Reviewerly/exHarmony huggingface dataset:
Masked Deep Face Recognition using ArcFace and Ensemble Learning

With advancements in technology, human biometrics, especially face recognition, has witnessed a tremendous increase in usage, prominently in the field of security. Face recognition proves to be a convenient, coherent, and efficient way to identify a person uniquely. Face recognition systems are trained generally on human faces sans masks. With the ubiquitous use of face masks due to the ongoing COVID-19 pandemic, face recognition becomes a daunting challenge. In this paper, the deep learning architectures, namely MobileNetV2, DenseNet201, ResNet50V2, and VGG16 with the ArcFace loss function, were trained on the newly created dataset called "MaFaR", which consists of a mixture of masked and unmasked images of 75 distinct individuals, and ensemble learning techniques have been used to improve the performance, achieving an accuracy 93.65%.
Detection of Wildlife Animals using Deep Learning Approaches: A Systematic Review

The detection of animals, in particular wildlife animals, is important to monitor their distribution for the conservation of animal life and to address a broad range of questions of animal researchers, such as in the study of ecosystem function and behavioural ecology, analyse the growth and development of animals, understand population dynamics, and discover the factors influencing animal movements. Researchers use camera traps that are activated when an animal enters their field, allowing them to collect millions of images of animals without disturbing them. Machine learning methods, particularly convolutional networks, have quickly risen to prominence as the preferred way for detecting and recognizing animals in camera trap images. This paper examines the major deep learning ideas relevant to the detection and recognition of wildlife animals, as well as the contributions to the field, the majority of which have been published recently. We survey the use of deep learning techniques for automated animal recognition, segmentation, and detection and provide a concise analysis and comparison of these approaches. The open challenges and prospective research directions are discussed.
Reviewing System using Exploratory Data Analysis and Ensemble Machine Learning Algorithms

In the fast-growing technological advancements, every business venture is thriving for success. Irrespective of the business’s paradigm, each one has a reviewing system that envisages the working throughput of the system. The impact justifies the company’s current commercial state and also helps to bring in new customers. The proposed exploratory data analysis shows the relationship between the reviews on textual and non-textual parameters aimed to work on a user-based analysis. In this paper two different datasets are analyzed where the reviews are predicted based on extensive exploratory data analysis and feature engineering. This paper focused on examining how reviews are influenced based on the independent parameters from Zomato and Yelp datasets. These datasets have huge geographical differences that allowed us to obtain various diverse users-review coming from different backgrounds, cultures, and heritage. After feature construction, the predictive model is analyzed by applying supervised and ensemble machine learning algorithms such as Linear regression, Logistic regression, Decision trees, RandomForest, XGBoost, and GradientBoosting. The inferences obtained from both datasets gave us coherent results that validate the experimental procedures, extend the same analogy in different business paradigms, and offer a comprehensive prediction.
A Sinhala Natural Language Interface for Querying Databases Using Natural Language Processing

In this paper, the author presents SinSQLFinder, a Sinhala natural language user interface for generating SQL queries. Data is the heart of the decision-making process in every business, every organization, every government office. But at the same time working with data stored in the databases require special technical skills like Structured Query Language (SQL). It is an identical problem that the non-technical people are facing difficulties while retrieving data from the databases. In Sri Lanka, the native language of the majority of people is Sinhala and a considerable amount of e-governance applications use relational databases. Therefore, to manipulate data from such database applications easily, non-technical users who are more confident with the Sinhala language, need a solution to agree with a simple sentence in Sinhala and generate a valid SQL query. Nevertheless, the main goal of this research has been to establish a strong link between the Sinhala language and structured query language. Because of the language’s complexity, a unique method was developed to translate Sinhala language questions into structured query languages.
DMF-MPC: A Dual-Stage and Multi-Step Forecasting Model Based Cooling System Control Method for Data Center Energy Cost Minimization

Nowadays, surveys show that the cooling system accounts for 30&#x0025; of the energy consumption of data centers. The challenge of designing an effective cooling control system to minimize data center energy consumption cost as much as feasible while maintaining normal operation is a pressing one to address. The traditional cooling control method produces excessive cooling energy consumption, which causes a large amount of energy waste and economic expenses; Moreover, the existing Model Predictive Control (MPC) method based on the physical model has high modeling difficulty and model migration cost, and can only provide short-term control strategy. In this paper, we propose an MPC method based on a dual-stage and multi-step forecasting model (DMF-MPC), which can forecast the cooling system energy consumption in data centers accurately and provide a cost-saving control strategy of the cooling system for the next 24 hours on a combination of the peak-valley time-of-use (TOU) electricity price. We firstly propose a dual-stage and multi-step forecasting model (DMFM), a pure data-driven deep learning model, which adopts the historical server features of data centers, future weather factors, and cooling control to forecast the cooling system energy consumption for the multiple future periods, also a multi-step iterative training method is proposed in the training process to reduce the multi-step forecasting errors. In the control part of DMF-MPC, we introduce an optimal control model for the data center cooling system based on DMFM. The cost-saving control strategy for the next 24 hours under the peak-valley TOU electricity price standard can be obtained by solving the model through optimization algorithms. Finally, the effectiveness of the method is verified by validating DMF-MPC in a real campus high-performance computing (HPC) data center located in China. Our method achieves a 15&#x0025;-25&#x0025; cost reduction in the experiment.
Jyane: Detecting Reentrancy vulnerabilities based on path profiling method

Ethereum is essentially a transaction-driven state machine, and a smart contract is a piece of executable code on Ethereum. Compared with the scripting language on Bitcoin, the smart contract language solidity, which is Turing-complete and the ex-pressive capabilities are very powerful. However, this attribute also brings many potential security threats, vulnerabilities, and various other issues. In this paper, we propose a novel smart contract security technology, named Jyane, to detect the Reentrancy vulnerability, which is one of the most threatening vulnerabilities to smart contracts. More importantly, Our tool-Jyane is the first path profiling solution for smart contracts. Firstly, we use EVM (Ethereum Virtual Machine) binary bytecode to construct control flow graphs (CFG), then use the improved Ball-Larus Path profiling algorithm (BLPP) to generate IDs for acyclic paths. Finally, after profiling the constructed paths, the suspicious paths can be detected successfully. We evaluate Jyane and other technology through comprehensive test and comparison; the results show that Jyane can profile the actual execution path of smart contracts to detect vulnerabilities with a low false-positive rate accurately. From the results of the evaluation, Jyane marked 27 of 1,226 Ethereum smart contracts selected in 2016 and 2017 as vulnerable contracts, included the vulnerability of the DAO contract which once led to a $60 million loss. Furthermore, compared with some other existing detection tools, Jyane shows broader detection range for Reentrancy vulnerabilities with lower time overhead.
Organs-At-Risk Segmentation in Medical Imaging Based on the U-Net with Residual and Attention Mechanisms

In this paper, we proposed a new organs-at-risk segmentation method for Medical images of heads and necks based on the U-Net with residuals and attention mechanism. A Project &#x0026; Excite (PE) block combined with a residual method is proposed as Residual-PE Block in the U-Net encoding stage in order to enhance the feature expression ability. Attention Gates (AGs) are introduced in the decoding stage to increase the relevance of feature weighting in accord with segmentation tasks. The proposed method has been evaluated through a set of real world medical images including 22 organs at risk in the head and neck. Experimental results showed that our proposed method improved the average segmentation accuracy by 3&#x2013;4&#x0025; compared with current existing methods. The average Dice Similarity Coefficient (DSC) score on the segmentation of the 22 types of head-and-neck related organs at risk is 79.5&#x0025; and the average 95&#x0025; Hausdorff Distance (HD) is 6.99mm. It demonstrates that this approach is promising for clinical doctors to improve their accuracy and efficiency in radiotherapy.
A Study of the Landscape of Security Issues, Vulnerabilities, and Defense Mechanisms in Web Based Applications

web application is application software that runs on a web server, unlike computer-based software programs that are run locally on the operating system of the device. Web applications are accessed by the user through a web browser with an active network connection. Their existence has been enabled in the contemporary era of the multifaceted World Wide Web (WWW), whose advancement to incorporate varied components and technologies has affected the consistent development of web applications. Currently, 4.66 billion people are active Internet users as Worldometer stated. However, according to the latest data breach report by IBM, the cost of a data breach in 2021 is US$ 4.24 million per incident on average, this is a 10% rise from the average cost in 2019 which was $3.86 million per incident on average. This paper discusses the diverse security issues, vulnerabilities, and defense mechanisms associated with web applications. The paper illuminates a comprehensive landscape on evolving and emerging web application vulnerabilities and reveals ways to attack, evade, and detect pattern mechanisms within all crucial web threats.
An Intelligent System for Detecting People from Images and Videos Provided by IoT Devices

Today, thanks to artificial neural networks, the field of computer vision is undergoing an unprecedented revolution. The rise of deep learning and the advent of big data have both facilitated the construction of neural networks, not only deep, but also increasingly efficient. This has resulted in the automation of Internet of Things (IoT) technologies and devices. In addition, there are more and more disasters across the world where it is sometimes difficult to find victims in disaster areas. The contribution of IoT technologies is very important in this field. Thus, we propose in this work an intelligent system for detecting people from images and videos provided by IoT devices. Thus, we initially list some open source datasets used for object detection tasks. Then, we propose four person detection models based on the YOLO architectures. After evaluation, we obtained good performance results in terms of Average Accuracy.
An Internet of Things System with a Social Robot to Reduce the Risk of Hypothermia and Dehydration Occurring in Older Adults

Many older adults might be at risk of developing hypothermia or becoming dehydrated, both of which can be dangerous conditions requiring hospitalization. This paper describes the development of a novel support system, Hypothermia and Dehydration Advising Companion (HyDeAdCo), that integrates off-the-shelf technologies with an IoT infrastructure, and a natural language interface provided via a social robot to specifically target reduction of the risk of hypothermia and dehydration occurring among older adults.
TunnelScanner: A Novel Approach For Tunnel Mixed Traffic Classification Using Machine Learning

With the explosive growth in the use of tunneling protocols, network management and network security are facing huge challenges. Tunnel traffic classification is the most basic step to maintain network security. The studies on the classification of tunnel traffic is mainly divided into machine learning methods and deep learning methods. However, existing studies only focus on the identification of a single application of the tunnel, assuming that the user only uses one application in the tunnel. Unfortunately, the assumption of a single application is not reasonable in the tunnel. The user may use lots of applications within a time in the tunnel. Moreover, the application traffic is mixed and overlapped in the tunnel. As a solution, we propose TunnelScanner, a novel framework for the identification of two mixed applications in the tunnel. We extracted novel features named maxcum from the tunnel mixed traffic, and then input the extracted features into the traditional machine learning model to train the classifier, to achieve high accuracy and low training overhead. We collected nine types of mixed traffic in three types of tunnels (SSH, SSL, L2TP) to verify the effectiveness of TunnelScanner. The experimental results demonstrate that TunnelScanner is superior to the state-of-the-art methods in terms of accuracy, recall, and stability.
A Comparative Study on Hyperparameter Optimization Methods in Software Vulnerability Prediction

Hyperparameter optimization (HPO) is the procedure to find the optimal hyperparameters for predictive modelling. In order to build an efficient software vulnerability prediction (SVP) model, effective HPO method is required. Recent studies have explored the area of machine learning for SVP models&#x0027; construction. Different machine learning algorithms have distinct hyperparameters to be tuned. In this paper, we have compared four HPO methods for six machine learning based SVP models using an open-source public dataset &#x2018;Drupal&#x2019;. The performance of the models is evaluated by the accuracy of the model and the computational time. Experiments are performed using various python libraries such as sklearn, skopt, hyperopt, and tpot. After comparative analysis, we found that bayesian optimization and random search has shown the performance improvement for SVP models with effective computational time.
Exploring Self-supervised Learning for Radio Signal Recognition

Data annotation is indispensable but difficult and expensive for supervised learning. While Radio signal is ubiq-uitous and available naturally, few approaches are designed to utilize large amounts of unlabeled data onto radio signal recognition. To handle this problem, we propose a self-supervised representation learning framework with two steps: pre-training for extracting features and fine-tuning for radio signal recognition. The radio signal from our dataset Rayl-0.5 contains 10 modulation methods for signal recognition task. Based on careful inspection of underlying regularities in the data, our model pre-trains radio signal without modulation methods labels, and provides competitive results by linear evaluation on recognizing radio signal with only 10&#x0025; labels. A strict baseline method is considered using supervised learning for recognition task, which training radio signal dataset with 100&#x0025; and 10&#x0025; labels. Finally, our self-supervised model Self-RadioNet shows better performance compared to supervised learning methods trained for 10&#x0025; labeled data.
A Study of Software Clone Detection Techniques for Better Software Maintenance and Reliability

Major problem in the development of software development is the presence of duplicate code that has a great impact on the overall affect the maintainability of the software. Clone detection technique applied with the core objective to identify the software codes which are identical. Various approached had been proposed in past by various researchers based which are based on text based, and token-based techniques. However, the proposed approaches were not considered very reliable methods due to the inability to find out syntactic differences between programs. Highly effective way to identify syntactic difference is through usage of abstract syntax tree. There are numerous ways to find similarity between two programs. In the proposed work, proposed software clone detection technique software code would be represented in the form of metrics affecting maintenance and reliability of opensource software. Afterwards, features extraction would be done in the form of flexible vectors of different forms to detect different types of clones. Proposed technique would be based upon adaptive prefix filtering on sets of vectors to detect similarity among the vectors. Similarity index detected among the vectors would be used to define given codes as code clones.
On the Practical Uses of Experimental Adversarial Neural Cryptography

With the rise of generative adversarial networks (GANs), many areas have seen remarkable improvements, e.g., computer vision, natural language processing, and the medical field. Notably, cryptography has been fueled by GANs producing adversarial neural cryptography (ANC). However, in these five years, ANC has little documented experimentation and applications that can be used in the real world. This paper aims to perform experiments on ANC to verify if the current status of ANC is ready for practical implementations of symmetric-key encryption. In our investigation, we assess several entities in ANC during training, encryption, and decryption of an ANC model, including decryption accuracy analysis. Furthermore, we study the resources required for deployment using different quantization techniques to reduce the size of an ANC model and its impact on performance and decryption accuracy. Our study provides enough data for offering practical advice for using and implementing ANC models.
Hybrid Approach for Virtual Machine Optimization using BAT Algorithm in cloud

The virtual machine (VM) in cloud is overutilized if it takes the total execution time of workflow more than the maximum acceptable limit. In this paper, bat Algorithm is hybridized with heuristic techniques to identify the VMs under overload condition and to optimize their utilization. In proposed model, the bat algorithm parameters (Frequency, velocity, loudness, pulse rate) are seeded by the heuristic approaches (HEFT and PEFT) to optimize makespan and cost of executing the workflows. The model is implemented in CloudSim and results are analyzed with reference to state-of-the-art ACO and PSO metaheuristics.
Deep-Learning Side-Channel Attack Against STM32 Implementation of AES

Deep-Learning Side-Channel Attacks (DLSCAs) have become a realistic threat to cryptographic algorithms, such as Advanced Encryption Standard (AES). Since the encryption has to run in hardware at some point to actually do things, there might be some unintentional physical leakage, such as the different amount of power consumed by the victim device. By using deep-learning models to analyze the power traces, the attacker is able to derive the secret key. In this project, we implement a real deep-learning based attack to against a STM32 implementation of AES. We apply four different types of neural networks, MLP, CNN, LSTM and RNN, to classify traces. Afterwards, we evaluate to which extent different types of models could make the attack more efficient.
A lightweight Ontology for real time semantic correlation of situation awareness data generated for first responders

In case of an emergency, the immediate action of the first responders is crucial for saving human lives. Their intervention requires specialized instrumentation, available at all times and easily accessible, which meets stringent requirements in terms of detection accuracy, quick localization, and reduction of false alarms. This work proposes a novel ontology-based methodology which integrates data from IoT devices in the frame of a Situation Awareness (SA) semantic model. The proposed model aims at providing the conceptual representation of core entities, which will be represented by concepts and will cover specific aspects of the SA domain, such as proper decision making during the course of an emergency operation, conceptual representation of critical information required for such tasks and of important information flows which potentially exist between involved actors. Finally, we set the scene towards validating the efficiency and efficacy of our proposed model directly in the field, through seven (7) use cases.
An SEI-Based Identification Scheme for Illegal FM Broadcast

Illegal radio signals do harm to social security and electromagnetic safety with low cost and huge economic benefits. Therefore, illegal radio signal monitoring is an important part of electromagnetic safety monitoring. In this paper, we propose an identification method, characterized by radio frequency (RF) fingerprint, for illegal frequency modulation (FM) broadcast. The idea of specific emitter identification (SEI) is introduced in broadcast monitoring. Illegal FM radio station is considered as the unique emitter individual which is different from the legal radio stations. The individual features are extracted as RF fingerprints of the FM radio stations including fractal features and refined composite multiscale dispersion entropy (RCMDE). Illegal FM broadcasting signals can be identified and detected based on local outliers factor (LOF) algorithm. Experimental results show that the SEI-based method can effectively detect illegal FM broadcasting signals. It is capable of achieving nearly 99&#x0025; accuracy in legal and illegal FM radio station identification tasks.
Research on Single Image Deraining Algorithm Based on Euclidean Distance with Adaptive Progressive Residual Network

In rainy days, rain streaks have various directions, shapes and densities. In order to achieve the purpose of rain removal, the deep network structure becomes complicated, thus reducing the efficiency of rain removal. The same model have an incomplete rain streaks removal problem for various rainy images. This paper proposes the adaptive progressive residual network (APRN) to solve the above problems. APRN used recursive method to achieve progressive rain streaks removal. We used the Euclidean distance to determine the distance between the derain images, and designed the Euclidean distance ratio (EDR) threshold to stop the network recursion and output final derain image. EDR compares the Euclidean distance of the de-rain images in adjacent stages, and controls the network recursion and helps the network processes rainy images more efficiently. Experiments show that APRN has better visual effects on different datasets, and the processing efficiency is improved.