Reviewerly/exHarmony huggingface dataset:
Graph Transformer for Human Parsing

The human parsing task is dedicated to allocating pixel-level fine-grained semantic labels. However, recent solutions are limited to utilize the additional prior information (poses and edges), remaining problem with incomplete and mislabeling human parts. To solve the above problem, we propose a novel Graph Transformer, which can fully capture the correlation among different human parts implied in the image without priors. Firstly, a graph representation module is developed to generate graph represented part features. Secondly, inferring the graph features to acquire the semantic correlation. Comprehensive experiments and corresponding results on three public datasets show that the Graph Transformer performs favorably against the state-of-the-art methods.
Perceptual Model Hashing: Towards Neural Network Model Authentication

A lot of excellent neural network models are valuable wealth to the field of artificial intelligence, which may be plagiarized and distributed without authorization. For this reason, few research establishments and industries reveal the internals of their neural network models. To authenticate suspicious pirated models, this letter proposes a gray-box hashing method for the neural network models that designed for image classification. In the proposed method, the hash sequence of original model can be extracted without knowing both the structure and weight parameters except the vectors in output layer. To the best of our knowledge, this is the first work focusing on gray-box perceptual model hashing to identify and authenticate neural network models. Experimental results show that our method performs satisfactory perceptual robustness and discrimination capability, and can effectively classify perceptual similar versions of the original model and distinct models.
3WS-ITSC: Three-Way Sampling on Imbalanced Text Data for Sentiment Classification

Sentiment analysis is an important research direction of natural language processing. The data imbalance is a critical issue in text sentiment classification task. That arises the problem of high misclassification cost. This paper proposes a three-way sampling sentiment classification model for imbalanced text data to reduce the misclassification cost. Specifically, the model extracts boundary points through three-way sampling and collaborates with cost-sensitive learning for action on sampled results. Firstly, in order to reduce sampling time, the text data is converted into a one-dimensional vector by bag mapping. Secondly, three-way sampling is used to obtain boundary points that can characterize the majority class. Finally, a sequential three-way sentiment classification algorithm is used to predict sentiment polarity. The experimental results show that the proposed model outperforms state-of-the-art sentiment classification methods in the scenario of extremely imbalanced test data.
Analysis of Public Emotion and Disseminator-Audience Framework during Outbreak of Public Crisis Events: A case study of Microblog Account Xiake Island

This study crawled the cross-sectional data of the contents and comments from Microblog Account Xiake Island during the outbreak of coronavirus pneumonia as subjects, to examine the deviation and resonance association among affective fluctuations of the Chinese public, media framework, and audiences’ cognitive framework. Using SnowNLP to conduct sentiment analysis of text comments, we found that during the outbreak of coronavirus pneumonia, the public spent most of the time in low-intensity negative affectivity, and the average affective propensity in response to individual microblog fluctuated greatly, and the public was easily caught in an emotional frenzy, which reduces the level of trust in government. Through a comparison of public affectivity and related epidemic data, Xiake Island focuses on reporting emotional facts, whose construction of social reality contains obvious emotional trajectories. Clustering analysis of thematic framework by LDA algorithm reveals that in terms of framework, the framework Xiake Island uses resonates to a large degree with the framework users focus on. In terms of the level of concerns over the framework, Xiake Island deviates to a certain extent from the public. This deviation, together with the strategy of focusing on reporting emotional facts, is a discursive strategy adopted by the new mainstream media to seek the reconstruction of cultural leadership.
Enhancing Indic Handwritten Text Recognition Using Global Semantic Information

Handwritten Text Recognition (htr) is more interesting and challenging than printed text due to uneven variations in the handwriting style of the writers, content, and time. htr becomes more challenging for the Indic languages because of (i) multiple characters combined to form conjuncts which increase the number of characters of respective languages, and (ii) near to 100 unique basic Unicode characters in each Indic script. Recently, many recognition methods based on the encoder-decoder framework have been proposed to handle such problems. They still face many challenges, such as image blur and incomplete characters due to varying writing styles and ink density. We argue that most encoder-decoder methods are based on local visual features without explicit global semantic information. In this work, we enhance the performance of Indic handwritten text recognizers using global semantic information. We use a semantic module in an encoder-decoder framework for extracting global semantic information to recognize the Indic handwritten texts. The semantic information is used in both the encoder for supervision and the decoder for initialization. The semantic information is predicted from the word embedding of a pre-trained language model. Extensive experiments demonstrate that the proposed framework achieves state-of-the-art results on handwritten texts of ten Indic languages.
Physical Black-Box Adversarial Attacks Through Transformations

Deep learning has shown impressive performance in numerous applications. However, recent studies have found that deep learning models are vulnerable to adversarial attacks, where the attacker adds imperceptible perturbations into benign samples to induce misclassifications. Adversarial attacks in the digital domain focus on constructing imperceptible perturbations. However, they are always less effective in the physical world because the perturbations may be destroyed when captured by the camera. Most physical adversarial attacks require adding invisible adversarial features (e.g., a sticker or a laser) to the target object, which may be noticed by human eyes. In this work, we propose to employ image transformation to generate more natural adversarial samples in the physical world. Concretely, we propose two attack algorithms to satisfy different attack goals: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Efficient-AATR</i> employs a greedy strategy to generate adversarial samples with fewer queries; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Effective-AATR</i> employs an adaptive particle swarm optimization algorithm to search for the most effective adversarial samples within the given the number of queries. Extensive experiments demonstrate the superiority of our attacks compared with state-of-the-art adversarial attacks under mainstream defenses.
A Dense-Aware Cross-splitNet for Object Detection and Recognition

Object detection and recognition is widely used in various fields and have become key technologies in computer vision. The distribution of objects in natural images can be roughly divided into densely stacked objects and scattered objects. Due to the incomplete attributes or features of some objects in densely stacked distributions, some object detectors have missed local area details or low detection accuracy. In this paper, we propose Cross-splitNet, a novel cross-split method for dense object detection and recognition based on candidate box generation. First, an adaptive feature extraction network is constructed. Different datasets are input into convolutional neural networks with various depths, the generalization of the model. Then, the proposed cross-split algorithm is introduced to guide the different deep networks to learn features of images with various densities, according to intermediate object density classification results. Finally, we adopt a feature pyramid network (FPN) subnet to perform multi-scale feature extraction while retaining lower-layer object information and physical characteristics. The model was trained on the COCO 17, VOC 12, and VOC 07 datasets, which contain a large number of object categories. Our network was compared with several two-stage detectors, and the results show that our model achieved an average precision (AP) of 0.819 at 22.9 frames per second (FPS) on the VOC 07+12 dataset. The mean average precision (mAP) of the object detection model with R50+R2-101 backbones on the COCO dataset was increased by 1.9%.
Participatory Design Fictions with Mixed Reality: A User Study Framework for Future Smart Cockpit

Before widely adopting in the real life, Emerging technologies and design concepts require appropriate user studies to explore demands from users. Smart cockpit is a typical fields driven by cutting-edge technologies, and vehicles are becoming more intelligent touchpoints empowered by V2X technologies. A repeated, correlative and continuous framework was employed for future-oriented user study such as smart cockpit’s connectivity capability in the context of V2X, by presenting the Participatory Design Fictions with Mixed Reality, which aims for stimulating imagination of the participants to gather their views and discussions about the future. Thematic analysis, discourse analysis and creative analysis were adopted to evaluate this framework and method. Results indicated that Participatory Design Fictions with Mixed Reality provided researchers with more in-depth insights about the preferable futures articulated by different groups when conducting future-oriented, demand mining-oriented user study as a effective tool and method.
A Concept of Unbiased Stochastically Deterministic Policy Gradient for Better Convergence in Bipedal Walker

The main goal of this work is to play a role of playground to further improve our own conceptual model, or “unbiased Deep Deterministic Policy Gradient (uDDPG)” developed recently: due to search for an algorithm that can provide faster convergence to better policies the agent can meet a problem of falling to the local optimum regions and slightly improve from there for the rest of experiments. In other words, proposed concept has a problem of limited policy generalization, especially for more complex systems. To achieve a better exploration and consequently search for higher scores, we tried to equip our concept with simplified ideas from the notable works: we unanchored uDDPG or in parallel added Q network's update without the link to real Q values but with Entropy Regularization of soft Q learning based algorithms, e.g. Soft Actor and Critic; additionally, reduced ideas behind Forward-Looking Actor (FORK), Twin Delayed DDPG and Distributional Perspective on Q value were utilized for more “optimistic” but “safer” search.
Optimizing Image Captioning using Deep Learning based Object Detection

Many of today’s facilities available for the general public around the world are generally not accessible for people with disabilities. In the first place, accessibility is not even considered by many before providing the facilities. And among such people with disabilities are the ones with visual impairment. Many places like supermarkets, train stations, restaurants, etc., do not provide or have the necessary equipment to help such people interact with their surroundings. Naturally, they face difficulties in completing their purpose. The task of Image Captioning in Computer Vision helps in providing software solutions for such people. With the help of Image Captioning, these people can get to know their surroundings and interact accordingly. It does not entirely solve the problem at hand but it does help them to a certain extent. For these models to work correctly, they need to be optimised. This project aims at leveraging the state-of-the-art Object Detection models and combine them with the standard Image Captioning models to make the results more accurate.
Research on Quantitative Investment Risk Assessment Method of Financial Derivatives

Based on the quantitative investment of financial derivatives, this paper evaluates and prevents its existing risks. Risk, use the comprehensive evaluation model to simulate the application and use the analytic hierarchy process to complete the establishment of the product risk evaluation index system. The study shows that the probability of occurrence of lower risk in the HHY series capital- guaranteed, investment products of H Bank is 36.2%, and the probability of occurrence of general risk is 34.8%. There is a greater possibility of general risk and lower risk, and there is a certain investment risk. According to the quantitative risk assessment results, for China at this stage, structured wealth management products have put forward feasible risk management suggestions, including classified management of wealth management products, and targeted development of the wealth management market. The whole process of supervision and regulation should be carried out to prevent the occurrence of systemic risks; the responsibilities of banks should be stipulated, and external information disclosure should be strengthened. Expose; strengthen investors' awareness of investment risks through education.
Deep Guided Context-aware Network for Anomaly Detection in Musculoskeletal Radiographs

Automated anomaly detection and localization in musculoskeletal radiographs are essential for large-scale screening in the radiograph workflow. However, the anomaly is a localized pattern that may be affected by the extra irrelevant areas, and the ambiguous and subtle features of abnormal regions are difficult to detect. To tackle these two problems, we propose a deep guided context-aware network (DR-Net) for anomaly detection in musculoskeletal X-rays. Specifically, we design a positional guide module, which embeds the spatial positional information as prior knowledge to guide the network to enhance the feature representation of a specific region. Then, to detect subtle anomalies, we construct a contextual relation module. It can obtain context-aware features by capturing the spatial dependence of any two positions from the entire X-ray image. It combines context appearance information and selects more distinguishable features from space and channel, producing a detailed visualization of the anomaly region. Note that only image-level labels are required. The extensive experiments on the two radiograph datasets show that DR-Net has a promising performance in anomaly detection and localization.
VDS: A Variant of Δ-stepping Algorithm for Parallel SSSP Problem

Δ-stepping is a famous parallel algorithm for the single-source shortest path problem. It requires a tuning parameter (delta) to achieve a good trade-off between parallelism and work efficiency. The performance of Δ-stepping changes drastically with the changing value of delta. A poor choice of delta leads to an inefficient Δ-stepping algorithm. For large graphs, finding the best-performing value of delta is difficult. This paper proposes a variant of the Δ-stepping algorithm (VDS). We have evaluated the proposed algorithm on graph500 data sets. Our results show that the proposed algorithm is equally work-efficient and scalable compared to Δ-stepping, and its performance remains almost stable with the changing value of delta. Against the best performing value of delta, VDS’s performance on different deltas varies up to 136%, whereas Δ-stepping’s performance varies up to 430%. For the best performing value of delta, the proposed algorithm is competitive or slightly efficient compared to the Δ-stepping. And, for the most inefficient delta, the proposed algorithm is 2.8–3.6x faster than the Δ-stepping.
Access Control System Using Multi-factor Authentication

A secure user authentication process is a key prerequisite for ensuring the security of the entire electronic system.On the other hand, current systems usually deploy many constrained devices with limited computational power, memory space and cryptographic support.This makes it hard to deploy secure cryptographic mechanisms in this environment.In this article, we present our multifactor authentication system using a reader with a secure module represented with MultOS smart card and an Android smart phone acting as a user authentication device.The system supports NFC (Near Field Communication) communication interface for intermediating communication between smart phone and reader, supports additional authentication factors (e.g.PIN code or fingerprint) and is easily implementable even on very constrained devices such as smart cards.
Based on Big Data Exploration of the “Trinity” Collaborative Training Mechanism of Primary School General Teachers, Local Government, Colleges and Universities, and Local Primary Schools

With the global demand for primary school teachers and the demand for practical training capabilities for teachers in educational reforms, local governments, universities and local primary schools will shoulder the important task of cultivating outstanding teachers in the future. For the teaching profession with strong professional practice, the training process of general education teachers should not only pay attention to the acquisition of general subject knowledge, but also strengthen the formation of general subject practical ability and the cultivation of excellent general subject quality. This paper analyzes the previous general education practice training ability training system lacks the method of introducing unified practice education, and establishes a systematic practice training ability training system. Finally, according to the implementation and reflection of the plan in the course of action, it is believed that the process of cultivating the practical ability of ordinary students should be constantly reviewed, and new modules should be added as ordinary students improve. The study found that in the process of constructing the educational practice training practice system of ordinary colleges and universities, the renewal of training concepts, the implementation of training plans and the continuous reflection of training methods are the main ways to improve training ability.
Modeling Tradeoffs Using Preference-Based Feedback in Session-Based Recommender Systems

Cold-start is a challenge in most data-driven approaches across various paradigms of recommender systems research. Knowledge integration is becoming essential to alleviate the problem. Despite the availability of knowledge representations like the knowledge graph, there is a need for a richer semantic model to capture relationships among products in a domain. Recommender systems inspired by case-based reasoning uses rich domain knowledge in their recommendation process. Conversational case-based reasoning recommender systems (CCBR-RS) are session-based recommender systems that operate under cold-start assumptions. User preferences in such scenarios can only be learned based on the context of the current session. Tradeoffs have been used in literature to model the relationship among products in recommending appropriate products. The models in earlier works have been based on crisp sets, which lack flexibility in capturing varying degrees of tradeoffs, which is a more natural way of dealing with tradeoffs. This work proposes a flexible tradeoff representation scheme based on fuzzy sets to model tradeoffs in terms of linguistic terms and a fuzzy inference system that measures the similarity among tradeoffs represented in linguistic terms. We demonstrate the performance improvement in utilizing a more flexible model of tradeoffs by evaluating the methods on three datasets in a CCBR-RS framework.
SFMNet:focusing on feature attention for image classification

In the process of convolution network processing, a convolution of each layer can be regarded as a mapping operation of image features. As we all know, when the human eye maps the prescene capture to the brain neurons, the perception of different parts of a scene is different, especially when the perspective focuses on a part or the characteristics of an object, the visual cortex pays more attention to this part. In the construction of CNN, there is consideration of image perception field, but little consideration of image feature area. Therefore, this paper proposes a convolution module called "select future map" (SFM). The model uses the residual idea of RESNET network and the use of channel attention in SENet network, and develops the attention to its own characteristic attention. The structure of convolution module is divided into three parts. The first part is multi module feature convolution, which is used to extract the features of each module in the image. The second part is feature processing, which is used to pay attention to the proportion of different module features. The third part is feature aggregation, which multiplies the modular convolution and the corresponding specific gravity to obtain the next feature image. Taking this as the basic unit, add modules to form a network, and the final experimental results get an accuracy of 84.12%.
Exploring the Impacts of Power Grid Signals on Data Center Operations Using a Receding-Horizon Scheduling Model

Data centers (DCs) can help decarbonize the power grid by helping absorb renewable power (e.g., wind and solar) due to their ability to shift power loads across space and time. However, to harness such load-shifting flexibility, it is necessary to understand how grid signals (carbon signals and market price/load allocations) affect DC operations. An obstacle that arises here is the lack of computationally-tractable DC operation models that can capture objectives, constraints, and information flows that arise at the interface of DCs and the power grid. To address this gap, we present a receding-horizon resource management model (a mixed-integer programming model) that captures the resource management layer between the DC scheduler and the grid while accounting for logical constraints, different types of objectives, and forecasts of incoming job profiles and of available computing capacity. We use our model to conduct extensive case studies based on public data from Microsoft Azure and MISO. Our studies show that DCs can provide significant temporal load-shifting flexibility that results in reduced carbon emissions and peak demand charges. Models and case studies are shared as easy-to-use Julia code.
Implementation of K-Nearest Neighbor to Predict the Chances of COVID-19 Patients’ Recovery

Coronavirus Disease 2019 (COVID-19) is a new disease discovered in 2019 in Wuhan, China, and then spread worldwide. Many victims have confirmed varying positive levels of infection based on the patient’s immunity. This study aimed to predict the chances of COVID-19 patients’ recovery based on the patient’s symptoms and conditions. The method used is the K-Nearest Neighbor (KNN) algorithm. KNN produces two classes of predictions: the chance of recovering or the possibility of dying. Based on the experimental results on 496 data from patients who were confirmed positive for COVID-19, KNN predicted the chances of recovery for patients with confirmed COVID-19 with an average accuracy of 88.16%. A prediction system for the chance of recovery for COVID-19 patients is constructed by choosing the best model from five test scenarios based on the given k value. The best model is at a value of k equal to 4, with an accuracy value of 88.8%.