Reviewerly/exHarmony huggingface dataset:
Research on emotion classification technology of movie reviews based on topic attention mechanism and dual channel long short term memory

In recent years, with the popularity of the Internet, more and more people like to comment on movies they have watched on the film platform after watching them. These reviews hide the reviewers' feedback on films. Mining the emotional orientation information in these reviews can provide consumers with shopping references and help businesses optimize film works and improve business strategies. Therefore, the emotional classification of film reviews has high research value because few emotion dictionaries and analysis tools are available for reference and use in film reviews. The accuracy of emotion classification still needs to be improved. This study introduces the attention mechanism and dual channel long short term memory (DC-LSTM) while building the emotion dictionary in the field of Chinese film review. It classifies Chinese film reviews in terms of topic-based fine-grained emotion. First, the emotion vector is constructed using the constructed movie review emotion lexicon. The semantic vector obtained by the Word2vector tool is input to LSTM to encode the comment text. Then, the topic attention module is used to decode. Finally, the final emotion classification result is obtained through the softmax function of the entire link layer and the output layer. The thematic attention modules constructed in this study are independent of each other for attention parameter adjustment and learning. One attention module corresponds to one film theme. In this study, eight themes, including "plot," "special effects," "original work," "music," "thought," "theme," "acting skills," and "joke," were extracted, and each theme was classified into three types of emotions: "positive," "neutral," and "negative." The experimental results on the crawled Chinese film review dataset show that the proposed algorithm is superior to some existing algorithms and models in accuracy, precision, recall and F1 measure. The DCLSTM based on the thematic attention mechanism (DCLSTM-TAM) model constructed in this study introduces the emotion vector into the network and adds the theme attention mechanism. It can not only classify the emotion for different topics of a film review but also effectively deal with film reviews with fuzzy emotional tendencies. It realizes the fine-grained emotion classification of film topics and improves the accuracy of emotion classification of film reviews. The emotion classification method and model proposed in this study have good transferability, and the change of training corpus is also applicable to other short text fields.
Semantic Annotator for Knowledge Graph Exploration : Pattern-Based NLP Technique

Semantic Annotator for knowledge Graph Exploration, abbreviated as SAGE is a “Thing” annotation system. Here, “Thing” refers to any concept, named individuals (aka entities), entity relations, and attributes. The system is primarily built based on the idea of “string to thing” where the “string” is any given text (e.g., abstract of an article) as input by the user. For annotation, the system utilises knowledge graph(s). SAGE can be used by anyone for annotating Things and for their exploitation on the Web. The annotation of things is done through exact and partial matches. For exact matches, the system makes explicit the name of the knowledge graphs it is sourced from. It also shows the type hierarchies for the matched named entities. In the current work, we describe the SAGE annotation system, designed on pattern-based NLP techniques, along with its features and various usage, and the experimental results.
Translation of atherosclerotic disease features onto healthy carotid ultrasound images using domain-to-domain translation

In this work, we evaluated a model for the translation of atherosclerotic disease features onto healthy carotid ultrasound images. An un-paired domain-to-domain translation model – the cycle Generative Adversarial Network (cycleGAN) – was trained to translate between carotid ultrasound images of healthy arteries and images of pronounced disease. Translation performance was evaluated using the measurement of wall thickness in original and generated images. In addition, we explored disease translation in different tissue segments (subcutaneous tissue, muscle, lumen, far wall, and deep tissues), using structural similarity index measure (SSIM) maps. Features of pronounced disease were successfully translated to the healthy images (1.2 (0.33) mm vs 0.43 (0.07) mm, p < 0.001), while overall anatomy was retained as SSIM value was equal to 0.78 (0.02). Exploration of translated features showed that both arterial wall and subcutaneous tissues were modified in the translation, but that the subcutaneous tissue was subject to distortion of the anatomy in some cases. The image quality influenced the disease translation performance. The results show that the model can learn a mapping between healthy and diseased images while retaining the overall anatomical contents. This is the first study on atherosclerosis disease translation in medical images. The concept of translating disease onto existing healthy images may serve purposes such as education, cardiovascular risk communication in health conversations, or personalized modelling in precision medicine.
SMART: A Lightweight and Reliable Multi-Path Transmission Model against Website Fingerprinting Attacks

The rapid development of IoT technology has promoted the integration of physical space and cyberspace. At the same time, it has also increased the risk of privacy leakage of Internet users. A large number of research works have shown that attackers can infer Internet surfing privacy through traffic patterns without decryption. Most of the existing research work on anti-traffic analysis is based on a weakened experimental assumption, which is difficult to apply in the actual IoT network environment and seriously affects the user experience. This article proposes a novel lightweight and reliable defense—SMART, which can ensure the anonymity and security of network communication without sacrificing network transmission performance. SMART introduces a multi-path transmission model in the Tor network, and divides traffic at multiple Tor entry onion relays, preventing attackers from obtaining network traffic statistical characteristics. We theoretically proved that SMART can improve the uncertainty of website fingerprint analysis results. The experimental result shows that SMART is able to resist encrypted traffic analysis tools, reducing the accuracy of four state-of-the-art classifiers from 98% to less than 12%, without inducing any additional artificial delay or dummy traffic. In order to avoid the performance degradation caused by data reassembly, SMART proposes a redundant slice mechanism to ensure reliability. Even in the case of human interference, the communication success rate is still as high as 97%.
Multiplication free Fast-Adaptive Binary Range Coder using ISW

Data compression is defined as the process of encoding, converting and modifying the bits-structures of data in such a way that reduces less-spaces on the disk. Fast-ABRC, a new context ABRC for compressing the image and video. This paper introduces novel hardware F-ABRC (Fast-adaptive binary range coder) and architecture of VLSI, as it doesn’t have requirement of LUTs (Look-up-Tables) and also it is completely multiplication free. To get the result, we will combine the utilization of simple operation to compute the approximation after encoding every single symbol and the PE (probability estimation) on the basis of ISW (Imaginary Sliding Window) with approximation of the multiplication. We have represented our introduced algorithm, which is faster and in comparison, to the existing model it gives superior compression efficiency and the comparison takes place on the basis of two parameters such as power dissipation (Dynamic and Static) and device utilization.
Vlasiator.jl: A Julia package for processing Vlasiator data

Vlasiator.jl is a Julia (Bezanson et al., 2017) package for processing and analyzing simulation data from the collisionless ion-kinetic plasma physics numerical model Vlasiator (Pfau-Kempf et al., 2022).This lightweight package retains all the actively used functionalities in its sister Python package Analysator (Battarbee et al., 2021) and is carefully designed for performance, capability and ease of use.Vlasiator.
Chinese Event Detection without Triggers Based on Dual Attention

In natural language processing, event detection is a critical step in event extraction, aiming to detect the occurrences of events and categorize them. Currently, the defects of Chinese event detection based on triggers include polysemous triggers and trigger-word mismatches, which reduce the accuracy of event detection models. Therefore, event detection without triggers based on dual attention (EDWTDA), a trigger-free model that can skip the trigger identification process and determine event types directly, is proposed to fix the problems mentioned above. EDWTDA adopts a dual attention mechanism, integrating local and global attention. Local attention captures key semantic information in sentences and simulates hidden event trigger words to solve the problem of trigger-word mismatch, while global attention digs for the context of documents, fixing the problem of polysemous triggers. Besides, event detection is transformed into a binary classification task to avoid problems caused by multiple tags. Meanwhile, the sample imbalance brought about by the transformation is settled with the application of the focal loss function. The experimental results on the ACE 2005 Chinese corpus show that, compared with the best baseline model, JMCEE, the accuracy rate, recall rate, and F1-score of the proposed model increased by 3.40%, 3.90%, and 3.67%, respectively.
Leveraging transition exploratory bonus for efficient exploration in Hard-Transiting reinforcement learning problems

In reinforcement learning (RL), agents learn policies from spatiotemporal data generated by interaction with the environment. However, spatiotemporal data containing reward signals are often sparse, the agent only gets a reward signal when it reaches the goal state. This kind of problem is technically challenging since rewards are the basis of policy learning. Recently, the prior knowledge of the task structure has been used to solve such problems such as hierarchical learning and goal-conditioned learning. In this paper, we consider a common task structure called Hard-Transiting. In a Hard-Transiting task, the difficulty of moving forward of the agent increases as it approaches the goal state. We formalize the Hard-Transiting RL problem with sparse rewards in which the transition probability decreases as the agent approaches the goal state. Inspired by reward bonus in interactive spatiotemporal data, we propose two novel algorithms with efficient exploration to solve such problems. For tabular setting, we adopt Transition Exploratory Bonus (TEB) to encourage exploration in Hard-Transiting problems and propose Model-Based Interval Estimation-TEB (MBIE-TEB) in which TEB is considered in the value iteration phase of the conventional MBIE algorithm. And under the performance metric of sample complexity, we give a theoretical proof of the upper bound of MBIE-TEB. For non-tabular setting, we propose Deep Q-Network-TEB (DQN-TEB) in which TEB is used as the intrinsic motivation in DQN. We test the proposed algorithms on two numerical tasks and one large-scale task. And the experimental results demonstrate that with the transition exploratory bonus, the proposed algorithms outperform the compared algorithms.
A Novel Short-Memory Sequence-Based Model for Variable-Length Reading Recognition of Multi-Type Digital Instruments in Industrial Scenarios

As a practical application of Optical Character Recognition (OCR) for the digital situation, the digital instrument recognition is significant to achieve automatic information management in real-industrial scenarios. However, different from the normal digital recognition task such as license plate recognition, CAPTCHA recognition and handwritten digit recognition, the recognition task of multi-type digital instruments faces greater challenges due to the reading strings are variable-length with different fonts, different spacing and aspect ratios. In order to overcome this shortcoming, we propose a novel short-memory sequence-based model for variable-length reading recognition. First, we involve shortcut connection strategy into traditional convolutional structure to form a feature extractor for capturing effective features from characters with different fonts of multi-type digital instruments images. Then, we apply an RNN-based sequence module, which strengthens short-distance dependencies while reducing the long-distance trending memory of the reading string, to greatly improve the robustness and generalization of the model for invisible data. Finally, a novel short-memory sequence-based model consisting of a feature extractor, an RNN-based sequence module and the CTC, is proposed for variable-length reading recognition of multi-type digital instruments. Experimental results show that this method is effective on variable-length instrument reading recognition task, especially for invisible data, which proves that our method has outstanding generalization and robustness in real-industrial applications.
Hash Indexing-Based Image Matching for 3D Reconstruction

Image matching is a basic task in three-dimensional reconstruction, which, in recent years, has attracted extensive attention in academic and industrial circles. However, when dealing with large-scale image datasets, these methods have low accuracy and slow speeds. To improve the effectiveness of modern image matching methods, this paper proposes an image matching method for 3D reconstruction. The proposed method can obtain high matching accuracy through hash index in a very short amount of time. The core of hash matching includes two parts: creating the hash table and hash index. The former is used to encode local feature descriptors into hash codes, and the latter is used to search candidates for query feature points. In addition, the proposed method is extremely robust to image scaling and transformation by using various verifications. A comprehensive experiment was carried out using several challenging datasets to evaluate the performance of hash matching. Experimental results show that the HashMatch presents excellent results compared to the state-of-the-art methods in both computational efficiency and matching accuracy.
Optimizing Video Compression With CNN-Based Autoencoders With Chroma Subsampling

In the context of convolutional neural network (CNN)-based video compressions, motivated by the lower acuity of the human visual system for color differences when compared with luma, we investigate a video compression framework using autoencoder networks that encode and decode videos by using less chroma information than luma information. For this purpose, instead of converting Y’CbCr 4:2:2/4:2:0 videos to and from RGB 4:4:4 as per the current state of the art, we have kept the video in Y’CbCr 4:2:2/4:2:0 and merged the luma and chroma channels after the luma is downsampled to match the chroma size. We have performed an inverse function for the decoder. The performance of our models against the 4:4:4 baseline is evaluated by using color peak signal-to-noise ratio (CPSNR), multiscale structural similarity (MS-SSIM), and video multimethod assessment fusion (VMAF) metrics. Our experiments reveal that, when compared to video compression involving conversion to and from RGB 4:4:4, the proposed method increases the video quality by about 5% for Y’CbCr 4:2:2 and 6% for Y’CbCr 4:2:0 while reducing the amount of computation by nearly 37% for Y’CbCr 4:2:2 and 40% for Y’CbCr 4:2:0. These results point us to optimization for 4:2:2 and 4:2:0 video of the current state-of-the-art autoencoder.
DTCC: Multi-level dilated convolution with transformer for weakly-supervised crowd counting

Abstract Crowd counting provides an important foundation for public security and urban management. Due to the existence of small targets and large density variations in crowd images, crowd counting is a challenging task. Mainstream methods usually apply convolution neural networks (CNNs) to regress a density map, which requires annotations of individual persons and counts. Weakly-supervised methods can avoid detailed labeling and only require counts as annotations of images, but existing methods fail to achieve satisfactory performance because a global perspective field and multi-level information are usually ignored. We propose a weakly-supervised method, DTCC, which effectively combines multi-level dilated convolution and transformer methods to realize end-to-end crowd counting. Its main components include a recursive swin transformer and a multi-level dilated convolution regression head. The recursive swin transformer combines a pyramid visual transformer with a fine-tuned recursive pyramid structure to capture deep multi-level crowd features, including global features. The multi-level dilated convolution regression head includes multi-level dilated convolution and a linear regression head for the feature extraction module. This module can capture both low- and high-level features simultaneously to enhance the receptive field. In addition, two regression head fusion mechanisms realize dynamic and mean fusion counting. Experiments on four well-known benchmark crowd counting datasets (UCF_CC_50, ShanghaiTech, UCF_QNRF, and JHU-Crowd++) show that DTCC achieves results superior to other weakly-supervised methods and comparable to fully-supervised methods.
StringENT test suite: ENT battery revisited for efficient P value computation

Abstract Random numbers play a key role in a wide variety of applications, ranging from mathematical simulation to cryptography. Generating random or pseudo-random numbers is not an easy task, especially when hardware, time and energy constraints are considered. In order to assess whether generators behave in a random fashion, there are several statistical test batteries. ENT is one of the simplest and most popular, at least in part due to its efficacy and speed. Nonetheless, only one of the tests of this suite provides a p value, which is the most useful and standard way to determine whether the randomness hypothesis holds, for a certain significance level. As a consequence of this, rather arbitrary and at times misleading bounds are set in order to decide which intervals are acceptable for its results. This paper introduces an extension of the battery, named StringENT, which, while sticking to the fast speed that makes ENT popular and useful, still succeeds in providing p values with which sound decisions can be made about the randomness of a sequence. It also highlights a flagrant randomness flaw that the classical ENT battery is not capable of detecting but the new StringENT notices, and introduces two additional tests.
A time-sensitive learning-to-rank approach for cloud simulation resource prediction

Abstract Predicting the computing resources required by simulation applications can provide a more reasonable resource-allocation scheme for efficient execution. Existing prediction methods based on machine learning, such as classification/regression, typically must accurately predict the runtime of simulation applications and select the optimal computing resource allocation scheme by sorting the length of the simulation runtime. However, the ranking results are easily affected by the simulation runtime prediction accuracy. This study proposes a time-sensitive learning-to-rank (LTR) approach for cloud simulations resource prediction. First, we use the Shapley additive explanation (SHAP) value from the field of explainable artificial intelligence (XAI) to analyze the impact of relevant factors on the simulation runtime and to extract the feature dimensions that significantly affect the simulation runtime. Second, by modifying the target loss function of the rankboost algorithm and training a time-sensitive LTR model based on simulation features, we can accurately predict the computing resource allocation scheme that maximizes the execution efficiency of simulation applications. Compared with the traditional machine learning prediction algorithm, the proposed method can improve the average sorting performance by 3%–48% and can accurately predict the computing resources required for the simulation applications to execute in the shortest amount of time.
Introduction to Special Section: Disremembering the Harrisons

March 29 2023 Introduction to Special Section: Disremembering the Harrisons Janeil Engelstad Janeil Engelstad Janeil Engelstad, Make Art with Purpose (MAP) Global Innovation and Design Lab University of Washington, Tacoma Email: janeil@makeartwithpurpose.net. Search for other works by this author on: This Site Google Scholar Author and Article Information Janeil Engelstad Janeil Engelstad, Make Art with Purpose (MAP) Global Innovation and Design Lab University of Washington, Tacoma Email: janeil@makeartwithpurpose.net. Online ISSN: 1530-9282 Print ISSN: 0024-094X © ISAST2023ISAST Leonardo 306. https://doi.org/10.1162/leon_a_02378 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Janeil Engelstad; Introduction to Special Section: Disremembering the Harrisons. Leonardo 2023; doi: https://doi.org/10.1162/leon_a_02378 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsLeonardo Search Advanced Search This content is only available as a PDF. © ISAST2023ISAST Article PDF first page preview Close Modal You do not currently have access to this content.
Practical Reasoning and Practical Argumentation: A Stakeholder Commitment Approach

This paper examines the conceptual and terminological overlap between theories and models of practical deliberation developed within the fields of Practical Reasoning (PR) and Practical Argumentation (PA). It carefully delineates the volitional, epistemic, normative, and social commitments invoked and explicates various rationales for attributing the label 'practical' to instances of reasoning and argumentation. Based on these analyses, the paper develops a new approach to practical deliberation called the Stakeholder Commitment Approach (SCA). By distinguishing between 'problem holder' and 'problem solver', and specifying the distributions of attributable commitments among the stakeholders, the SCA introduces an extension and refinement of the grounds for assigning the label 'practical' that brings PR and PA closer together.