i.e., bosonic) quantum systems. First, we investigate the inverse eigenvalue problem for symmetric entanglement witnesses -- that is, we investigate what their possible spectra are. Second, we investigate the problem of characterizing which separable symmetric quantum states remain separable after conjugation by an arbitrary unitary acting on symmetric space -- that is, which states are separable in every orthonormal symmetric basis. Both of these questions have been investigated thoroughly in the non-symmetric setting, and we contrast the answers that we find with their non-symmetric counterparts.
A Deep Learning Approach for Channel Estimation in 5G Wireless Communications

This paper studies the applications of Deep Learning (branch of Machine Learning) in 5G wireless communication systems. Deep Learning requires having large sets of data for training and testing purposes. Obtaining such large datasets through measurement campaigns is a challenging and costly task. Therefore, it is a common practice to use synthesized data. The aim of the current paper is to survey different applications of deep learning in 5G systems, and more specifically, that implementing the massive multiple input multiple output (mMIMO) using deep learning, for estimating the wideband mMIMO channel, and for beamforming co-operative transmission. Then, methods for dataset generation for such systems are covered and reviewed. The required datasets for deep-learning may be constructed via ray-tracing simulators. Illustration of the methodology with some deployment scenarios relying on Remcom wireless InSite® ray-tracing simulator are introduced. These scenarios are used to provide the required datasets to be used for deep learning. Then, several directions are proposed for future work that are based on sensing the environment type, (Indoor/Outdoor), and jointly estimating the channel vector, Direction-of-Arrival (DoA), Direction of Departure (DoD), and Rx point power allocation. In a manner similar to adaptive beamforming, the RF radiation pattern can be adapted according to the time-varying DoA and DoD, and estimated channel.
Reform and Innovation of Reading Course for English Majors in 5G Era

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
Deep Blind Un-Supervised Learning Network for Single Image Super Resolution

Deep learning based methods have recently made significant progress in image super resolution (SR) field and lead to great performance gain in terms of both effectiveness and efficiency. Most of the current methods have struggled to design more complicated and deeper network architectures and aimed to learn a good LR-to-HR mapping with the previously prepared training sample pairs under a fixed degradation model (Blurring and down-sampling operations) such as bicubic dawn-sampling. However, these methods are generally implemented in a fully-supervised way with largescale training dataset, and are hardly generalized to most real scenarios with unknown and complicated degradation model. This study proposes a blind un-supervised learning network for automatically estimating the degradation operations in single SR problem, where the blurring kernel (operation) is unknown. Motivated by the considerable possessed image priors in the network architectures, we construct a generative network for simultaneously learning the inherent priors of the latent high resolution (HR) image and the degradation operations with the under-studying low-resolution (LR) observation only. Specifically, we exploit a general depth-wise convolutional layer for both approximating a special degradation and automatically learning any complicated blurring kernel in a general SR framework, and propose an end-to-end HR image learning network from its LR observation. Experimental results on two benchmark datasets validate that our proposed method achieve promising performance under the unknown degradation model.
Naive and Neighbour Approach for Phishing Detection

Phishing is a cybercrime that involves tricking the user into entering sensitive information often by disguising as an authentic website. It has become very critical for us to devise solutions in order to protect user information. Our proposed work uses machine learning models for classification of websites to prevent frequent security attacks. We utilise various non-text based features from authentic sources. The inexistence of text features in our dataset makes the process of detection easier for Domain Registrars and Search Engines. Results indicate that KNN outperforms other classifiers by giving an accuracy of 95% in detection of phishing websites. However, Naive Bayes does exceptionally well in identifying the authentic websites.
Attend, Correct And Focus: A Bidirectional Correct Attention Network For Image-Text Matching

Image-text matching task aims to learn the fine-grained correspondences between images and sentences. Existing methods use attention mechanism to learn the correspondences by attending to all fragments without considering the relationship between fragments and global semantics, which inevitably lead to semantic misalignment among irrelevant fragments. To this end, we propose a Bidirectional Correct Attention Network (BCAN), which leverages global similarities and local similarities to reassign the attention weight, to avoid such semantic misalignment. Specifically, we introduce a global correct unit to correct the attention focused on relevant fragments in irrelevant semantics. A local correct unit is used to correct the attention focused on irrelevant fragments in relevant semantics. Experiments on Flickr30K and MSCOCO datasets verify the effectiveness of our proposed BCAN by outperforming both previous attention-based methods and state-of-the-art methods. Code can be found at: https://github.com/liuyyy111/BCAN.