Reviewerly/exHarmony huggingface dataset:
Multi-UAVs for Bushfire Situational Awareness: a Comparison of Environment Traversal Algorithms

Unmanned Aerial Vehicles (UAVs) are efficient (often Internet-connected) devices with unique capabilities, making them suitable for achieving continuous and real-time Situational Awareness (SA) over disaster areas such as bushfires. Nevertheless, UAVs are resource-constrained with limited processing power and flight time, preventing them from achieving full coverage of the affected area. This research aims at developing a computationally fast approach implementable on resource-constrained UAVs for mission planning and traversing the environment to monitor the fire-affected areas. Thus, this study investigates the performance of optimized Rapidly-exploring Random Tree (RRT*) and modified Genetic Algorithm (GA) for multiple UAVs, facilitating them to efficiently traverse over the affected area and capture information from the spots with over 50% probability of fire existence. The performance of these two algorithms is thoroughly investigated, and a comparative analysis proves the efficiency of GA in satisfying all mission objectives, including short computation time and efficient battery usage.
Research and Development of Real Estate Information Registration Platform Based on WebGIS

For the purpose of managing the registration process and results, this paper uses WebGIS to develop the real estate information registration platform to realize the functions of user login, data query, data editing, data output and data analysis, meet the work requirements of real estate registration, and help the registration department to perform the unified registration responsibilities of real estate. It effectively solves the difficult problem of information integration after the integration of registration institutions, and highlights the accuracy, openness and ease of use of the platform, which brings great convenience to the real estate registration in Qingzhou area.
Multimedia Recommendation System for Video Game Based on High-Level Visual Semantic Features

The increase of multimedia content in e-commerce and entertainment services creates a new research gap in the field of recommendation systems. The main emphasis of the presented work is on increasing the accuracy of multimedia recommendations using visual semantic content. Recent approaches have shown that the inclusion of visual information is helpful to understand the semantic features for a recommendation model. The researchers have contributed to the field of multimedia item recommendations using low-level visual semantic features. Here, we seek to extend this contribution by exploring the high-level visual semantic content using constant visual attributes for video game recommendation systems. With the exponential growth of multimedia content in the video game industry in the last decade, researchers investigate the importance of personalized video game recommendation techniques. Previous methods have not investigated the importance of visual semantic content for video game recommendations. A practical recommendation system for video games is challenging due to the data diversity, level of user interest, and semantic complexity of features involved. This study proposed a novel method named Deep Visual Semantic Multimedia Recommendation Systems (D_VSMR) to deal with high-level visual features for multimedia recommendation systems. A visual semantic-based video game recommendation system utilizing deep learning methods for visual content learning and user profile learning is introduced. The proposed approach employs content-based techniques to expand users’ profiles. The user profile expansion is based on the visual content of games. The required datasets have been obtained from video game e-commerce platforms like Google Play Store and Amazon for evaluation purposes. The evaluation results have shown that the proposed approach’s accuracy and effectiveness have been improved up to 95.87% compared to the other state-of-the-art methods.
Compound Fault Separation and Diagnosis Method Based on FSA-CNN and DAN

With the industry becoming larger and more complicated, the technology of fault diagnosis becomes more and more important. Due to the strong coupling and non-stationarity of the compound fault, the existing fault diagnosis methods cannot accurately identify all single faults’ detailed information contained in the compound fault. This paper proposes a compound fault separation and diagnosis method based on FSACNN and DAN. Firstly, in order to highlight certain frequency segments, frequency segment attention module is added to CNN. Secondly, a compound fault feature separation framework based on DAN is proposed, which can separate compound fault to two fault components accurately. Thiredly, a signature matrix is introduced into ELM to improve the performance of the classifier. Finally, ablation experiments are designed to prove the advantage of the proposed method.
Latin Square Image Cipher for Medical Images

With the increase in threat of online hacking and exploitation of personal data such as medical images there has been a demand and a need for providing a robust and near impenetrable encryption system. In this paper, we implement an approach useful in the encryption of images and videos called ‘Latin Square Image Cipher’(LSIC). The implementation has been carried out for grayscale, as well as color images. In this, we execute algorithms designed to generate Latin squares so that we can manipulate pixel data. The cipher includes random noise embedding with the Least Significant Bit (LSB) into the encryption process so as to strengthen the security. The LSIC algorithm ensures a uniformly distributed pixels in the cipher text and also works on a two-dimensional key space. In addition to the above it also has excellent confusion and diffusion properties, which is an important factor to take into account while designing a cipher. The correlation analysis conducted indicates that the cipher shows good resistance and security against brute force attacks.
A Boolean Network Tomography based Method for Deterministic Multi-point Fault Detection

Time-sensitive networking (TSN) is a deterministic network, where failures will seriously affect the quality of the real-time data transmission service. Hence, fault localization is critical in TSN. In the application domain of TSN, an ideal link failure detection method needs to have properties including execution time deterministic, low detection traffic overhead, multi-point fault detection capability for arbitrary typologies. However, current state-of-the-art fault detection algorithms can not achieve these properties simultaneously. In this paper, we propose a Boolean network tomography-based multi-point fault detection method that leverages the deterministic transmission mechanism of TSN to guarantee that the detection time is upper bounded. The method includes an offline preparation phase and an online operation phase. In the preparation phase, we use a detection flow generation algorithm to get a set of detection flows that can identify up to <tex>$K$</tex> faults with as few paths as possible. In the operation phase, detection packets are realized as time-sensitive flows routing along the detection paths. They are sent periodically to the controller which performs a topology discovery algorithm to infer the faulty links based on the arrival status of each detection packet. Comprehensive experiments are conducted and the results show that compared with existing fault detection methods, the proposed algorithm can accurately identify multiple faulty links in deterministic time, and the generated detection paths set needs fewer paths than the prior methods.
Road Traffic Density Surveillance System using IR Sensor, Image Processing and Raspberry Pi: A Comparative Analysis

In our present time, the use of vehicles has become widespread which has given rise to the problem of traffic congestion a common sight in day-to-day life. And with the increased sales of vehicles in recent years the problem is almost considered as a known factor that gives rise to noise and air pollution in coming years. This imminent problem gives validation to the use of de-congestion methods, some of which are more efficient traffic signs and constant traffic monitoring to prevent a standstill event that is known to devolve into traffic jams. Traffic monitoring can be done with the help of Frame comparison as well as Edge Detection in tandem with IR sensors that give a rough estimate of how far the traffic congestion goes. In this paper we have focused on the merging of three different approaches viz. the IR Sensor in passive mode, Real-time Background Subtraction in a semi-active mode, and Real-time Canny Image Processing in an active mode in an efficient manner in a prototype and analyzed their performance using Raspberry pi for selection of a preferred system. The results obtained reduce traffic congestion using the dynamic times allocated to the higher traffic lanes until an average time, which is assumed with lower traffic lanes. This method is inexpensive and easily implemented with Raspberry Pi's help to run it arguably efficiently on medium-traffic areas.
Social Recommendation System with Multimodal Collaborative Filtering

The widespread use of social network applications has led to an information explosion, making it difficult for users to find target information smoothly and accurately. The personalized recommendation has become a key solution in this situation, and it helps users to easily obtain the information that they are interested in. In this study, we propose a novel framework to provide recommendations more precisely, which is an attention-based recommendation system with multimodal graph collaborative filtering. Our system first exploits the high-order connectivity representations via graph convolutional networks, and then using the relations between users and items to obtain features. After that, our system utilizes attention-based LSTM to capture user's habits and preferred items. By leveraging the user and item information to create multimodal graphs, the proposed system is able to better capture users' habits and provide better recommendations for the users of social network applications. Through experiments on three real-world datasets, we demonstrate that our proposed system is able to significantly outperform the state-of-the-art methods.
Implementation and Performance Analysis of Indian Sign Language Recognition on various Computing Devices

Sign Language is a language that uses hand and facial gestures for communication. Its automated recognition is important as it would aid deaf and mute community since they use Sign Language to communicate. In this work an image data set of Indian Sign Language having 36 classes of commonly used words is presented. Three deep learning models VGG16, VGG19 and InceptionV3 were used for classification of the images in the data set achieving accuracy as high as 99.7852%. Three computing devices mobile CPU, High Performance Computing (HPC) machine, and Intel Neural Compute Stick 2 (Intel NCS 2) were used for measuring inference time with the least time recorded to be 77.2819 seconds. Comparative study of the performance of the deep learning models and computing devices has been presented in this paper.
The Performance of Different Algorithms to Solve Traveling Salesman Problem

Traveling salesman problem, with extensive potential applications in industries of all sorts, has been studied for decades. Although there are massive solutions put forward, the most efficient and exact way has never been widely acknowledged since this problem is typically NP-hard. We selected three types of most commonly used algorithms to solve this problem: Greedy Algorithm, Ant Colony Algorithm and Simulated Annealing Algorithm. With data sets given in TSPLIB, we computed and compared these algorithms in both the shortest distance and the time cost. Based on the statistics obtained from experiments, it can be found that Greedy Algorithm runs the fastest with a sacrifice of accuracy, whereas Simulated Annealing Algorithm searches out the shortest path in a relatively small group of cities and Ant Colony Algorithm performs even better when the points increase.
Dense 3D Coordinate Code Prior Guidance for High-Fidelity Face Swapping and Face Reenactment

In face synthesis tasks, commonly used 2D face representations (e.g. 2D landmarks, segmentation maps, etc.) are usually sparse and discontinuous. To combat these shortcomings, we utilize a dense and continuous representation, named Projected Normalized Coordinate Code (PNCC), as the guidance and develop a PNCC-Spatio-Normalization (PSN) method to achieve face synthesis regarding arbitrary head poses and expressions. Based on PSN, we provide an effective framework for face reenactment and face swapping task. To ensure a harmonious and seamless face swapping, a simple yet effective Appearance-Blending Module (ABM) is proposed to fit the synthesized face to the target face. Our method is subject-agnostic and can be applied to any pair of faces without extra fine-tuning. Both qualitative and quantitative experiments are conducted to demonstrate the superiority of the proposed method in comparisons to existing state-of-the-art systems.
Improving ASR Error Correction Using N-Best Hypotheses

In the field of Automatic Speech Recognition (ASR), Grammatical Error Correction (GEC) can be used to correct errors in recognition results of ASR systems and whereby it further reduces the word error rate (WER). Most conventional GEC approaches make correction merely based on a single recognition hypothesis from the ASR system, i.e., the best path in the decoding lattice. However, with such limited information, it's common that GEC fails to detect errors in a sentence or even makes wrong corrections. In this paper we propose two novel GEC approaches utilizing N-best hypotheses instead, that's, N-best plain approach and N-best alignment approach. In this way, our novel approaches can access more information than conventional approaches with 1-best-only hypothesis, and thus more accurate corrections can be made. Experiments on an industrial-scale internal dataset and the open-source dataset AISHELL-1 demonstrate the effectiveness of N-best approaches: 1) both N-best plain and N-best alignment approaches achieve remarkable WER reduction. N-best alignment approach achieves the best performance on both datasets, 12.54% WERR on an internal dataset and 27.71% WERR on AISHELL-1. 2) They also outperform the baseline 1-best GEC approach by a large margin.
Darknet Web URL Detection without URL Content Leakage

Darknet is a kind of network that uses special software to communicate anonymously. The original purpose of anonymous communication is to protect users’ privacy. However, due to its untraceability, the darknet is used by criminals to engage in illegal activities. Detecting the URL of the darknet is the key step to control the darknet. According to the characteristics of the darknet web, this paper proposes a method to detect the dark web address by using URL entropy, an attribute independent of web content. URL entropy reflects the dispersion of URL characters and does not reveal the content of URL. Based on the URL entropy, this paper proposes a URL detection method for the darknet web URL, which does not disclose the URL content. Experiments show that the proposed URL detection model based on URL entropy has higher security and stability.
Communication-Efficient Heterogeneous Federated Dropout in Cross-device Settings

Federated Dropout has emerged as an elegant solution to conjugate communication-efficiency and computation-reduction on Federated Learning (FL) clients. We claim that Federated Dropout can also efficiently cope with device heterogeneity by exploiting a server that broadcasts custom and differently-sized sub-models, selected from a discrete set of possible sub-models, to match the computation capability constraints of FL clients. In addition, we further reduce the up-link communication cost by applying per-layer or traditional Sparse Ternary Compression (STC) to sub-model updates. We demonstrate the effectiveness of our solution by reporting results for a well-known CNN used for classification tasks considering the Federated EMNIST dataset.
Digital watermarking in fractional integral transform domain with chaos based random and partially informed detection

This work aims to design and develop a watermarking scheme that is suitable to applications where high imperceptibility is desired along with improved security from malicious attacks, for e.g., the medical images need to be minimally distorted while embedding the information. Unlike other schemes that are majorly based on DWT-SVD or other decomposition methods that have only embedding strength as the secret key, our proposed scheme is based on embedding watermark information in fractional integral transform domain with embedding locations that are sparse and randomly selected with a 2D robust LSCM based chaos. The secret key comprises of transform orders, input parameters of the chaotic map and the watermark strength. Experimental results and evaluations prove that the proposed scheme is able to provide higher security with larger key space and is robust to most of the image processing attacks.
Thai Name Gender Classification using Deep Learning

Thai name gender prediction model is presented in this work. The proposed model is based on a Bidirectional Long Short Term Memory (Bi-LSTM). Two dropout blocks are added to reduce the overfitting issue of the model. Two sets of Thai name corpus are used for training and evaluating the proposed model. The first corpus consists of Thai names collected from social media in this work while the second corpus is a combination of the first corpus and the publicly available dataset. The names are classified into male and female. The unisex classification is labeled to the names that the proposed model cannot decide to be either male or female with a probability greater than 0.65. The proposed model provides an average overall accuracy of78.65&#x0025;. The model generated from the second corpus provided the best performance at an average overall accuracy of 81.43&#x0025;. The model generated from the first corpus provided the average overall performance of 75.87&#x0025;.
Detecting Vulnerabilities in Source Code Using Machine Learning

In recent years, software vulnerabilities have been the source of countless cyber attacks. Despite the existence of various methods for detecting software vulnerabilities like static analysis tools and dynamic analysis tools, the number of vulnerabilities discovered each year continues to climb. Over the last decade, machine learning models have made a significant progress. As machine learning methods, do not need human experts to define features and can learn vulnerability patterns automatically as it captures patterns that human may not understand. The goal of this paper is to create a machine learning model that could discover vulnerabilities in the function scope (i.e. method or procedure in any programming). To accomplish this aim we propose a novel feature extraction technique based on clustering the vocabulary of the function text using Kmeans. Typically, the vulnerability classification problem is an imbalanced one, as most functions are naturally not vulnerable. We use a dataset and compare the effect of four different class imbalance handling techniques (Class weight, Random Undersampling, Random Oversampling and Synthetic Minority Oversampling Technique (SMOTE)). Results show that using the Class Weight modification technique is the best in both metrics we use (Recall and F1 score) (76% and 17%). The results show that our method has very comparable results relative to other methods in addition to faster training time due to using shallow machine learning model.
Experimental verification of full-field accuracy in stereo-DIC based on the ESPI method

This paper proposes a method to merge stereo-digital image correlation (DIC) and electronic speckle pattern interferometry (ESPI) data by camera calibration. The proposed method is employed to verify the accuracy of full-field out-of-plate displacements measured by stereo-DIC in a cantilever beam test. The mean absolute error and the root mean square error (RMSE) of the full-field displacement measured by four-megapixel cameras are 0.849 µm and 1.08 µm at 60 mm field of view, respectively, and the RMSE of the central area is 0.615 µm. The errors are not uniformly distributed because of the imperfect calibration. When the lenses are changed and the field of view reaches 120 mm, the RMSE is 1.48 µm with uniform distribution. These accuracies could be traced back to the laser wavelength to confirm the stereo-DIC data. The proposed method can be used not only to verify the full-field measurement accuracy of DIC but also to determine the rigid-body displacement for ESPI with a high-precision stereo-DIC. Thus, the displacement vector can be obtained. Furthermore, it can unify the coordinate of multiple ESPI systems to achieve a large range of high-precision three-dimensional deformation measurements.
Kidney MRI Segmentation for Lesion Detection Using Clustering with Slime Mould Algorithm

Both the incidence and mortality rates of kidney cancer are increasing worldwide. Imaging examinations followed by effective systemic therapies can reduce the mortality rate. In this article, a new method to segment the kidney MRI for lesion detection is developed using a hard-clustering technique with Slime Mould Algorithm (SMA). First, a new partitional or hard clustering technique is developed using SMA which searches the optimal cluster centers for segmentation. In the preprocessing steps of the proposed method, the noise and intensity inhomogeneities are removed from the MR images as these artifacts affect the segmentation process. Region of Interests (ROIs) are selected and the clustering process is carried out using the SMA-based clustering technique. After the clustering, i.e., segmentation, the lesions are separated from the segmented images and finally, localized in the MR images as the postprocessing steps. The quantitative results are measured in terms of a well-known cluster validity index named Dunn-index and compared with that of the K-means algorithm. Both the quantitative and qualitative (i.e., visual) results show that the proposed method performs better than K-means.
Related-Tweakey Impossible Differential Attack on Reduced-Round SKINNY-AEAD M1/M3

SKINNY-AEAD is one of the second-round candidates of the Lightweight Cryptography Standardization project held by NIST. SKINNY-AEAD M1 is the primary member of six SKINNY-AEAD schemes, while SKINNY-AEAD M3 is another member with a small tag. In the design document, only security analyses of their underlying primitive SKINNY-128-384 are provided. Besides, there are no valid third-party analyses on SKINNY-AEAD M1/M3 according to our knowledge. Therefore, this paper focuses on constructing the first third-party security analyses on them under a nonce-respecting scenario. By taking the encryption mode of SKINNY-AEAD into consideration and exploiting several properties of SKINNY, we can deduce some necessary constraints on the input and tweakey differences of related-tweakey impossible differential distinguishers. Under these constraints, we can find distinguishers suitable for mounting powerful tweakey recovery attacks. With the help of the automatic searching algorithms based on STP, we find some 14-round distinguishers. Based on one of these distinguishers, we mount a 20-round and an 18-round tweakey recovery attack on SKINNY-AEAD M1/M3. To the best of our knowledge, all these attacks are the best ones so far.