Reviewerly/exHarmony huggingface dataset:
CovertSYS: A systematic covert communication approach for providing secure end-to-end conversation via social networks

While encryption can prevent unauthorized access to a secret message, it does not provide undetectability of covert communications over the public network. Implementing a highly latent data exchange, especially with low eavesdropping/discovery probability, is challenging for practical scenarios, such as social and political movements in authoritarian regimes, military operations, and privacy preservation. Moreover, the current literature suffers from a low embedding capacity and monolingual applicability, limiting the amount of hiding secret data within short text messages using state-of-the-art algorithms, e.g., linguistic-based, structural-based, or coverless-based solutions. In this paper, we present a systematic covert communication technique called CovertSYS that enables a multilingual secure end-to-end conversation via messaging or social network platforms. The CovertSYS functions by encrypting a confidential message using a multi-factor authentication scheme and converting the encoded binary data into hidden Unicode symbols to be transmitted under cover of short text messages. We then conduct extensive experiments to confirm the security and validity of the proposed technique against state-of-the-art approaches. Our experimental results show that the CovertSYS provides a superior mean performance of 91.53% by improving the criteria scores: embedding capacity rate of 100%, imperceptibility rate of 76.4%, and distortion robustness rate of 98.2%. Finally, we discuss the practical implications of the proposed technique compared to the existing text steganography methods.
The time-course of real-world scene perception: Spatial and semantic processing

Real-world scene perception unfolds remarkably quickly, yet the underlying visual processes are poorly understood. Space-centered theory maintains that a scene's spatial structure (e.g., openness, mean depth) can be rapidly recovered from low-level image statistics. In turn, the statistical relationship between a scene's spatial properties and semantic content allows for semantic identity to be inferred from its layout. We tested this theory by investigating (1) the temporal dynamics of spatial and semantic perception in real-world scenes, and (2) dependencies between spatial and semantic judgments. Participants viewed backward-masked images for 13.3 to 106.7 ms, and identified the semantic (e.g., beach, road) or spatial structure (e.g., open, closed-off) category. We found no temporal precedence of spatial discrimination relative to semantic discrimination. Computational analyses further suggest that, instead of using spatial layout to infer semantic categories, humans exploit semantic information to discriminate spatial structure categories. These findings challenge traditional 'bottom-up' views of scene perception.
Improved Double-Layer Structure Multilabel Classification Model via Optimal Sequence and Attention Mechanism

Multilabel classification is a key research topic in the machine learning field. In this study, the author put forward a two/two-layer chain classification algorithm with optimal sequence based on the attention mechanism. This algorithm is a classification model with a two-layer structure. By introducing an attention mechanism, this study analyzes the key attributes to achieve the goal of classification. To solve the problem of algorithm accuracy degradation caused by the order of classifiers, we adopt the OSS (optimal sequence selection) algorithm to find the optimal sequence of tags. The test results based on the actual dataset show that the ATDCC-OS algorithm has good performance on all performance evaluation metrics. The average accuracy of this algorithm is over 80%. The microaverage AUC performance reaches 0.96. In terms of coverage performance, its coverage performance is below 10%. The comprehensive result of single error performance is the best. The loss performance is about 0.03. The purpose of the ATDCC-OS algorithm proposed in the study is to help improve the accuracy of multilabel classification so as to obtain more effective data information.
Time-varying signal recovery based on low rank and graph-time smoothness

Time-varying data recovery problem exists extensively in computer vision, image processing, environment monitoring, etc. In recent years, the emerging field of graph signal processing provides a new way to solve this problem, deriving the graph signal matrix completion (GSMC) which incorporates the correlation among data entries. The model-based methods of GSMC are more interpretable, but their reconstruction quality is still not satisfactory, especially when observations are sparse. In this paper, we propose a new matrix completion method to solve the time-varying data recovery problem. By adopting the time series analysis method to capture the evolution of data in the time dimension, we obtain a method based on Low Rank and Graph-Time Smoothness (LRGTS). The proposed method has high recovery accuracy by using the second-order information associated with the problem. Numerical results on three real-world datasets demonstrate that our scheme has better reconstruction performance when known entries are sparse, compared with existing matrix completion approaches.
FlowTracker: Improved flow correlation attacks with denoising and contrastive learning

Tor is the most widely used anonymous network which provides anonymity by using proxy nodes to route data. However, many researches have proved that flow correlation attacks can break the anonymity of users’ communication relationships: once attackers have access to the ingress and egress traffic flows at both ends of the network, they will find the specific user who is visiting a monitored Internet service by observing the traffic shape. Existing flow correlation attacks have limited effects because of insufficient traffic shape features, network noise influence and lack of differentiated learning on unrelated flows. In this paper, we propose an improved flow correlation attacking model named FlowTracker which contains four modules: In flow cumulative representation module, raw and normalized cumulative sequences are combined to construct robust traffic shape features. In noise resistant mapping module, stacked auto-encoders are adopted to learn the mapping relationship between related ingress and egress cumulative representations and filter out the network noise. Moreover, we leverage contrast learning in distance optimization module to generate the optimized representation in which difference between unrelated representations is amplified, further reducing the false correlation rate with similar but unrelated flows. Finally, the target flow is reported through a multi-layer decision process in the hierarchical judgment module. We evaluate FlowTracker and other advanced comparison methods on the public and self-built datasets with the more realistic unidirectional setting, experiment results demonstrate that FlowTracker has the highest attacking success rate under general and targeted scenarios, especially when the traffic shape is seriously destroyed by traffic obfuscation.
A Novel Approach for Detecting Face Masks and Social Distancing in Public Places

The system is fed with the CCTV footage or a real-time web camera, where the people in the frame get detected and the face mask detection module takes place. It uses the facial recognition and identifies a person without mask, followed by a notification alert message through a mail. Then the social distancing module checks whether the person is in a safe zone by measuring social distancing with nearby people. It alerts as “Please put on the mask. The people near you are not following social distancing.” The CCTV cameras are set-up in places where the system needs to follow the above violation metrics. Extra CCTV cameras can be installed if certain areas are meant to be monitored where CCTV cameras were not previously installed. Then the servers or computers must be installed to run inference of the received footage. The system uses a deep learning model in the computer servers to identify the above specified violation metrics.
Preserving Privacy of Patients With Disabilities in the Smart Healthcare Systems

With a greater boost in the development of technology, the healthcare industry has had a huge development in terms of offering innovative diagnoses and services to patients. Healthcare has become an indispensable resource in the daily lives of humans leading to a huge innovation in the healthcare sector. To improve the efficiency of diagnosis and with the potential to offer better treatment to patients, the healthcare industry has adopted numerous technologies such as the internet of things (IoT), machine learning, blockchain, etc., to pave way for sustainability in the medicinal sector. These systems, though very advanced, have some drawbacks; like problems of security and privacy of medical data that can expose these records to threats of privacy, and loss of data through manhandling of records and data transactions resulting in delays, while the patient is being monitored. etc., which can pose a major threat to the patient's well-being. This paper will provide how a blockchain network in any IoT-based device can help in the preservation of these medical records, using suitable algorithms.
Cost-free adversarial defense: Distance-based optimization for model robustness without adversarial training

Although convolutional neural networks (CNNs) have advanced to demonstrate superior performance in image classification tasks that often surpass human capability, the feature space of CNNs, which are trained using a typical training method, is limited by the smaller-than-expected inter-class variances. Consequently, CNNs are prone to misclassifying adversarial examples with high confidence, and the difference between an adversarial example and a normal input is indistinguishable by human beings. To alleviate this problem, we propose a training methodology that defends against adversarial attacks through a constraint that applies a class-specific differentiation to the feature space of CNNs. The proposed methodology first forces the feature representations that corresponding to each class to be localized on the hypersphere surface with a particular radius. The forced representation is then trained to be located as close to the center of the hypersphere as possible, resulting in feature representations with a small intra-class variance and large inter-class variances. The experimental results reveal that the proposed two-step training method enhances defense performance by 17.1%p and demonstrates a training speed of up to 30 times faster than the existing distance-based adversarial defense methodology. The code is available at: https://github.com/lepoeme20/cost-free-adversarial-defense
Transforming Construction Entities from Traditional Management to Autonomous Management Using Blockchain

Digitalizing supports construction entities’ processes that aim to perform better. However, bureaucracy and excessively complicated procedures are still causing poor project performance. The traditional management authority model is centralising and pyramidal, where decisions come from the upper management and apply over the project regardless of the project-specific needs. Autonomous management processes are driven according to individuals or workgroups perspectives. The autonomous management approach allows for an accelerated decision process to improve construction project performance. This article investigates the management of construction entities and activities using an autonomous management system by exploring the possibility of developing Blockchain-based management tools. Blockchain refers to the distributed ledger technology where data are stored permanently on a multi-node network and connected using cryptography. Blockchain can help track activities, monitor individuals’ performance and enforce accountability to create an autonomous construction organisation. Decentralised Autonomous Organisations (DAO) is a Blockchain concept that aims to automate decisions and facilitate process workflow. DAOs aim to reduce the time needed for repetitive decisions and distribute the responsibilities among the project participant. The study aims to shed light on the DAO concept and explore the possibilities of adopting it within construction practices. The article presents a hypothetical example for using DAOs in construction using the DAO deployment platform to illustrate the creative workflow for non-coders.
The Effect of Harmony Memory Integration into the Bees Algorithm

The advent of optimization algorithms facilitated finding good solutions for engineering problems. This paper presents a comparative case study between two algorithms relevant to bee search methods. One of the algorithms was modified by adding harmonic memory, which is a stage of the Harmonic Search Algorithm. Both algorithms were applied to a spherical four-linkFour -link mechanism for gripper design as a case study. The results in terms of the coupler trajectory of the mechanism showed the superiority of integrating harmony memoryHarmonyMemory into the Bees Algorithm. A prototype is manufactured to show the success of rapid design and production.
Α New Method to Generate the Initial Population of the Bees Algorithm for Robot Path Planning in a Static Environment

This research work presents a modified form of the Bees Algorithm for mobile robotMobile robot path planningPath planning. This modification is based on an alternative method to generate the initial population of the Bees Algorithm. The proposed method is adopted with the Bees AlgorithmBees Algorithm to find the shortest collision-free pathCollision-free path for a mobile robotMobile robot in static environmentsStatic environment. The environment is represented using a 2D configuration space method that includes the robot and stationary obstaclesStationary obstacles—this representation guarantees dealing with a continuous mapMaps as a reality. The new approach of initialising the population of the BeesBees Algorithm ensures finding the initial paths even though the complexity of the given environment. The local searchLocal Search and global searchGlobal Search are also implemented to enhance the initial solutions. Several benchmark mapsMaps were simulated to compute the fitness of the generated paths. The results obtained using the Bees AlgorithmBees Algorithm for path planningPath planning were compared with those of other algorithms. The simulation proved the significant performance of the Bees Algorithm. The comparison results show the efficiency and superiority of the proposed method in finding the shortest pathShortest path.
BLACK FUNGUS DETECTIONUSING MACHINE LEARNING

Fungus is extremely disreputable and dangerous for human health and cause various life- threatening diseasesto humans. Thousands of different fungusspecies exist in the world and sporesalways present in environment. Its sign and symptom are non-specific. Mucormycosis also knownas black fungus is a fungal infection that causes discoloration over nose and eye, blurred or doublevision, chest pain, breathing difficulties, fever and cough.The main aim of this project is to analyse and predict the infection probability based on the black fungus images with help of fungus detection system and algorithms to automatically detects fungus using machine learning techniques. we are using in project CNN algorithm.The dataset used is raw data based on the pulmonary Mucormycosis symptoms.
Optimization Methods for Convolutional Neural Networks – The LeNet-5 Algorithm

Convolutional neural networks are enhanced version of fully connected neural networks. The neural networks are used to recognize objects after training the neural network system for some datasets that can also be divided into classes at the output. These networks were a breakthrough in computer vision filed for object recognition where the system can optimize its parameters for better results with using feed forward and back propagation. The convolutional neural networks reduced the time of training and testing the dataset by replacing the full network nodes connecting to each node in the subsequent layer to some nodes or filter to each subsequent layer node. There are many algorithms for convolutional neural networks ranging from simple algorithms to complex ones. Each algorithm has different hidden layers with different hyper parameters and filters. The activation functions and number of nodes in each layer for each algorithm may be different. The applications for these convolutional neural networks cover many fields such as hand written digit recognition, alphabet handwritten recognition, and any group of objects that can be divided into classes such as cloth, X-ray imaging and many more. The LeNet-5 algorithm is one of the convolutional neural networks. With full analysis of this algorithm, I will prove that a simple module of the algorithm can provide maximum accuracy and minimum loss function than the original algorithm.
PrevDistro: An open-access dataset of Hungarian preverb constructions

Abstract Hungarian has a prolific system of complex predicate formation combining a separable preverb and a verb. These combinations can enter a wide range of constructions, with the preverb preserving its separability to some extent, depending on the construction in question. The primary concern of this paper is to advance the investigation of these phenomena by presenting PrevDistro (Preverb Distributions), an open-access dataset containing more than 41.5 million corpus occurrences of 49 preverb construction types. The paper gives a detailed introduction to PrevDistro, including design considerations, methodology and the resulting dataset's main characteristics.
Cross-lingual transfer of knowledge in distributional language models: Experiments in Hungarian

Abstract In this paper, we argue that the very convincing performance of recent deep-neural-model-based NLP applications has demonstrated that the distributionalist approach to language description has proven to be more successful than the earlier subtle rule-based models created by the generative school. The now ubiquitous neural models can naturally handle ambiguity and achieve human-like linguistic performance with most of their training consisting only of noisy raw linguistic data without any multimodal grounding or external supervision refuting Chomsky's argument that some generic neural architecture cannot arrive at the linguistic performance exhibited by humans given the limited input available to children. In addition, we demonstrate in experiments with Hungarian as the target language that the shared internal representations in multilingually trained versions of these models make them able to transfer specific linguistic skills, including structured annotation skills, from one language to another remarkably efficiently.
Recognition of Continuous Face Occlusion Based on Block Permutation by Using Linear Regression Classification

Face occlusion is still a key issue in the study of face recognition. Continuous occlusion affects the overall features and contour structure of a face, which brings significant challenges to face recognition. In previous studies, although the Representation-Based Classification Method (RBCM) can better capture the differences in different categories of faces and accurately identify human face images with changes in light and facial expressions, it is easily affected by continuous occlusion. For face recognition, there is a situation where face error recognition occurs. The RBCM method frequently learns to cover the characteristics of face recognition and then handle face error recognition. Therefore, the elimination of occlusion information from the image is necessary to improve the robustness of such models. The Block Permutation Linear Regression Classification (BPLRC) method proposed in this paper includes image block permutation and Linear Regression Classification (LRC). The LRC algorithm belongs to the category of nearest subspace classification and uses the Euclidean distance as a metric to classify images. The LRC algorithm is based on one of the classification methods that is susceptible to outliers. Therefore, block permutation was used with the aim of establishing an image set that does not contain much occlusion information and constructing a robust linear regression model. The BPLRC method first modulates all the images and then lists the schemes that arrange all segments, enters the image features of various schemes into linear models, and classifies the result according to the minimum residual of the person’s face image and reconstruction image. Compared to several state-of-the-art algorithms, the proposed method effectively solves the continuous occlusion problem for the Extended Yale B, ORL, and AR datasets. The proposed method recognizes the AR data concentration scarf to cover the accuracy of human face images to 93.67%. The dataset recognition speed is 0.094 s/piece. The arrangement method can be combined not only with the LRC algorithm, but also other algorithms with weak robustness. Due to the increase in the number of blocks and the increase in the calculation index of block arrangement methods, it is necessary to explore reasonable iteration methods in the future, quickly find the optimal or sub-best arrangement scheme, and reduce the calculation of the proposed method.
Teaching Reform of Ancient Literature Based on Credible BP Neural Network Technology in New Media Environment

Recent years have seen a progressive integration of the new media environment into people’s lives, studies, and employment, which has had a profound impact on China’s social economy. It is unavoidable to conduct the educational reform of classical literature in the age of new media. Data mining (DM) is a method for analysing and learning from data in databases and data warehouses using artificial intelligence. They work better together to create a solid foundation for decision-making analysis of businesses or pertinent departments in many fields. The BP algorithm can cause the weight to converge to a specific number, but it cannot ensure that that value is the error plane’s overall minimum. In order to correct the flaw that the BP neural network is prone to falling into local minima, the improved credible BP neural network used in this paper adopts the momentum factor. The findings demonstrate that there is little variation between the predicted value and the actual value achieved by applying the BP algorithm in the experimental group with order of magnitude standardised operation, and that this algorithm’s error has been decreased by 7.38%. It demonstrates that by examining students’ test scores in a data warehouse with a neural network algorithm in DM, we may discover potential patterns among the data.