Reviewerly/exHarmony huggingface dataset:
Non-Autoregressive Transformer Automatic Speech Recognition

Recently very deep transformers have outperformed conventional bi-directional long short-term memory networks by a large margin in speech recognition. However, to put it into production usage, inference computation cost is still a serious concern in real scenarios. In this paper, we study two different non-autoregressive transformer structure for automatic speech recognition (ASR): A-CMLM and A-FMLM. During training, for both frameworks, input tokens fed to the decoder are randomly replaced by special mask tokens. The network is required to predict the tokens corresponding to those mask tokens by taking both unmasked context and input speech into consideration. During inference, we start from all mask tokens and the network iteratively predicts missing tokens based on partial results. We show that this framework can support different decoding strategies, including traditional left-to-right. A new decoding strategy is proposed as an example, which starts from the easiest predictions to the most difficult ones. Results on Mandarin (Aishell) and Japanese (CSJ) ASR benchmarks show the possibility to train such a non-autoregressive network for ASR. Especially in Aishell, the proposed method outperformed the Kaldi ASR system and it matches the performance of the state-of-the-art autoregressive transformer with 7x speedup. Pretrained models and code will be made available after publication.
Convolutional Neural Network and Maxpooling Architecture on Zynq SoC FPGA

In recent year, the advancement of artificial intelligence applications require a dedicated processor with high computational power, architecture flexibility, and small design size, as well as low power. This paper aims to present a configurable convolutional neural network (CNN) and max-pooling processor architecture that is suitable for small size SoC (System On Chip) implementation. The processor is designed as IP core in SoC system. Architecture flexibility is achieved by implementing the system in both hardware and software. Therefore the system is implemented in Zynq 7000 FPGA bases SoC. The system functionality is verified by implementing CNN and Maxpooling algorithm in the proposed FPGA.
Optimization of an H0 photonic crystal nanocavity using machine learning

Using machine learning, we optimized an ultrasmall photonic crystal nanocavity to attain a high <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>Q</mml:mi> </mml:math> . Training data were collected via finite-difference time-domain simulation for models with randomly shifted holes, and a fully connected neural network (NN) was trained, resulting in a coefficient of determination between predicted and calculated values of 0.977. By repeating NN training and optimization of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>Q</mml:mi> </mml:math> value on the trained NN, the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>Q</mml:mi> </mml:math> was roughly improved by a factor of 10–20 for various situations. Assuming a 180-nm-thick semiconductor slab at a wavelength approximately 1550 nm, we obtained <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>Q</mml:mi> <mml:mo>=</mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>1</mml:mn> </mml:mrow> <mml:mo>,</mml:mo> <mml:mspace width="negativethinmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>011</mml:mn> </mml:mrow> <mml:mo>,</mml:mo> <mml:mspace width="negativethinmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>400</mml:mn> </mml:mrow> </mml:math> in air; 283,200 in a solution, which was suitable for biosensing; and 44,600 with a nanoslot for high sensitivity. Important hole positions were also identified using the linear Lasso regression algorithm.
PgRC: pseudogenome-based read compressor

Abstract Motivation The amount of sequencing data from high-throughput sequencing technologies grows at a pace exceeding the one predicted by Moore’s law. One of the basic requirements is to efficiently store and transmit such huge collections of data. Despite significant interest in designing FASTQ compressors, they are still imperfect in terms of compression ratio or decompression resources. Results We present Pseudogenome-based Read Compressor (PgRC), an in-memory algorithm for compressing the DNA stream, based on the idea of building an approximation of the shortest common superstring over high-quality reads. Experiments show that PgRC wins in compression ratio over its main competitors, SPRING and Minicom, by up to 15 and 20% on average, respectively, while being comparably fast in decompression. Availability and implementation PgRC can be downloaded from https://github.com/kowallus/PgRC. Supplementary information Supplementary data are available at Bioinformatics online.
Procedural Text Generation from a Photo Sequence

Multimedia procedural texts, such as instructions and manuals with pictures, support people to share how-to knowledge.In this paper, we propose a method for generating a procedural text given a photo sequence allowing users to obtain a multimedia procedural text.We propose a single embedding space both for image and text enabling to interconnect them and to select appropriate words to describe a photo.We implemented our method and tested it on cooking instructions, i.e., recipes.Various experimental results showed that our method outperforms standard baselines.
A cubic spline method combing improved particle swarm optimization for robot path planning in dynamic uncertain environment

This article considers a robot path planning problem originated from a robot factory inspection scenario. In the problem, the robot is in a dynamic uncertain environment, that is, a moving target object and several static and dynamic obstacles. An inertial positioning strategy is proposed to enable the robot to predict the position of the target in advance. From this predicted position, the robot path is generated by cubic spline interpolation, and then an improved particle swarm optimization algorithm with a random positive feedback factor in velocity updating optimizes the path. The experimental results show that the proposed method can successfully avoid the obstacles and reach the target object. In addition, the inertial positioning strategy and the improvement of particle swarm optimization can effectively shorten the path of the robot.
SVD-Based Image Watermarking Using the Fast Walsh-Hadamard Transform, Key Mapping, and Coefficient Ordering for Ownership Protection

Proof of ownership on multimedia data exposes users to significant threats due to a myriad of transmission channel attacks over distributed computing infrastructures. In order to address this problem, in this paper, an efficient blind symmetric image watermarking method using singular value decomposition (SVD) and the fast Walsh-Hadamard transform (FWHT) is proposed for ownership protection. Initially, Gaussian mapping is used to scramble the watermark image and secure the system against unauthorized detection. Then, FWHT with coefficient ordering is applied to the cover image. To make the embedding process robust and secure against severe attacks, two unique keys are generated from the singular values of the FWHT blocks of the cover image, which are kept by the owner only. Finally, the generated keys are used to extract the watermark and verify the ownership. The simulation result demonstrates that our proposed scheme is highly robust against numerous attacks. Furthermore, comparative analysis corroborates its superiority among other state-of-the-art methods. The NC of the proposed method is numerically one, and the PSNR resides from 49.78 to 52.64. In contrast, the NC of the state-of-the-art methods varies from 0.7991 to 0.9999, while the PSNR exists in the range between 39.4428 and 54.2599.
Influence of modern information technologies on the worldview

The article reveals and summarizes various approaches to the study of the influence of modern information technologies on the worldview. The importance of addressing the study of a holistic worldview as an integral and dynamic psychological phenomenon is justified. Particular attention is paid to the concepts of computer metaphor, sociocultural pathology and clip thinking. Education approaches are analyzed in this context. The problem of methods for studying the worldview of an individual and group subject in virtual space is discussed: methods for working with big data, sentiment analysis of the text, corpus linguistics methods. Some of the possibilities of these methods are illustrated, their advantages and limitations are considered, their high potential in psychological research is emphasized. Methodological problems of studying the influence of modern information technologies on the worldview are formulated, in particular, the uncertainty of terms in the framework of the concept of clip thinking, the paucity of differential psychological studies and psychological methods.
Familiarity, Causes and Reactions of Software Practitioners to the Presence of Technical Debt: A Replicated Study in the Chilean Software Industry

Decisions made to accomplish short-term goals but affecting negatively the maintainability of the system is called Technical Debt (TD). In Chile, where software industry is rising 11% every year, it becomes crucial to understand how TD is perceived in software teams, its causes and how they are dealing with it. To accomplish this task we ran a TD survey, as a part of InsighTD. Results showed that almost two thirds of the respondents had some level of familiarity with the concept of TD. Among them, lack of qualified professionals is the most cited cause in general and, also, by software architects and developers. Conversely, planning and management is the most cited category of causes of TD. Results indicated that the concept of TD is well understood by software practitioners and that most efforts need to be invested by researchers on offering strategies and tools to support TD management.
Identifying unreliable predictions in clinical risk models

The ability to identify patients who are likely to have an adverse outcome is an essential component of good clinical care. Therefore, predictive risk stratification models play an important role in clinical decision making. Determining whether a given predictive model is suitable for clinical use usually involves evaluating the model's performance on large patient datasets using standard statistical measures of success (e.g., accuracy, discriminatory ability). However, as these metrics correspond to averages over patients who have a range of different characteristics, it is difficult to discern whether an individual prediction on a given patient should be trusted using these measures alone. In this paper, we introduce a new method for identifying patient subgroups where a predictive model is expected to be poor, thereby highlighting when a given prediction is misleading and should not be trusted. The resulting "unreliability score" can be computed for any clinical risk model and is suitable in the setting of large class imbalance, a situation often encountered in healthcare settings. Using data from more than 40,000 patients in the Global Registry of Acute Coronary Events (GRACE), we demonstrate that patients with high unreliability scores form a subgroup in which the predictive model has both decreased accuracy and decreased discriminatory ability.
Continuous-Time Collision Avoidance for Trajectory Optimization in Dynamic Environments

Common formulations to consider collision avoidance in trajectory optimization often use either preprocessed environments or only check and penalize collisions at discrete time steps. However, when only checking at discrete states, this requires either large margins that prevent manipulation close to obstacles or dense time discretization increasing the dimensionality of the optimization problem in complex environments. Nonetheless, collisions may still occur in the interpolation/transition between two valid states or in environments with thin obstacles. In this work, we introduce a computationally inexpensive continuous-time collision avoidance term in presence of static and moving obstacles. Our penalty is based on conservative advancement and harmonic potential fields and can be used as either a cost or constraint in off-the-shelf non-linear programming solvers. Due to the use of conservative advancement (collision checks) rather than distance computations, our method outperforms discrete collision avoidance based on signed distance constraints resulting in smooth motions with continuous-time safety while planning in discrete time. We evaluate our proposed continuous collision avoidance on scenarios including manipulation of moving targets, locomanipulation on mobile robots, manipulation trajectories for humanoids, and quadrotor path planning and compare penalty terms based on harmonic potential fields with ones derived from contact normals.
The OL-DAWE Model: Tweet Polarity Sentiment Analysis With Data Augmentation

Introducing negative items into sentences can shift the polarity of emotional words and leads to misclassification. Therefore, dealing with the negative item is indispensable to the analysis of the polarity of tweets. This paper first uses the combination of Conjunction Analysis (CA) technology and Punctuation Mark Identification (PMI) technology to detect negation cue and its scope. Besides, we propose the OL-DAWE model, which uses Data Augmentation(DA) technology to generate opposed tweets according to the original tweet. The model extends the training data set, and test data set and learns the original and opposed sides of the tweet in the training module. When predicting the polarity of tweets, the OL-DAWE model considers the positive degree (negative degree) of the original tweet and the negative degree (positive degree) of its opposed tweet. We conduct experiments on two real-world data sets. We prove the effectiveness of our combined technology in negation processing and show that the OL-DAWE model in the polarity sentiment analysis of tweets is better than the baseline for its simplicity and high efficiency.
FAOD-Net: A Fast AOD-Net for Dehazing Single Image

In this paper, we present an extremely computation-efficient model called FAOD-Net for dehazing single image. FAOD-Net is based on a streamlined architecture that uses depthwise separable convolutions to build lightweight deep neural networks. Moreover, the pyramid pooling module is added in FAOD-Net to aggregate the context information of different regions of the image, thereby improving the ability of the network model to obtain the global information of the foggy image. To get the best FAOD-Net, we use the RESIDE training set to train our proposed model. In addition, we have carried out extensive experiments on the RESIDE test set. We use full-reference and no-reference image quality evaluation indicators to measure the effect of dehazing. Experimental results show that the proposed algorithm has satisfactory results in terms of defogging quality and speed.
Visual and Textual Jointly Enhanced Interpretable Fashion Recommendation

With the rapid development of online shopping, interpretable personalized fashion recommendation using image has attracted increasing attention in recent years.The current work has been able to capture the user's preferences for visible features and provide visual explanations.However, they ignored the invisible features, such as the material and quality of the clothes, and failed to offer textual explanations.To this end, we propose a Visual and Textual Jointly Enhanced Interpretable (VTJEI) model for fashion recommendations based on the product image and historical review.The VTJEI can provide more accurate recommendations and visual and textual explanations through the joint enhancement of textual information and visual information.Specifically, we design a bidirectional two-layer adaptive attention review model to capture the user's visible and invisible preferences to the target product and provide textual explanations by highlighting some words.Moreover, we propose a review-driven visual attention model to get a more personalized image representation driven by the user's preference obtained from the historical review.In this way, we not only realize the joint enhancement of visual information and textual information but also provide a visual explanation by highlighting some regions.Finally, we performed extensive experiments on real datasets to confirm the superiority of our model on Top-N recommendations.We also built a labeled dataset for evaluating our provided visible and invisible explanations quantitatively.The result shows that we can not only provide more accurate recommendations but also can provide both visual and textual explanations.
A Novel Feature Fusion Method for Computing Image Aesthetic Quality

Computationally, the aesthetic quality of an image means that the model automatically scores the aesthetic level of the image. However, there are many factors that determine beauty or ugliness for photographic photos. Therefore, extracting a variety of representative aesthetic features and fusing these features are still difficult tasks. In this paper, we design a two-stream network to calculate the aesthetic quality of the image. The upper stream of the network is an improved network with the SEResNet-50 and six skip connections added, which can improve the performance of the model without training to obtain deep convolutional neural network features. The lower stream of the network consists of the proposed algorithms for handcrafted extracting aesthetic features and multiple convolution layers to extract the aesthetic features. Finally, to fuse the features of the two-stream network without adding feature dimensions, a novel feature fusion layer is proposed. The results show that this novel feature fusion method can calculate results close to the artificial aesthetic evaluation.
Open-World Person Re-Identification With RGBD Camera in Top-View Configuration for Retail Applications

Person re-identification (re-ID) is currently a notably topic in the computer vision and pattern recognition communities. However, most of the existing works on re-ID have been designed for closed world scenarios, rather than more realistic open world scenarios, limiting the practical application of these re-ID techniques. In a common real-world application, a watch-list of known people is given as the gallery/target set for searching through a large volume of videos where the people on the watch-list are likely to return. This aspect is fundamental in retail for understanding how customers schedule their shopping. The identification of regular and occasional customers allows to define temporal purchasing profiles, which can put in correlation the customers' temporal habits with other information such as the amount of expenditure and number of purchased items. This paper presents the first attempt to solve a more realistic re-ID setting, designed to face these important issues called Top-View Open-World (TVOW) person re-id. The approach is based on a pretrained Deep Convolutional neural Network (DCNN), finetuned on a dataset acquired by using a top-view configuration. A special loss function called triplet loss was used to train the network. The triplet loss optimizes the embedding space such that data points with the same identity are closer to each other than those with different identities. The TVOW is evaluated on the TVPR2 dataset for people re-ID that is publicly available. The experimental results show that the proposed methods significantly outperform all competitive state-of-the-art methods, bringing to different and significative insights for implicit and extensive shopper behaviour analysis for marketing applications.
Results of the Multi-Domain Task-Completion Dialog Challenge

The paper provides an overview of the “Multi-domain Task Completion” track (Track 1) at the 8th Dialog System Technology Challenge (DSTC-8). There are two tasks in this track. The first task is end-to-end multi-domain task-completion, which aims to build end-to-end task completion dialog systems based on ConvLab. The second task is fast domain adaptation, seeking to develop models that predict user responses when only limited in-domain data is available. We describe the submissions for both tasks, automatic evaluation and human evaluation procedures, and discuss the outcomes of these two evaluations.
Sea Food Supply Chain Management Using Blockchain

Blockchain is a distributed open ledger that is used to record the transaction across many computers. Blockchain technology can be applied in any domain such as banking, healthcare, real estate, travel, food, and supply chain. Blockchain allows us to have a distributed peer to peer networks, so it provides security and privacy. Example: Bitcoin. To enhance the Supply chain management using in the blockchain using some unique tags (Example: RFID/NFC/QR Code) tags describe the information about products and track the products. This paper discusses the blockchain in supply chain management to eliminate the disadvantages of the supply chain in the life cycle. In order to provide the solution of supply chain management to using these unique tags to get all details for manufacturing date/expiry date, etc., from the manufacturer side so no can alter this tag for the manufacturer to build a trustworthy of wholesaler/retailer/consumer for using the special tags.
A Frequency-Separated 3D-CNN for Hyperspectral Image Super-Resolution

Considering the limitations such as cost, it is of great significance to use super-resolution methods to improve image spatial quality in the field of hyperspectral remote sensing. Due to little dependence on auxiliary information which is difficult to obtain, i.e., multispectral images and natural images, methods based on single-frame are generally considered to have good flexibility and application value. In this paper, a three-dimensional convolutional neural network with three branches combined with an analytical method is proposed, achieving better SR quality and suppressing spectral distortion as well. Firstly, the wavelet transformation is introduced to decompose the hyperspectral image into a variety frequency of components effectively and reversibly. Then, these components are fed into different three-dimensional convolutional branches respectively. Finally, hyperspectral images with high resolution are obtained by dimension amplification, detail reconstruction and inverse wavelet transformation. The presence of frequency separation and the architecture of our model having different branches designed according to frequency make it better than comparable approaches. The method proposed in this paper not only combines the high efficiency of analytical method and the flexibility of neural network, but inhibits the influence of spectral distortion as well. Compared with the state-of-art methods on real space-based hyperspectral image datasets, the effectiveness of the proposed method is demonstrated.