Reviewerly/exHarmony huggingface dataset:
Hashing Fake: Producing Adversarial Perturbation for Online Privacy Protection Against Automatic Retrieval Models

The wide application of deep neural networks (DNNs) has significantly improved the performance of hashing models on multimodal retrieval issues. DNN-based deep models can automatically learn semantic features from raw data to make human-level decisions. However, the superior generalization leads to potential privacy leakage risks. Strong DNN-based retrieval models enable malicious crawlers to search for nontag private information based on semantic similarity matching. Hence, executing effective privacy protection mechanisms against those retrieval software is essential for reliable social website construction. In this article, we propose a retrieval task-based adversarial perturbation generation method called Hashing Fake to meet this request. Specifically, DNNs are recently found to be vulnerable to a specific set of attacks called adversarial perturbations, which denote some magnitude-restricted signals added on objective samples to misguide well-crafted DNN models, and perturbations’ magnitudes are small enough that will not induce humans’ perception. Moreover, since existing adversarial perturbation generation methods are designed for supervised tasks, Hashing Fake constructs a differential approximation substitution for perturbation production on unsupervised retrieval tasks. Through extensive experiments on several deep retrieval benchmarks, we demonstrate that well-crafted perturbations using Hashing Fake can effectively misguide objective models’ recognitions to make false predictions. The added norm-restricted perturbations on objective samples will not alter humans’ perception; hence, Hashing Fake can be applied on real-world social websites to protect subscribers’ privacy against malicious retrieval software.
Improving the Accuracy of Homography Matrix Estimation for Disturbance Images Using Wavelet Integrated CNN

The objective of this study is to improve the robustness of homography estimation using deep learning for images superimposed with various types of disturbances. In conventional deep learning homography estimation, the original image and the image with perturbations and disturbances are simultaneously input to the model for estimation. The disadvantages of this approach are that the original image is affected by noise and the model itself is unclear. In this study, features are extracted separately for each of the two images, and a model is constructed based on ResNet using these features as input. In addition, when extracting the features of the perturbed and disturbed images, WaveCNet, which integrates the discrete wavelet transform into the CNN, is used to add pinpoint tolerance to the disturbance. The estimation accuracy of the homography matrix in the method proposed in this study shows improved accuracy in various noises. These results suggest that the proposed method is effective in reducing the effect of disturbance by extracting features robust to disturbance for each image.
Stealth: A Heterogeneous Covert Access Channel for Mix-net

The anonymous communication network based on the Mix-net model can protect the communication privacy by hiding the communication relationship between users. However, most anonymous communication networks lack the entry hiding mechanism for Mix-net. This paper proposes an anonymous network ingress protection mechanism, which provides a covert data transmission channel between end users and Mix-net nodes based on various Internet public services. In this paper, a covert transmission channel is implemented in the Internet environment based on three heterogeneous protocols, WebDav, Git, and SMTP. The experimental results show that this method can meet the performance requirements of data transmission while maintaining high privacy.
CODE-SWITCHING IN ENGLISH TEACHING PROCESS: A STUDY OF LECTURERS’ UTTERANCE AT DEPARTMENT OF ENGLISH EDUCATION

Code-mixing or code-switching in English teaching and learning used in EFL classroom, especially to enhance understanding skill in English. Code-mixing and code-switching in teaching process will make language learning more meaningful and effective (Liu, 2008). Most studies of code-mixing and code-switching easily found in oral communication, such as in daily communication or formal situation. A lot of research on code-mixing and code-switching were not mention the specific process of those codes in learning process (Kim, 2006; Marzona, 2009; Saputro, 2013; Siregar and Sumarsih, 2014; Ansar, 2017; Oladosu, 2011; Mustain, 2011; Siskawati, 2021). The other gaps are most of the research sites were in junior high school and senior high school and others from television, newspaper, magazine, television program, etc. Then other different is they used teacher, students and group of society as the sample of research. In this research, lecturer of Department of English Education became sample of the research and university as site of the research. Objective of the research is to find out kind of code-switching uttered by the lecturer in teaching process at Department of English Education and the factors influencing code-switching. Method used in this research is descriptive qualitative. The subjects of the research are two lecturers of Department of English Education. The instrument consists of documentation of descriptive observation (by joining class group platform) and list of interviews on August 2021. In analyzing the data, the researcher observed the learning process through online platform, recorded the lecturer utterance, transcribed the recording, classified the data into kinds of code-switching used by the lecturer based on Romaine’s theory (1995), then calculated the percentage of each kind of code-switching. The researcher analyzed the factor why the lecturer used code-switching in teaching process based on the recording of interview and draw a conclusion by using Holme’s theory (1992). The result showed there are two types of code-switching used by the lecturer, namely inter-sentential switching (57,5%), intrasentential switching (42,5%), and there was not tag-switching. The data of interview showed the reason of lecturer used code-switching in teaching process because of the participant, setting or social context, topic, and function.
Overview of 3GPP standardization for 5G AR/MR experiences using glasses-type devices

Augmented Reality (AR) and Mixed Reality (MR) experiences are emerging as essential services of 5G systems, and AR/MR glasses, as a new device form-factor, are widely known as the most suitable device to realize the full range of related AR/MR services. While many Consumer Electronics (CE) manufacturers are developing AR/MR glasses devices for release in the near future, it is important to support interoperability between devices and to ensure the Quality of Service (QoS) of AR/MR services when these devices are connected to 5G systems. Recently, the 3rd Generation Partnership Project (3GPP) has published a new Release-17 specification TR 26.998, providing information on the functional structures of AR/MR glasses, core use cases, potential media exchange formats, and Key Performance Indicators (KPI) metrics. In this paper, the context of the technical report is briefly introduced, together with a description of the relevant ongoing works in the 3GPP SA4 working group for next Release-18 specifications.
Ordinal Inverse Reinforcement Learning Applied to Robot Learning with Small Data

Over the last decade, the ability to teach actions to robots in a user-friendly way has gained relevance, and a practical way of teaching robots a new task is to use Inverse Reinforcement Learning (IRL). In IRL, an expert teacher shows the robot a desired behaviour and an agent builds a model of the reward. The agent can also infer a policy that performs in an optimal way within the limitations of the knowledge provided to it. However, most IRL approaches assume an (almost) optimal performance of the teaching agent, which might become unpractical if the teacher is not actually an expert. In addition, most IRL focus on discrete state-action spaces that limit their applicability to certain real-world problems such as within the context of direct Policy Search (PS) reinforcement learning. Therefore, in this paper we introduce Ordinal Inverse Reinforcement Learning (OrdIRL) for continuous state variables, in which the teacher can qualitatively evaluate robot performance by selecting one among the predefined performance levels (e.g. {bad, medium, good} for three tiers of performance). Once the OrdIRL has fit an ordinal distribution to the data, we propose to use Bayesian Optimization (BO) to either gain knowledge on the inferred model (exploration) or find a policy or action that maximizes the expected reward given the prior knowledge on the reward (exploitation). In the case of large-dimensional state-action spaces, we use Dimensionality Reduction (DR) techniques and perform the BO in the latent space. Experimental results on simulation and with a robot arm show how this approach allows for learning the reward function with small data.
Detection Method of User Behavior Transition on Computer

IT general controls in a company are essential, and improper controls pose a significant risk to the company. One of those appropriate controls is the management and verification of daily logs. Companies utilize text-based logs (e.g., keystroke and application information) to monitor user behavior. However, some systems may only record PC screenshot image logs to prioritize stable operation. Therefore, we focus on PC screenshot image logs. Previous studies have attempted to analyze screenshot images as a classification problem or semi-supervised clustering. However, the number of tasks is indefinite, and the creation of training data and daily log checks is very costly. This paper proposes an efficient method for checking logs: detecting user action transitions using screenshot images. The proposed method detects user behavior transitions by grouping image and text features obtained from screenshot images based on their similarity without learning or labeling. We show that the proposed method can detect user behavior transitions with a reproduction rate of over 98% and reduces the total number of logs checked by auditors to about 1/4.
BFNet: A Lightweight Network Designed for Pedestrian Detection

Many advanced pedestrian detection models have achieved incredible results. However, these high-accuracy models have huge network scales and heavy inference computation loads, which may lead to the failure in high real-time industrial fields. In this paper, we propose a Branch Fusion Network (BFNet) to reduce inference computations and improve the inference speed with the high detection accuracy. The inference computations of most pedestrian detection models mainly concentrate upon the feature extraction stage. The proposed BFNet shunts the features in the feature extraction stage on channel dimension through the branch structure. Only part of the features passes through the original model branch with heavy computation loads, while the rest of the features pass through the lightweight branch. Moreover, multi-branch features inside BFNet are fused to make feature information complementary and enhance the learning ability of the network. The proposed networks focus on both real-time performance and detection accuracy. The experimental results on pedestrian datasets indicate that the proposed two-branch BFNet sacrifices only a small amount of detection accuracy but reduces the computations by 46.3%.
Current Software Defect Prediction: A Systematic Review

detecting defect in a software product prior to testing reduces the cost of testing and improve the quality of the software product. Various methods for enhancing the accuracy of defect prediction model have been published. The goal of this review is to identify and analyze the dataset, models, framework and the performance of software defect prediction model. The IEEExplore, Science Direct, Scopus, and Google Scholar databases were used to search and download the relevant papers. Sixty-eight (68) papers published between 2017 to 2021 were selected based on exclusion and inclusion criteria. Analysis of the selected studies revealed that 100% of the selected articles used the publicly available dataset from NASA, PROMISE and others. The most frequently used models in software defect prediction studies were identified. The analysis also revealed IEEE Transactions on Software Engineering Journal is the most significant journal with respect to software defect prediction studies using Scimago Journal Ranking as criteria. The review also identified studies on enhancing the predictive performance of defect prediction models. Software defect prediction is still active and sound. Thus, needs more research especially on enhancement methods.
Efficient Reduction Algorithms for Special Gaussian Integer Moduli

Gaussian integers are a subset of the complex numbers with integers as real and imaginary parts. When Gaussian integers are equipped with modulo operations, they form Gaussian integer rings or fields, depending on the specific choice of the modulus. Arithmetic on Gaussian integers can offer advantages in terms of operand size and improved parallelism, due to independent calculation of the real and imaginary parts. However, although Gaussian integer modulo reduction is the fundamental operation to enable computations in finite Gaussian integer rings and fields, efficient algorithms for Gaussian integer modulo reduction have not been widely investigated so far. In this work, we fill this gap and present efficient reduction algorithms for Gaussian integer moduli of special forms. Indeed, we demonstrate that there exist different classes of Gaussian integer moduli allowing for very fast reductions. Finally, we show that the computational complexity of the proposed algorithm is significantly reduced compared with generic Gaussian integer reduction methods known to date, e.g., Montgomery-based reduction for Gaussian integers.
A New Evaluation Strategy to Open World Target Recognition

With the increasing requirements for target classification capabilities in an open scene, various open set recognition algorithms emerge in an endless stream. It becomes urgent to evaluate the recognition performance. However, very few studies have examined this crucial and challenging problem previously. To fill the gap, a new evaluation strategy is proposed in this paper. First, two measurements, Openness and openness based on the number of instances per unknown class are presented to quantify the degree of open scenario. The recognition performance is then evaluated by this unified evaluation criteria across several different perspectives, the number of classes, the number of instances, and the synthesis difficulty, on which the ability of algorithms to classify the known classes and identify the unknown classes can be analyzed quantitatively. Multiple experiments are performed on the real dataset. The experimental results demonstrate the effectiveness of the proposed evaluation strategy.
Entity-Aware Social Media Reading Comprehension

Social media reading comprehension (SMRC) aims to answer specific questions conditioned on short social media messages, such as tweets. Sophisticated neural networks and pretrained language models have been successfully leveraged in SMRC, accompanying with a series of deliberately-designed data cleaning strategies. However, the existing SMRC techniques still suffer from unawareness of various entity mentions, i.e., the successive tokens (words, sub-words or characters) that fully or briefly describe named entities, such as abbreviated person names. This unavoidably brings negative effects into question answering towards the questions of “who”, “where”, “which organization”, etc. To address the issue, we propose to enhance the capacity of a SMRC model in recognizing entity mentions and, more importantly, construct an entity-aware encoder to incorporate latent information of entities into the understanding of questions and tweets. In order to obtain a self-contained entity-aware encoder, we build a two-channel encoder-shareable neural network for multitask learning. The encoder is driven to produce distributed representations that not only facilitate decoding of entity mentions but prediction of answers. In our experiments, we employ 12-layer transformer encoders for multi-task learning. Experiments on the benchmark dataset TweetQA show that our method achieves significant improvements. It is also proven that our method outperforms the state-of-the-art model NUT-RC, yielding improvements of 2.5% BLEU-1, 3% Meteor and 2.2% Rouge-L, respectively.
Base on Long Short-term Memory Network for Fatigue Detection

This paper focuses on a real-time fatigue detection flow. The system will be doing this all inside of python and building it up step by step to be able to detect a bunch of different poses and specifically signs of drowsiness. In order to do that we use a few key models and using media pipe holistic to be able to extract key points. This is going to allow us to extract key points from our face. The system uses tensorflow and keras and builds up a long short-term memory(LSTM) model to be able to predict the action which is being shown on the screen. We need to do is collect a bunch of data on all of our different key points, so we collect data on our face and save those as numpy arrays. The face detection method is based on a deep neural network using LSTM layers to go on ahead and predict that temporal component, which be able to predict action from a number of frames not just a single frame. Integrate using opencv and then proceed to make real-time predictions using the webcam.
MonoPLFlowNet: Permutohedral Lattice FlowNet for Real-Scale 3D Scene Flow Estimation with Monocular Images

Real-scale scene flow estimation has become increasingly important for 3D computer vision. Some works successfully estimate real-scale 3D scene flow with LiDAR. However, these ubiquitous and expensive sensors are still unlikely to be equipped widely for real application. Other works use monocular images to estimate scene flow, but their scene flow estimations are normalized with scale ambiguity, where additional depth or point cloud ground truth are required to recover the real scale. Even though they perform well in 2D, these works do not provide accurate and reliable 3D estimates. We present a deep learning architecture on permutohedral lattice - MonoPLFlowNet. Different from all previous works, our MonoPLFlowNet is the first work where only two consecutive monocular images are used as input, while both depth and 3D scene flow are estimated in real scale. Our real-scale scene flow estimation outperforms all state-of-the-art monocular-image based works recovered to real scale by ground truth, and is comparable to LiDAR approaches. As a by-product, our real-scale depth estimation is also comparable to other state-of-the-art works.
A Study of Answer Selection Task Based on Deep Learning Methods

Answer selection task is an important task in question answering systems. In this work, we propose several deep learning methods to address answer selection task. Current answer selection tasks use LSTM networks to learn the contextual information of query and candidate answer sequences, but the LSTM network suffers from the problem of gradient instability and fail to extract local information. Aiming to solve these problems, we first introduce fusion layer with residual ideas to alleviate gradient instability. Then we further introduce CNN networks to capture local n-gram information. In addition, we introduce one-way and two-way attention mechanism respectively, in order to capture the interaction between query and candidate answer, and further improve model performance. Experimental results of two public datasets InsuranceQA and WikiQA show that our methods outperform baseline methods, which conclude the effectiveness of our methods proposed.
VCL-GAN: A Variational Contrastive Learning Generative Adversarial Network for Image Synthesis

Generative Adversarial Networks (GANs) have worked well for image generation, but recent works have shown that their generated images lack diversity. In response to this problem, we propose an image generation network based on contrastive learning (CL) and Autoencoder (AE). Firstly, we try to let the network learn to extract different types of features by Siamese network (SN), which is a classic of contrastive learning. Secondly, we perform variational processing on the resulting codes, so that the GANs can generate high-quality images of multiple types by the codes. Finally, extensive experiments and comparisons with the state-of-the-art are conducted on the Tsinghua dog dataset that the method has made progress in generating realistic images of different types.
A Security and Privacy of Cloud-Assisted Healthcare System

Healthcare System is an essential asset in the lives of people worldwide. The advances in digital communication technology have significantly impacted the healthcare business during the past several years. Digital communication technology enables integrated healthcare systems, allowing healthcare participants to efficiently arrange their operations, tailored and preventative healthcare to people, and increase design and operational efficiency. Cloud computing is a rapidly developing new technology incorporated into the healthcare system. According to the results of a recent study of all healthcare companies, cloud computing is necessary to store patient protected health information (PHI) and aid in emergencies utilizing a cloud-based virtual server. This technology not only helps to minimize the expenses connected with healthcare, but it also helps to save lives by providing a quick diagnosis. When exchanging sensitive information, various privacy problems occur, which must be addressed. This study aims to discover multiple schemes and taxonomies related to the current ways of cloud technology employed in Electronic healthcare systems.
Low-discrepancy Sampling for Full Reference Image Quality Assessment Speed Up

Image quality assessment (IQA) aims to predict the image quality perceived by the human visual system (HVS). Full Reference (FR) image quality assessment is an objective algorithm requiring information about the reference image for quality assessment. Consequently, the FR algorithms may need a high number of operations to complete the evaluation. Another relevant point is that IQA based on Convolutional Neural Networks (CNN) requires a long training. Considering the high computational cost of FR assessment, we propose to use sampling methods as an alternative to the conventional IQA. First, we apply Van der Corput-Halton, Sobol, and uniform sampling methods to obtain a small representation of the images. Afterwards, we evaluate the sampled image using the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Deep Image Quality Measure for FR (DIQaM-FR) metrics. The experimental results reveal that 7.8% of image pixels of the Live database are sufficient to obtain approximate values of SSIM and low mean error of PSNR. The sampling blocks used in the training of DIQaM-FR demonstrate to be adequate for training the model showing a correlation of 0.968 for SROCC applied on the Live database and a considerably lower training time.