Reviewerly/exHarmony huggingface dataset:
The Language Ecology of Graduate Students: An Exploration through Vocabulary Contribution

This study explores the language ecology of graduate students through vocabulary contribution. Understanding students’ learning ecology may be done through the identification of pertinent sociomaterial networks with which students engage to initiate or complement learning. This study was set in an academic writing module taught by the researcher. An open invitation was extended to his students to contribute any vocabulary they encountered outside of his classroom. Along with the vocabulary contribution, students also had to provide the excerpt where the word occurred and the source-type. Contributions were made on an online Excel file. There was a total of 277 contributions made, of which 259 were unique (229 words and 30 unique strings of words). Students’ contributions of strings of words were not anticipated. A majority of these contributions came from academic sources, such as research articles or book chapters, which may be a pertinent aspect of the graduate students’ language ecology. Through the findings, it is recommended that English for academic purposes (EAP) or English for specific purposes (ESP) instructors identify language ecologies found in the broader university setting to glean relevant pedagogical materials that can support students’ language development.
Partition Oracles from Weak Key Forgeries

In this work, we show how weak key forgeries against polynomial hash based Authenticated Encryption (AE) schemes, such as AES-GCM, can be leveraged to launch partitioning oracle attacks. Partitioning oracle attacks were recently introduced by Len et al. (Usenix'21) as a new class of decryption error oracle which, conceptually, takes a ciphertext as input and outputs whether or not the decryption key belongs to some known subset of keys. Partitioning oracle attacks allow an adversary to query multiple keys simultaneously, leading to practical attacks against low entropy keys (e. g. those derived from passwords).
Design and Research of Industrial Robot Control System Based on Machine Vision

People obtain 80% of external information through vision. Vision is a powerful and effective way for people to obtain information. Therefore, it has been people's dream for many years to make industrial robots have vision. With the advent of computer vision, it has become possible for robots to have vision. Robot vision is to perform image processing from the obtained image information, and feedback information quickly, so that the computer gives control signals. Based on machine vision, this paper studies and analyzes the design of industrial robot control system
Multi-classification of Histopathological Images based on Convolutional Neural Networks

Breast cancer is the main cause of harm to women's health, and pathological examination is the key means of diagnosis of breast cancer, so the accurate classification of histopathological images is of great significance in clinical application. The classification of benign (B), malignant (M) and eight subtypes (adenosis (A), fibroadenoma (F), tubular adenoma (TA), phyllodes tumor (PT), ductal carcinoma (DC), lobular carcinoma (LC), mucinous carcinoma (MC) and papillary carcinoma (PC)) of breast cancer was studied. The convolutional neural network was used to realize the automatic feature learning and automatic classification of images. A preprocessing method suitable for HE staining images and a training strategy for global fine-tuning were proposed. The dataset was expanded by the enhancement technology. Comparison experiments were carried out on two different models, Googlenet and Resnet50, and the results were compared with the existing research results. The results show that the proposed method can produce higher classification accuracy.
Human Action Recognition on Raw Depth Maps

We propose an effective framework for human action recognition on raw depth maps. We leverage a convolutional autoencoder to extract on sequences of deep maps the frame-features that are then fed to a 1D-CNN responsible for embedding action features. A Siamese neural network trained on repre-sentative single depth map for each sequence extracts features, which are then processed by shapelets algorithm to extract action features. These features are then concatenated with features extracted by a BiLSTM with TimeDistributed wrapper. Given the learned individual models on such features we perform a selection of a subset of models. We demonstrate experimentally that on SYSU 3DHOI dataset the proposed algorithm outperforms considerably all recent algorithms including skeleton-based ones.
Efficient Machine Learning-Based Diagnosis System for Breast Cancer

Machine Learning (ML) algorithms find their application in the field of medicine especially in cancer prognosis enabling early diagnosis and treatment. Breast cancer is the predominant form of cancer occurring in women. It is a malignant growth or tumor resulting in uncontrolled division of cells that invades into the adjoining cells and also spreads to different parts of the body. It is the fifth leading cause of cancer mortality. In this paper, diverse ML algorithms are applied to the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are used for feature extraction, while Naive Bayes Classifier (NBC), Random Forest (RF), Neural Networks (NNs) and Support Vector Machine (SVM) are used for classification of data in the WDBC dataset. It is seen that LDA and NNs offer better results in terms of Accuracy, Sensitivity, Specificity and Kappa coefficient.
A GAN-based approach for password guessing

Password is the most widely used authenticate method. Individuals ordinarily have numerous passwords for their documents or devices, and, in some cases, they need to recover them with password guessing tools. Most popular guessing tools require a dictionary of common passwords to check with password hashes. Thus, generative adversarial networks (GANs) are suitable choices to automatically create a high-quality dictionary without any additional information from experts or password structures. One of the successful GAN-based models is the PassGAN. However, existing GAN-based models suffer from the discrete nature of passwords. Therefore, we proposed and evaluated two improvement of the PassGAN model to tackle this problem: the GS-PassGAN model using Gumbel-Softmax relaxation and the S-PassGAN using a smooth representation of a real password obtained by an additional Auto-Encoder. Experiment results on three different popular datasets show that the proposed method is better than the PassGAN both in the standalone and combining cases. Moreover, the matching rate of the proposed method can be increased by more than 5%.
An Improved DeepLab Model for Clothing Image Segmentation

Image segmentation is an effective method to extract the clothing region from the image, which is especially suitable for the analysis and processing of the clothing image with the complex background. At present, the research of image segmentation mainly focuses on the field of deep learning, and image segmentation methods such as DeepLab sequence based on convolutional neural network have been widely used. However, their segmentation results are not good enough when there are the complex deformation and edges in the clothing images. In order to improve the performance of clothing image segmentation, an improved DeepLab model for clothing image segmentation is developed in this paper. Based on the DeepLabV3+ model, the receptive field module and the decoder are redesigned in the new model. For the receptive field module, the ASPP (Atrous Spatial Pyramid Pooling) is changed to an improved RFBs (Receptive Field Block), which performs much better in simulating the human visual perception. For the decoder, the interpolation upsampling is replaced with a transpose convolution one due to it’s deformation adaptability to the edges and corners in the images; the concatenations between the high-level and the low-level features are increased from two-stage to five-stage in order to obtain more low-level features. After training and testing on deepfashion2 dataset, the improved model achieved performance of 97.26% Accuracy, 93.23% mIoU, 90.56% AP75 and 44.80% AP95 which is significantly better compared with DeepLabv3+. It takes 93.806 ms for the improved DeepLab model to complete the inference of one image, which is only slightly slower than that (92. 09S ms) for DeepLabV3+. The improved DeepLab model has a stronger ability to obtain information such as clothing edges, which makes the performance of segmentation better.
An agent-based approach to predicting lymph node metastasis status in breast cancer

We present a flexible, multi-agent approach to predictive classification problems which uses simple, modular agents that interact and share information socially in an arena with a variable number of participants. Opinion aggregation is accomplished using a honey-bee-derived optimization algorithm that improves accuracy and reduces variance compared with existing weighted and unweighted voter mechanisms. Confidence metrics may be derived from the agent interactions. We apply our system to a data set of 483 de-identified breast cancer patients to predict node-positive or node-negative disease with over 78.5% accuracy in general. When eliminating low-confidence predictions, which leaves 79.5% of patients, classification accuracy improves to 84.5%.
A Multiscale Attention Mechanism Based Vehicle Re-Identification

Vehicle re-identification is effected by the vehicle appearance feature, especially for the illumination variation, line of sight occlusion, and image angle change. To solve this problem, a vehicle feature extraction approach based on multi-scale fusion attention mechanism is proposed. First, ResNet-50 is used as backbone network to strengthen the extraction of more discriminative local feature. Second, a plug-and-play spatial attention module is inserted into the backbone network, allowing the backbone network to pay more attention to the local information, which makes the final extracted vehicle features more discriminative. Experimental results on the VeRi-776 dataset indicate that the proposed approach achieved the highest Rank-1 index of 94.28% with mAP of 78.52%, which outperforms the other compared spatial attention mechanism based methods.
An introduction of Python ecosystem for deep learning

Known as a high-level programming language, Python is easy to use and learn. It also works quickly; furthermore, it is very versatile and may become the dominant platform for machine learning. If you would like to set up a career in deep learning, you need to know Python programming along with the Python ecosystem. Creating effective deep learning models would require a basic understanding of the Python ecosystem, e.g., NumPy, Pandas, Matplotlib, and Scikit-learn libraries. This chapter introduces various Python libraries and examples that are very useful to develop deep learning applications.
Storyspace

Storyspace, introduced at the first ACM Workshop on Hypertext in 1987, was developed as a hypertext system for exploring creative writing and for writing instruction. Uniquely among systems of that era, Storyspace continues in use today, and Storyspace documents remain conveniently accessible. The technical and theoretical forces that shaped Storyspace continue to be interesting today.
Intelligent Answer System Based on Vulnerability Knowledge Graph

Analyzing security vulnerabilities is of great significance to network protection. Although existing CVE and other databases contain rich vulnerability information, they are poorly readable and difficult to express the correlation between vulnerabilities and vulnerabilities, and between vulnerabilities and affected entities. In addition, the existing database does not support natural language retrieval, and non-professionals have certain difficulties in retrieval. Therefore, a security vulnerability ontology is designed, and then a knowledge graph is constructed; intent identification and slot filling of natural language questions are performed to complete semantic analysis; on this basis, an intelligent question answering system based on the vulnerability knowledge graph is designed and implemented. Experiments have proved that the system can simplify the user’s process of vulnerability information retrieval and query, and is convenient for mining the potential value of vulnerability database information.
Succinct Publicly-Certifiable Proofs

We study zero-knowledge arguments where proofs are: of knowledge, short, publicly-verifiable and produced without interaction. While zkSNARKs satisfy these requirements, we build such proofs in a constrained theoretical setting: in the standard-model—i.e., without a random oracle—and without assuming public-verifiable SNARKs (or even NIZKs, for some of our constructions) or primitives currently known to imply them.We model and construct a new primitive, SPuC (Succinct Publicly-Certifiable System), where: a party can prove knowledge of a witness \({\mathsf w}\) by publishing a proof \(\pi _0\); the latter can then be certified non-interactively by a committee sharing a secret; any party in the system can now verify the proof through its certificates; the total communication complexity should be sublinear in \(|{\mathsf w}|\). We construct SPuCs generally from (leveled) FHE, homomorphic signatures and linear-only encryption, all instantiatable from lattices and thus plausibly quantum-resistant. We also construct them in the two-party case replacing FHE with the simpler primitive of homomorphic secret-sharing.Our model has practical applications in blockchains and in other protocols where there exist committees sharing a secret and it is necessary for parties to prove knowledge of a solution to some puzzle. Our constructions can be seen as a way to compile a designated-verifier SNARK into a proof system with a flavor of public-verifiability with similar efficiency features of the starting dvSNARK (e.g., proving time).We show that one can construct a version of SPuCs with robust proactive security from similar assumptions. In a proactively secure model the committee reshares its secret from time to time. Such a model is robust if the committee members can prove they performed this resharing step correctly. Along the way to our goal we define and build Proactive Universal Thresholdizers, a proactive version of the Universal Thresholdizer defined in Boneh et al. [Crypto 2018].
Design and Implementation of Authenticated Encryption Co-Processors for Satellite Hardware Security

FPGA implementation is attained through either the traditional Register Transfer Level (RTL) flow or High-Level Synthesis (HLS) flow. The Consultative Committee for Space Data Systems (CCSDS) has recommended a standard for security algorithms for space missions. Authenticated encryption, the most important of those algorithms, can be achieved by either cipher-based or hash-based algorithms. In this paper, firstly, a brief explanation of the CCSDS standard authenticated encryption algorithms of both types is provided. Secondly, the algorithms are implemented in both RTL and HLS flows to measure and quantify the gap between the two design flows. Results show that the HLS modules utilize 44% more LUTs and consume an average of 40.8% more power than the RTL ones. In addition, the RTL modules demonstrated 28 times higher throughput than that of the HLS ones. Therefore, it is recommended to use the traditional RTL approach over the HLS one and the cipher-based module over the hash-based one at the expense of longer time-to-market for the RTL design. Additionally, the cipher-based module when compared to the hash-based one has proven higher efficiency utilizing 12% less area, achieving 35% higher throughput, and consuming 17% less energy per bit.
Representation without Elections: Civil Society Participation as a Remedy for the Democratic Deficits of Online Speech Governance

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Research on Video Quality Evaluation of Sparring Motion Based on BPNN Perception

The quality of boxing video is affected by many factors. For example, it needs to be compressed and encoded before transmission. In the process of transmission, it will encounter network conditions such as packet loss and jitter, which will affect the video quality. Combined with the proposed nine characteristic parameters affecting video quality, this paper proposes an architecture of video quality evaluation system. Aiming at the compression damage and transmission damage of leisure sports video, a video quality evaluation algorithm based on BP neural network (BPNN) is proposed. A specific Wushu video quality evaluation algorithm system is implemented. The system takes the result of feature engineering of 9 feature parameters of boxing video as the input and the subjective quality score of video as the training output. The mapping relationship is established by BPNN algorithm, and the objective evaluation quality of boxing video is finally obtained. The results show that using the neural network analysis model, the characteristic parameters of compression damage and transmission damage used in this paper can get better evaluation results. Compared with the comparison algorithm, the accuracy of the video quality evaluation method proposed in this paper has been greatly improved. The subjective characteristics of users are evaluated quantitatively and added to the objective video quality evaluation model in this paper, so as to make the video evaluation more accurate and closer to users.
Satellite Data Transmission Method for Deep Learning-Based AutoEncoders

To improve the accuracy of satellite data transmission, deep learning (DL) are applied to the Ka-band satellite communication system under complex channel conditions, and a satellite data transmission method for deep learning-based auto encoder (AE) is proposed in this paper. Specifically, the transmitter and receiver are integrated with deep neural network (DNN), and an interference layer is used to simulate the complex channels that may occur during the satellite data transmission. Finally, we trained the entire network iteratively. And optimized the entire network performance. The result exposed that the proposed method achieve an order of magnitude higher than traditional BPSK modulation, coherent demodulation, Hamming code (HC) and hard-decision decoding method, and two orders of magnitude higher than Hamming code and maximum likelihood estimation (MLE) methods. It has been verified that the new network structure of the AE can improve the data transmission accuracy, thus providing new avenues for promoting the application of deep learning in wireless transmission, as well as new ideas for satellite data transmission.
End-to-End Deep Sketch-to-Photo Matching Enforcing Realistic Photo Generation

The traditional task of locating suspects using forensic sketches posted on public spaces, news, and social media can be a difficult task. Recent methods that use computer vision to improve this process present limitations, as they either do not use end-to-end networks for sketch recognition in police databases (which generally improve performance) or/and do not offer a photo-realistic representation of the sketch that could be used as alternative if the automatic matching process fails. This paper proposes a method that combines these two properties, using a conditional generative adversarial network (cGAN) and a pre-trained face recognition network that are jointly optimised as an end-to-end model. While the model can identify a short list of potential suspects in a given database, the cGAN offers an intermediate realistic face representation to support an alternative manual matching process. Evaluation on sketch-photo pairs from the CUFS, CUFSF and CelebA databases reveal the proposed method outperforms the state-of-the-art in most tasks, and that forcing an intermediate photo-realistic representation only results in a small performance decrease.
Object Interaction Recommendation with Multi-Modal Attention-based Hierarchical Graph Neural Network

Object interaction recommendation from Internet of Things (IoT) is a crucial basis for IoT related applications. While many efforts are devoted to suggesting object for interaction, the majority of models rigidly infer relationships from human social network, overlook the neighbor information in their own object social network and the correlation of multiple heterogeneous features, and ignore multi-scale structure of the network. To tackle the above challenges, this work focuses on object social network, formulates object interaction recommendation as multi-modals object ranking, and proposes Multi-Modal Attention-based Hierarchical Graph Neural Network (MM-AHGNN), that describes object with multiple knowledge of actions and pairwise interaction feature, encodes heterogeneous actions with multi-modal encoder, integrates neighbor information and fuses correlative multi-modal feature by intra-modal hybrid-attention graph convolution and inter-modal transformer encoder, and employs multi-modal multi-scale encoder to integrate multi-level information, for suggesting object interaction more flexibly. With extensive experiments on real-world datasets, we prove that MMAHGNN achieves better recommendation results (improve 3-4% HR@3 and 4-5% NDCG@3) than the most advanced baseline. To our knowledge, our MM-AHGNN is the first research in GNN design for object interaction recommendation. Source codes are available at: https://github.com/gaosaroma/MM-AHGNN.
Application of Artificial Neural Network (ANN) and Support Vector Machine (SVM) for Government Policy Recommendations to Allow People to Do Activities Without Masks

The Covid-19 pandemic that has hit the world, including Indonesia, has affected various aspects of life. This has also led to various new policies by the Indonesian government to limit the spread of Covid-19, one of which is the use of masks while on the move. This study aims to apply machine learning to government policy recommendations in allowing people to move without masks. The method used in this research is SVM (Support Vector Machine) for classification and ANN (Artificial Neural Network) for forecasting. The results of this study are MSE (Mean Squared Error) of 4.62 and MAE (Mean Absolute Error) of 0.97, and this study predicts the Covid-19 pandemic will decrease in February 2022 but will tend to rise again in March 2022. Next, in April 2022, cases will decrease. Based on the study results, the implementation of the policy can be carried out in April 2022 regarding community activities without masks.
Message Concealing in Vector Images

Various steganography techniques are used to conceal information. Typically, information is hidden in images, audio and video files, text documents, etc. We are considering vector images consisting of various mathematical objects (points, lines, curves of first and second-order curves, the Bezier curves, nodes, tangents, control points, etc.). The techniques of information hiding change those mathematical objects, for instance, in the way of encoding the basic points' coordinates. In the article, two methods (bit and pattern) are considered and being experimentally tested in order to track the possible destruction of embedded data. To do this, affine transformations are used and our experiments consist of repeated attempts to destroy information due to the use of various affine transformations with different parameters. The results demonstrate that vector images can indeed be used to conceal information, but the resistance against certain affine attacks is not always high.