Reviewerly/exHarmony huggingface dataset:
Assessment of Hidden Channel Attacks: Targetting Modbus/TCP

Recent findings in research on malware threats indicate an increasing use of information hiding techniques as a novel approach for compromising IT-Systems by using covert functions and hidden channels. Especially in the context of covert intrusion and data exfiltration, networks of Industrial Control Systems (ICS) are a valuable target for information hiding based attacks. In this paper we discuss how 18 known patterns of information hiding in networks can be applied to protocols found in ICS networks and demonstrate how information can be covertly embedded and retrieved at the example of Modbus/TCP to achieve an overall protocol-compliance by also studying the embedding capacity. Additionally we provide a first tendency of warden- compliance, if the warden has a suspicion that there is a hidden message (conspicuousness). For a practical analysis we introduce an evaluation framework based on open-source software enabling assessment and quick implementation of hidden channels in common protocols found in ICS networks. In combination with a pattern-based approach for the identification of hidden channels, we show how this framework can be utilized as a systematic approach to identify and evaluate plausible hidden channels in industrial communication protocols. From the 14 identified patterns, in this paper we use the introduced framework to implement and evaluate two exemplary timing and storage channels in Modbus/TCP. Our results show 14 protocol-compliant patterns of information hiding based attacks in the context of Industrial Control Systems as well as the necessity of more research in this particular field, especially in terms of further plausible combination of pattern, warden-compliance, detection and mitigation strategies.
A secure and efficient public auditing system of cloud storage based on BLS signature and automatic blocker protocol

Cloud computing is a model that enables users to store their data remotely and enjoy the applications and services provided by this model on demand through a common set of configurable computing resources, without the burden of local data storage as well as maintenance. However, users no longer have physical possession of external data, and this makes the task of protecting data integrity in cloud computing extremely important so that users can use the cloud storage as if it were local, without worrying about the need to verify its integrity. Hence, public cloud storage auditing is critical, so users can use a Third-party auditor (TPA) to verify data integrity. In order to provide effective TPA security, the audit should not achieves any security vulnerabilities to the privacy of user data and does not provide any additional online burden to the user. In this paper, we propose an effective public auditing system for cloud data based on the signature of Boneh-Lynn-Shacham (BLS), in order to ensure public auditing and maintain data privacy. The proposed system also realizes batch auditing and data dynamic process. In addition, the proposed system enhances the level of security authentication through an Automatic Blocker Protocol (ABP) to protect the system from unauthorized TPA. An extensive security and performance analysis shows that the proposed system is very secure and effective
MonodepthPlus: self-supervised monocular depth estimation using soft-attention and learnable outlier-masking

Self-supervised learning of depth from monocular videos has recently drawn attention as it has notable advantages over supervised ones in a training framework. We propose a self-supervised monocular depth estimation method with a set of improvements, which together result in both quantitatively and qualitatively improved depth maps compared to competing self-supervised methods. Our architecture amends current deep convolutional neural network backbone combined with attention mechanism to boost depth estimation performance. Additionally, for addressing moving objects and occlusion, we propose a learnable outlier-masking technique to exclude invalid pixels in photometric error map. Extensive experiments show the effectiveness of the proposed improvements. Our proposed model achieves state-of-the-art performance on KITTI dataset compared with other competing methods.
Verifiable Blockchain-Based Searchable Encryption with forward and backward privacy

Symmetric Searchable Encryption (SSE) is an ideal technology to secure searches. SSE is dedicated to improving data confidentiality without sacrificing the searchability of data. Currently, most SSE schemes work using a semihonest or curious cloud server model in which the search results are not absolutely trustworthy. Therefore, a verifiable SSE (VSSE) scheme is proposed to achieve data integrity verification. Although there are some solutions to the result verification problem, most of the schemes concentrate on a static environment and do not consider the issue of result verification in a dynamic environment. Therefore, this paper proposes a Verifiable Blockchain-Based Searchable Encryption with forward and backward privacy scheme (VBSEFB). First, VBSEFB supports local verification of the data returned by the cloud server in a dynamic environment. Second, our solution perfectly realizes forward and backward privacy and better guarantees data security than other methods. Finally, under the one-to-many model, the data owner's access control to data users is realized. A large number of experiments and evaluations prove the practicality of dynamically updating encrypted data.
A Modification-Free Steganography Algorithm Based on Image Classification and CNN

In order to improve the data-embedding capacity of modification-free steganography algorithm, scholars have done a lot of research work to meet practical demands. By researching the user's behavioral habits of several social platforms, a semi-structured modification-free steganography algorithm is introduced in the paper. By constructing the mapping relationship between small icons and binary numbers, the idea of image stitching is utilized, and small icons are stitched together according to the behavioral habits of people's social platforms to implement the graphical representation of secret messages. The convolutional neural network (CNN) has been used to train the small icon recognition and classification data set in the algorithm. In order to improve the robustness of the algorithm, the icons processed by various attack methods are introduced as interference samples in the training set. The experimental results show that the algorithm has good anti-attack ability, and the hiding capacity can be improved, which can be used in the covert communication.
Project-based-learning on critical reading course to enhance critical thinking skills

In some cases, EFL (English as a Foreign Language) students may be lack critical thinking in reading, particularly in Indonesia. The purposes of this study were to investigate the implementation of Project-Based-Learning (PjBL) on critical reading to enhance critical thinking skills and describe the students’ responses on the implementation of PjBL in critical reading to enhance critical thinking skills. This research employed a mixed-method approach. The subjects of this research were 26 EFL students at STKIP PGRI Sidoarjo, Indonesia. The data were obtained by using observation, interviews, and questionnaires. Data analysis techniques included data reduction, data presentation, and data conclusion. This study produced several results. Firstly, the implementation of PjBL on critical reading course to enhance critical thinking skills consisted of three stages: 1) planning, 2) implementing, and 3) evaluating. Secondly, based on the interviews, 88.89% of the students were motivated in working on the assignments by using the PjBL model, and 54% of them were excellent. Thirdly, based on the questionnaire, most students (73%) chose “strongly agree” with critical thinking skills. It is hoped that the findings of this research can give information about students’ critical thinking and give an appropriate learning model to enhance the students’ critical thinking.
Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings

Current meta-learning approaches focus on learning functional representations of relationships between variables, i.e. estimating conditional expectations in regression. In many applications, however, the conditional distributions cannot be meaningfully summarized solely by expectation (due to e.g. multimodality). We introduce a novel technique for meta-learning conditional densities, which combines neural representation and noise contrastive estimation together with well-established literature in conditional mean embeddings into reproducing kernel Hilbert spaces. The method shows significant improvements over standard density estimation methods on synthetic and real-world data, by leveraging shared representations across multiple conditional density estimation tasks.
The product property of the almost fixed point property for digital spaces

<abstract> Consider two digital spaces $ (X_i, k_i), i \in \{1, 2\} $, (in the sense of <italic>Rosenfeld model</italic>) satisfying the almost fixed point property(<italic>AFPP</italic> for brevity). Then, the problem of whether the <italic>AFPP</italic> for the digital spaces is, or is not necessarily invariant under Cartesian products plays an important role in digital topology, which remains open. Given a Cartesian product $ (X_1 \times X_2, k) $ with a certain $ k $-adjacency, after proving that the <italic>AFPP</italic> for digital spaces is not necessarily invariant under Cartesian products, the present paper proposes a certain condition of which the <italic>AFPP</italic> for digital spaces holds under Cartesian products. Indeed, we find that the product property of the <italic>AFPP</italic> is strongly related to both the sets $ X_i $ and the $ k_i $-adjacency, $ i \in \{1, 2\} $. Eventually, assume two $ k_i $-connected digital spaces $ (X_i, k_i), i \in \{1, 2\} $, and a digital product $ X_1 \times X_2 $ with a normal $ k $-adjacency such that $ N_k^\star(p, 1) = N_k(p, 1) $ for each point $ p \in X_1 \times X_2 $ (see Remark 4.2(1)). Then we obtain that each of $ (X_i, k_i), i \in \{1, 2\} $, has the <italic>AFPP</italic> if and only if $ (X_1 \times X_2, k) $ has the <italic>AFPP</italic>. </abstract>
Safety-Augmented Operation of Mobile Robots Using Variable Structure Control

The design process and complexity of existing safety controls are heavily determined by the geometrical properties of the environment, which affects the proof of convergence, design scalability, performance robustness, and numerical efficiency of the control. Hence, this brief proposes a variable structure control to isolate the environment’s geometrical complexity from the control structure. A super-twisting algorithm (STA) is used to achieve accurate trajectory tracking and robust safety control. Safety control is designed solely based on distance measurement. First, a nominal safety model for obstacle avoidance is derived, where realistic system constraints are considered. The nominal model is well-suited for safety control design for obstacle avoidance and border patrol with analytically proven stability results. The safety control uses distance measurement to maintain a safe distance by compensating the robot’s angular velocity. Also, a supervisory logic is constructed to unify the stability and safety features, and operational safety and precision tracking are proven under parametric uncertainty. The proposed design is modular with minimal tuning parameters, which reduces the computational burden of the algorithm and improves control scalability. The effectiveness of the proposed method is verified against various case studies.
Learning from Imbalanced Educational Data Using Ensemble Machine Learning Algorithms

Student’s academic performance is one of the most important parameters for evaluating the standard of any institute. It has become a paramount importance for any institute to identify the student at risk of underperforming or failing or even drop out from the course. Machine Learning techniques may be used to develop a model for predicting student’s performance as early as at the time of admission. The task however is challenging as the educational data required to explore for modelling are usually imbalanced. We explore ensemble machine learning techniques namely bagging algorithm like random forest (rf) and boosting algorithms like adaptive boosting (adaboost), stochastic gradient boosting (gbm), extreme gradient boosting (xgbTree) in an attempt to develop a model for predicting the student’s performance of a private university at Meghalaya using three categories of data namely demographic, prior academic record, personality. The collected data are found to be highly imbalanced and also consists of missing values. We employ k-nearest neighbor (knn) data imputation technique to tackle the missing values. The models are developed on the imputed data with 10 fold cross validation technique and are evaluated using precision, specificity, recall, kappa metrics. As the data are imbalanced, we avoid using accuracy as the metrics of evaluating the model and instead use balanced accuracy and F-score. We compare the ensemble technique with single classifier C4.5. The best result is provided by random forest and adaboost with F-score of 66.67%, balanced accuracy of 75%, and accuracy of 96.94%.
Lexical Simplification by Unsupervised Machine Translation

In recent years, simple Japanese has been attracting attention as information transmission for foreigners. Automatic text simplification aims to reduce the complexity of vocabulary and expressions in a sentence while retaining its original meaning. This paper aims at compressing vocabulary, focusing on lexical simplification. Since the construction or expansion of a simplification corpus is very costly, we construct a simplification model by unsupervised learning that does not require a parallel corpus for simplification. We construct a simplification model that does not require a parallel corpus using Unsupervised Statistical Machine Translation. Based on a predetermined vocabulary, a pseudo-corpus for simplification is constructed from a web corpus and we learn the simplification model by the pseudo-corpus. We only need a vocabulary and a plain text corpus to train the simplification model. Moreover, we propose to clean the phrase table by WordNet, which improves the performance in BLEU and SARI metrics. By suppressing distant paraphrasing with WordNet, it became easier to select the correct paraphrase candidate.
A Study on Contact Tracing Apps for Covid-19: Privacy and Security Perspective

To support the manual contact tracing methods of Covid-19, countries and big companies like Apple and Google are busy developing several contact tracing applications. The purpose of digital contact tracing apps is to accelerate existing traditional face to face interview method which can control the pandemic effectively and rapidly. A major concern is whether consumers will be willing to download, install, and use the contact tracing applications because of the debate it created about its main attribute like security, privacy concern, system framework, data processing, location measurement. In this paper we discuss the contact tracing apps and its different architecture, then we analyze the framework in term of security, privacy concern and privacy policy. We reported 47 contact tracing applications which are from 28 countries worldwide, with several others expected to be roll out later. We found that 23 percent of contact tracing apps currently implemented do not provide privacy policy in their documentation. We believe that these comprehensive evaluation and specific suggestions will lead to creation and implementation of solutions towards Covid-19 and support governments and mobile development industries in creating safe and privacy conserving apps for contact tracing solutions.
Adopted image matching techniques for aiding indoor navigation

Abstract In indoor navigation and visual odometry, cameras may be used as an aiding algorithm for inertial navigation. Fast and accurate image matching is an important task used in various applications in computer vision and visual odometry. In this research, the performances of all recently available detection, description, and matching techniques were compared. The detection techniques included HARRIS, GFTT, SIFT, SURF, STAR, FAST, ORB, MSER, Dense, and SimpleBlob. Feature description techniques included SIFT, SURF, ORB, and BRIEF. Finally, image matching techniques included BruteForce, BruteForce-L1, BruteForce-Hamming, BruteForce-HammingLUT, and FlannBased. The comparison was made between different kinds of geometric distortions and deformations, such as scaling, rotation, fish-eye distortion, and shearing. In this research, the lighting conditions and the shadowing effects were not taken into consideration. To perform this task, different types of transformations were manually applied to original images and computed with the matching evaluation parameters, such as the number of detected keypoints in images, the processing time, and the matching accuracies for each algorithm to show which algorithm was the best and most robust against these distortions. This work provided us with a perspective on and contributed to the field of image-based indoor navigation systems to recommend further research. The results showed that the ORB detector, the ORB descriptor, and either the BruteForce-Hamming or the BruteForce-HammingLUT matchers were favored to be used in indoor environments.
Sentiment Analysis to Measure Public Response to Online Education During Coronavirus Pandemic

Corona virus (Covid-19) has infected the world with huge impacts and changes that were not imagined, in large sectors such as the economy and education. The education sector is considered an important sector for all societies due to its desired impact on the future of generations. The increasing and rapid spread and precautionary measures have led to the search for suitable alternatives for the continuation of education to ensure that students receive appropriate education and are not affected scientifically or psychologically in the event of dropping out of education. This study seeks to examine the extent to which the community accepts distance learning as a precaution by employing the sentiment analysis of Twitter's tweets as one of the most popular social media. This method is considered effective due to its ability to access the community's tweets quickly and at a low cost.
Overlapped Vehicle Tracking via Enhancement of Particle Filter with Adaptive Resampling Algorithm

Traffic surveillance and on-road security have elevated the demand of machine vision aided traffic control system. Through the modern video camera technology, vehicle tracking has become a vital approach to assist the on-road traffic systems. In the past, many tracking methods have been developed based on the detail and information extracted from the captured vehicle. However, conventional tracking system need to be improved since the background noises and sudden appear objects will increase the difficulties of continuously tracking the target vehicle. Hence, a particle filter algorithm with adaptive resampling approach has been proposed to overcome the vehicle occlusion problems. In addition, the proposed resampling approach can also be used to solve the common particle degeneracy problem. Experimental results show that the enhanced particle filter equipped with adaptive resampling algorithm is significantly improving the accuracy of the tracking process without compromising the processing time.
Surveillance video motion segmentation based on the progressive spatio‐temporal tunnel flow model

Motion segmentation is the first and important step of surveillance video summarisation, and traditional motion segmentation methods usually process all video data, which seriously affects the real-time performance of video synopsis. To address this issue, a novel method of surveillance video motion segmentation based on the progressive spatio-temporal tunnel (STT) flow model is proposed in this letter. Unlike traditional video segmentation methods, the proposed one only analyses the pixels on the circular sampling lines of video frames. Initially, the circular progressive STT is established by sampling pixels progressively. Subsequently, the progressive STT is expanded to form an STT expansion diagram and the STT expansion diagram is modelled as progressive STT flow. Finally, surveillance video motion fragments are segmented according to the progressive STT flow model. Experimental results demonstrate that the proposed method outperforms the existing state-of-the-art methods in terms of time consumption. This is accomplished with a comparable motion segmentation precision.
Crowd Counting in a Highly Congested Scene using Deep Augmentation Based Convolutional Network

In this Paper, we proposed a unique two-level data augmentation-based approach for crowd counting in a still image. Most of the existing researchers have used deep and shallow two different types of networks for high and low-level feature extraction. We used a single framework for high and low-level feature extraction. In our approach, we first convert our training samples to polar coordinates and use the magnitude of the resultant image and the skeleton of the image to feed directly to the deep convolutional neural network with the original training samples. Most of the crowd datasets have very less amount of training samples and deep learning-based approaches require large amounts of training data. This approach of two-level augmentation helps our deep network to learn high-level features directly from original samples as well as the low-level features from augmented samples. This feature extraction is then followed by a fully connected regress network for local patch count. We used two public datasets to demonstrate our proposed approach, UCF, and Shanghaitech datasets. The experiments show that our deep augmentation-based approach outperforms most of the state of the art methods.
An X-ray Image Enhancement Algorithm for Dangerous Goods in Airport Security Inspection

Aiming at the problem of color distortion in CLAHE enhanced airport security X-ray images, an X-ray image enhancement algorithm combining USM+CLAHE is proposed. First, calculate the grayscale images on the R, G, and B channels of the X-ray image and perform CLAHE enhancement respectively, and then merge the enhanced R, G, and B grayscale images. Next, perform the USM sharpening operation on the X-ray image enhanced by CLAHE, and finally merge the original image and the USM sharpened image according to the weight. The experimental results show that the USM+CLAHE algorithm can effectively enhance the security X-ray image, and at the same time can suppress the color distortion of the enhanced image.