Reviewerly/exHarmony huggingface dataset:
Interferometric accurate investigation of opto-thermo-mechanical features with help of artificial intelligence for antimicrobial polyamide-6 fibres grafted by quaternary ammonium salt with nano zinc oxide

The Mach–Zehnder interferometer attached with multifunction mechanical stretching and rotation device is utilized for examining the opto-thermo-mechanical features of antimicrobial polyamide-6 fibres grafted by quaternary ammonium salt with nano zinc oxide (PA6-g-QAS/ZnO). A refined artificial intelligence strategy is suggested to perform an accurate investigation for the opto-thermo-mechanical properties of these antimicrobial fibrous materials. The changing in refractive indices and birefringence along the axis of PA6-g-QAS/ZnO fibre is examined. The morphology and the homogeneity of the tested fibre are investigated via the interferometric computed tomographic technique. To improve opto-mechanical features and antimicrobial activity of this fibre, the stretching and annealing processes are suggested. The impact of cold stretching on the optical and structural features of the PA6-g-QAS/ZnO fibres is examined. The antimicrobial activities of the PA6-g-QAS/ZnO fibres are calculated before and after the cold stretching process. Furthermore, the effect of annealing process on the refractive index, and birefringence along the axis of PA6-g-QAS/ZnO fibres and their antimicrobial activities is studied at various temperatures and durations to prefer the annealing conditions. The opto-mechanical properties of the annealed PA6-g-QAS/ZnO fibres at the most preferred annealing conditions is investigated. The obtained results give significant data for improving the opto-mechincal features and antimicrobial activity of antimicrobial materials.
DCR‐Net: Dilated convolutional residual network for fashion image retrieval

Abstract Fashion image retrieval is an important branch of image retrieval technology. With the rapid development of online shopping, fashion image retrieval technology has made a breakthrough from text‐based to content‐based. But there is still not a proper deep learning method used for fashion image retrieval. This article proposes a fashion image retrieval framework based on dilated convolutional residual network which consists of two major parts, image feature extraction and feature distance measurement. For image feature extraction, we first extract the shallow features of the input image by a multi‐scale convolutional network, and then develop a novel dilated convolutional residual network to obtain the deep features of the image. Finally, the extracted features are transformed into high‐dimensional features vector by a binary retrieval vector module. For feature distance measurement, we first use PCA to reduce the dimension of the extracted high‐dimensional vectors. Then we propose a mixed distance measurement algorithm combined with cosine distance and Mahalanobis distance to calculate the spatial distance of the feature vectors for similarity ranking, which solves the problems of poor robustness in complex background fashion image retrieval and the inefficiency calculation of Mahalanobis distance. The experimental results show the superiority of our fashion image retrieval framework over existing state‐of‐the‐art methods.
A discrete artificial bee colony algorithm for quadratic assignment problem

The quadratic assignment problem (QAP) is a well-known challenging combinational optimization problem that has received many researchers’ attention with varied real-world and industrial applications areas. Using the framework of basic artificial bee colony algorithm, frequently used crossover and mutation operators, and combined with an effective local search method, this paper proposes a simple but effective discrete artificial bee colony (DABC) algorithm for solving quadratic assignment problems (QAPs). Typical QAP benchmark instances are selected from QAPLIB in order to conduct the simulation experiment where common performance metrics are used to evaluate the algorithm. The paper also investigates the influence factors of the algorithm’s performance. The results show that the proposed algorithm is a quite effective and practical new approach for handling QAP problems.
Evolution-Based Online Automated Machine Learning

Automated Machine Learning (AutoML) deals with finding well-performing machine learning models and their corresponding configurations without the need of machine learning experts. However, if one assumes an online learning scenario, where an AutoML instance executes on evolving data streams, the question for the best model and its configuration with respect to occurring changes in the data distribution remains open. Algorithms developed for online learning settings rely on few and homogeneous models and do not consider data mining pipelines or the adaption of their configuration. We, therefore, introduce EvoAutoML, an evolution-based online learning framework consisting of heterogeneous and connectable models that supports large and diverse configuration spaces and adapts to the online learning scenario. We present experiments with an implementation of EvoAutoML on a diverse set of synthetic and real datasets, and show that our proposed approach outperforms state-of-the-art online algorithms as well as strong ensemble baselines in a traditional test-then-train evaluation.
On how VoIP attacks foster the malicious call ecosystem

Switched telephone networks are a key and ubiquitous infrastructure. Recent technological advances have integrated modern and inexpensive systems into these networks in order to use the Internet to place calls via Voice over IP (VoIP). The evolution of this technology has also led to an increase in the number and sophistication of the techniques used by criminals to commit fraud. Specifically, with the emergence of VoIP, attackers can now adapt tools commonly used by cybercriminals, such as botnets, to make their attacks more complex and insidious. For example, through bots they can dial multiple numbers automatically, enabling them to target a greater number of victims, and do so more quickly. While recent studies have shed light on how certain parts of this ecosystem work, it is still unclear how attacks on VoIP systems contribute to this type of fraud. This paper presents a novel VoIP honeypot that captures voice interactions, in addition to employing low-level telemetry. With the study of how attackers obtain access to our honeypot and the actions they perform, we present an overview of the most prevalent types of fraud used in this ecosystem, including unique insights into the origin of the attacks and the destination of calls made through our architecture. Finally, we analyze in depth the actions taken to study the different types of telephony fraud.
Robot Path Planning Method Based on Indoor Spacetime Grid Model

In the context of digital twins, smart city construction and artificial intelligence technology are developing rapidly, and more and more mobile robots are performing tasks in complex and time-varying indoor environments, making, at present, the unification of modeling, dynamic expression, visualization of operation, and wide application between robots and indoor environments a pressing problem to be solved. This paper presents an in-depth study on this issue and summarizes three major types of methods: geometric modeling, topological modeling, and raster modeling, and points out the advantages and disadvantages of these three types of methods. Therefore, in view of the current pain points of robots and complex time-varying indoor environments, this paper proposes an indoor spacetime grid model based on the three-dimensional division framework of the Earth space and innovatively integrates time division on the basis of space division. On the basis of the model, a dynamic path planning algorithm for the robot in the complex time-varying indoor environment is designed, that is, the Spacetime-A* algorithm (STA* for short). Finally, the indoor spacetime grid modeling experiment is carried out with real data, which verifies the feasibility and correctness of the spacetime relationship calculation algorithm encoded by the indoor spacetime grid model. Then, experiments are carried out on the multi-group path planning algorithms of the robot under the spacetime grid, and the feasibility of the STA* algorithm under the indoor spacetime grid and the superiority of the spacetime grid are verified.
SAT: Integrated Multi-agent Blackbox Security Assessment Tool using Machine Learning

The widespread adoption of eCommerce, iBanking, and eGovernment institutions has resulted in an exponential rise in the use of web applications. Due to a large number of users, web applications have become a prime target of cybercriminals who want to steal Personally Identifiable Information (PII) and disrupt business activities. Hence, there is a dire need to audit the websites and ensure information security. In this regard, several web vulnerability scanners are employed for vulnerability assessment of web applications but attacks are still increasing day by day. Therefore, a considerable amount of research has been carried out to measure the effectiveness and limitations of the publicly available web scanners. It is identified that most of the publicly available scanners possess weaknesses and do not generate desired results. In this paper, the evaluation of publicly available web vulnerability scanners is performed against the top ten OWASP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> OWASP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> The Open Web Application Security Project (OWASP) is an online community that produces comprehensive articles, documentation, methodologies, and tools in the arena of web and mobile security. vulnerabilities and their performance is measured on the precision of their results. Based on these results, we proposed an Integrated Multi-Agent Blackbox Security Assessment Tool (SAT) for the security assessment of web applications. Research has proved that the vulnerabilities assessment results of the SAT are more extensive and accurate.
Secure and Efficient Hybrid Data Deduplication in Edge Computing

As an extension of cloud computing, edge computing introduces additional intermediate devices, called edge nodes near clients, providing computing services on behalf of the central cloud more efficiently. Although edge computing brings several benefits such as low latency and bandwidth savings on the edge side, rapid increase in the amount of data transmitted to the central cloud hinders efficient utilization of the storage system on the central cloud side especially when the data from edge devices are encrypted. To mitigate this issue in a privacy-preserving manner, data deduplication techniques for encrypted data have been extensively studied to enhance both the security and efficiency in the conventional cloud system with two different approaches. A server-side secure deduplication approach protects data privacy but impairs network efficiency by allowing duplicate uploads, while a client-side one improves network efficiency but suffers from potential information leakage due to its vulnerability to the side-channel attack. In this article, we propose a hybrid secure deduplication scheme for edge computing, which guarantees both advantages of the aforementioned two approaches. Specifically, our scheme guarantees data privacy by applying the server-side deduplication technique between the client and the edge nodes and maximizes network efficiency through the client-side deduplication technique between the edge nodes and the cloud. In addition, we devise a novel additively homomorphic encryption for efficient deduplication operations in the resource-limited edge nodes. Based on our experimental results, the proposed scheme reduces the communication costs by approximately 2.5 times for a storage server when the duplicate ratio is 50%, and the response time is reduced by about 2 times when the data size is 16 MB.
Study of subjective and objective quality assessment of infrared compressed images

Abstract Given the lack of accessible infrared compressed images’ benchmarks annotated by human subjects, this work presents a new database with the aim of studying both subjective and objective image quality assessment (IQA) on compressed long wavelength infrared (LWIR) images. The database contains 20 reference (pristine) images and 200 distorted (degraded) images obtained by application of the most known compression algorithms used in multimedia and communication fields, namely: JPEG and JPEG-2000. Each compressed image is evaluated by 31 subjects having different levels of experience in LWIR images. Mean opinion scores (MOS) and natural scene statistics (NSS) of pristine and compressed images are elaborated to study the performance of the database. Five analyses are conducted on collected images and subjective scores, namely: analysis by compression type, analysis by file size, analysis by reference image, analysis by quality level and analysis by subject. Moreover, a wide set of objective IQA metrics is applied on the images and the obtained scores are compared with the collected subjective scores. Results show that objective IQA measures correlate with human subjective results with a degree of agreement up to 95 %, so this benchmark is promising to improve existing and develop new IQA measures for compressed LWIR images. Thanks to a real-world surveillance original images based on which we analyze how image compression and quality level affect the quality of compressed images, this database is primarily suitable for (military and civilian) surveillance applications. The database is accessible via the link: https://github.com/azedomar/compressed-LWIR-images-IQA-database . As a follow-up to this work, an extension of the database is underway to study other types of distortion in addition to compression.
Open-Eye: An Open Platform to Study Human Performance on Identifying AI-Synthesized Faces

Al-synthesized faces are visually challenging to discern from real ones. They have been used as profile images for fake social media accounts, which leads to high negative social impacts. Although progress has been made in developing automatic methods to detect Al-synthesized faces. there is no open platform to study the human performance of Al-synthesized faces detection. In this work, we develop an online platform called Open-eye to study the human performance of Al-synthesized faces detection. We describe the design and workflow of the Open-eye in this paper.
Solar Energy Forecasting in Short Term Based on the ASO-BPNN Model

Accurate forecasting of short-term photovoltaic power output is vital for enhancing the operation efficiency of photovoltaic (PV) power stations and ensuring the safety and stable operation of grid-connected PV plants. Therefore, a short-term power forecasting model based on a backpropagation neural network with atom search optimization optimizes the weights and thresholds. Meanwhile, the Pearson correlation coefficient formula is introduced to screen the key meteorological factors and eliminate redundant factors, i.e., total irradiance, temperature, humidity, and direct irradiance are taken as the input of the prediction model. Moreover, the Euclidean distance formula is used to establish a customized training set for each test data which improves the dependability of the training set. Lastly, with the simulations of actual data from a solar farm in Yunnan, China, it is verified that the proposed ASO-BPNN model is competent to forecast the PV power generation.
Cosmetic Product Selection Using Machine Learning

Whenever a person wants to try a new cosmetic item, it's so difficult to choose. It's actually more than difficult. It's sometimes scary because new items that have never tried end up giving skin trouble. It is known that the information we need is on the back of each product, but it's really hard to interpret those ingredient lists unless you're a chemist. So instead of just being worried about the new choice, we decided to use Machine Learning technology to provide a solution for this problem. This paper suggests a simple cosmetic recommendation system. we extracted the effective cosmetic ingredients for each user attribute and developed a recommender system based on ingredients. We did a web scraping from sephora page, from where cosmetic ingredient information and chemicals used are extracted. Then NLP concepts are applied to the chemicals and a Document term matrix (DTM) is made. The matrix is filled with 1 or 0. If an ingredient is in a cosmetic, the value is 1. If not, it remains 0. All of the cosmetic items in our data will be vectorized into two-dimensional coordinates, and the distances between the points will indicate the similarities between the items. Finally, a plot is made. The plot is made as a scatter plot using plotly and add a hover tool to show that information.
Azerbaijani sign language recognition using machine learning approach

Abstract Sign Language recognition is one of the essential and focal areas for researchers in terms of improving the integration of speech and hearing-impaired people into common society. The main idea is to detect the hand gestures of impaired people and convert them to understandable formats, such as text by leveraging advanced approaches. In this paper, we present our contribution to the improvement of Azerbaijani Sign Language (AzSL). We worked on AzSL Alphabet static signs real-time recognition. The method applied in this work is Object Classification and Recognition by leveraging pre-trained lightweight Convolutional Neural Networks models. At first, a dataset containing near to 1000 images has been collected, then interesting objects on images have been labeled with bounding boxing option. To build, train, evaluate and deploy the relevant model, TensorFlow Object Detection API with Python has been employed. MobileNet v2 pre-trained model has been leveraged for this task. In the trial experiment with four sign classes (A, B, C, E) and 5000 step numbers 15.2% training loss and 83% evaluation mean average precision results have been obtained. In the next step of model deployment experiments with all 24 static signs of AzSL, 49700 and 27700 steps (180 and 100 epochs, respectively) 6.4% and 18.2% training losses, 66.5% and 71.6% mAP outcomes gained, respectively.
A Framework for Neural Network Architecture and Compile Co-optimization

The efficiency of deep neural network (DNN) solutions on real hardware devices are mainly decided by the DNN architecture and the compiler-level scheduling strategy on the hardware. When we try to fully exploit the underlying hardware and obtain the optimal tradeoff between DNN accuracy and runtime performance, we discovered that the two optimization goals of DNN architecture and scheduling policy are intimately related to each other. However, current hardware-aware Neural Architecture Search (NAS) methods primarily focus on the DNN architecture search process, ignoring the effects of various compiler-level scheduling strategies (e.g., graph-level optimization, loop transformations, parallelization, etc.) on network candidates being evaluated in the search process. As a result, they may overlook the true-optimal DNN implementations on hardware, which can only be discovered by trying-out different combinations of scheduling strategies and DNN architectures. This work proposes a NAS framework (CHaNAS) that searches for not only the network architecture but also the dedicated compiler-level scheduling policy, as the optimal co-design solution on the target hardware. We propose to use a block-based pre-scheduling methodology to reduce the co-design search space and enable the automatic generation of the optimal co-design, including the network architecture and the tensor programs that practice the scheduling policy. Further, we introduce a new search objective function based on the generalization gap to prevent the selection of architectures that are prone to overfitting. We evaluate CHaNAS on Imagenet on different hardware back-ends against the state-of-the-art hardware-aware search method based on the MobileNet-v3 search space. Experimental results show that the co-design solutions obtained by ChaNAS show up to 1.6×, 1.9×, and 1.7×, 24 performance boost on NVIDIA P100 GPU, Intel Xeon 8163 CPU, and Samsung Note 10 Mobile, respectively, over the baselines of the same-level accuracy.
Meta-Learning, Fast Adaptation, and Latent Representation for Head Pose Estimation

Head pose estimation is used in a variety of human-computer interface applications, like stare tracking, driving assistance, impaired assistance, and entertainment. Advances in convolutional neural networks have a considerable improvement in the performance of head pose estimation. However, difficulties in capturing well-labelled head pose data and differences in the facial features of different persons make them difficult to use. This work proposes a meta-learning based technique for head pose estimation problem in BIWI head pose dataset. An approach to learning latent representation of head pose features using variational autoencoder is implemented. Then a fast, adaptable head pose estimator is trained using meta-learning in a few-shot settings. Model agnostic meta-learning (MAML) algorithm has been deployed for training a head pose estimator. Mean Average Error (MAE <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">avg</inf> ) of 7.33 is achieved in predicting head pose angles in one-shot settings. After meta-training, the optimized model is used to analyze fast adaptation in a test set that has been separated from the BIWI head pose dataset. We begin with the trained network’s optimum parameters and optimize the inner loop for quick adaptation. The optimized model can predict accurate head poses using as few as 10 gradient descent steps in the unseen set of tasks sampled from the test set.