Reviewerly/exHarmony huggingface dataset:
Screening and Ranking resume’s using Stacked Model

Finding the top candidates for a position is the aim of the resume screening process. The application must make use of machine learning methodologies as well as natural language processing to rate candidates in real time. Natural language processing (NLP) and machine learning methods are used to rate resumes. The output would be the resume of a top candidate, with resumes and job descriptions serving as the input. The output’s results are obtained instantly. String matching, Cosine Similarity, and TF-IDF will all be done with Mon. Existing systems are straightforward and efficient, but they lack precision, efficiency, and processing capacity.
Prediction of Rheumatoid Arthritis Susceptibility Using Gene Mutation Rate

Rheumatoid Arthritis (RA) is an incurable condition which to the present day is an ailment without any cure. In this disease immune system of the body attacks its own healthy tissues perceiving them as foreign. RA without any cure causes damages to joints which are irreversible and very painful. The only solace to the affected patients who suffer from extreme pain is redemption with a few drugs. Disease onset prediction and further monitoring of disease activity are very crucial stages for RA.RA susceptibility has always had relationship with complex genetic characteristics. The proposed methodology emphasizes on early prediction of rheumatoid arthritis by applying the machine learning technique SVM based on HLA-DRB4 gene sequence and mutation rate. The technique facilitates in early prediction of rheumatoid arthritis by comparing pair-wise DNA nucleotide point mutation on one of the primary RA associated candidate gene HLA-DRB4 (which resides in chromosome location 6) with its mutated gene samples, thus calculating the mutation rate. On the basis of mutation rate value computed the sample bearing individual may be recommended to take up RA susceptibility test. This approach of applying linear kernel SVM machine learning technique on gene data substantially increases the chances of early disease prediction, which will be very beneficial to the healthcare community and people in general which in turn can prevent people from going through extreme pain and handicapped lifestyle as they can know beforehand whether they are prone to get rheumatoid arthritis.
Earthquake Damage Prediction and Rapid Assessment of Building Damage Using Deep Learning

One of the most expensive natural disasters that affect people, earthquakes occur suddenly. As a result, earthquake prediction has grown in importance and difficulty for humanity. Although numerous existing approaches attempt to handle this problem, the majority rely on seismic indicators created by geologists or feature vectors extracted by the deep learning techniques to describe an earthquake for the earthquake prediction. Combining these two categories of characteristics to enhance ultimate Earthquake prediction performance is still a challenge. In order to achieve this, we put forth a deep learning model that successfully fuses explicit and implicit information for earthquake prediction. Here, we use a Convolutional Neural Network to extract implicit features while using eight precursory pattern-based indicators as the explicit features. After that, an attention-based approach is suggested to effectively combine these two categories of traits. A dynamic loss function is the additionally created to address the imbalance of category in seismic data. On a test picture dataset, the performances of multiple CNN models are contrasted. In order to classify earthquake damage, the model is further developed as a web application. Damage assessment values, which are determined using the Convolutional Neural Network model and gradient-weighted class activation mappings, are used to determine the extent of the damage. The web-based program can efficiently and automatically categorize earthquake-related structural damage, making it appropriate for decision-making in emergency response, resource allocation, and policy formation.
BREAST AI: Low Cost, Explainable Artificial Intelligence Based App for Efficient Diagnosis of Breast Cancer in Developing Areas

Breast cancer is the most common cancer among women. Accurate diagnosis of breast cancer is crucial for successful treatment. However, many women in developing areas do not have access to similar medical resources as those in more developed areas, resulting in poor outcomes. Disparities range from a lack of mammograms in rural settings, as they are solely applicable to the breast, to a lack of trained radiologists to diagnose breast cancer. The goal of this research was to develop an artificial intelligence-based app for the accurate diagnosis of breast cancer via ultrasound scans without the need for an internet connection. The artificial intelligence model developed was a fine-tuned MobileNet-V2 convolutional neural network, trained on 624 ultrasound scans and tested on 156 scans from the Baheya Hospital in Egypt. The testing accuracy was 87.50%. To understand how the algorithm was making predictions (Explainable AI), code was created to generate heat maps to highlight parts of the breast ultrasound that were most significant in helping the algorithm make its decision. The AI model was downloaded as a TensorFlow Lite file, which allows for on-device machine learning, eliminating the need for an internet connection to operate, and developed into a user-friendly app. Further work, such as using a more diverse dataset, trying different convolutional neural network architectures, building a multimodal model, and implementing denoising algorithms can be done to improve this research.
Binary Bit Stream Compression Based on Mode Encoding Algorithm

After encoding audio, image, video, or text data, the output always has the form of the bit stream. Therefore, whether the bit stream can be further compressed is an interesting issue. Many ways, including direct encoding and zero-run length coding, can be applied to encode a bit stream. In this study, we propose a new way to encode the bit stream effectively and adaptively and then perform mode encoding. Experimental results show that, when the variance is not small, no matter whether the bitstream has the probability distributions of Gaussian, hyper-Laplacian, Pareto, or uniform, the proposed algorithm achieves a higher coding gain.
Research on Intelligent Scoring Algorithm for English Writing Quality Based on Machine Learning

The main writing characteristics of advanced learners in sentences are studied using the complexity analyzer of two sentence method, random forest, and logical regression machine learning algorithm. The results show that the accuracy of scoring and detection of the high-level composition is high even with the complexity of the two-sentence method, while the writing texts of advanced learners are outstanding in terms of unit length, lexical complexity, and specific sentence structure. The five most prominent syntactic features are average sentence length, the average number of sub-words contained in each word, the average total length of sub-words, the number of complex verb phrases in each subject and subordinate word, and the average number of complex noun phrases in each subword. The research result can be used as a reference for improving English composition learning ability and machine evaluation systems of composition.
Study on Phone Number based Natural Person Authentication in Digital Signature

Numerous viable techniques for digitizing processes exist with digital signatures a prime example. They can replace physical signature processes, minimize paper usage, and subsequently save transportation and delivery time costs while promoting environmental conservation. However, identifying users through accounts and passwords can expose them to the risk of being hacked. If accounts and passwords are stolen or leaked, the user's personal information and habits on social media are leaked. Therefore, the objective of this study is to establish a secure user identification method. Elliptic Curve Cryptography (ECC) is a highly secure algorithm that can be implemented in shorter keys and effectively save storage space. Therefore, we used the Elliptic Curve Digital Signature Algorithm (ECDSA) as the digital signature experimentation method. Additionally, we contemplated mobile phone number authentication as the user identification approach. Using Remote Authentication Dial-In User Service (RADIUS) effectively reduced the risk of user accounts and passwords by hackers and bolster natural person authentication.
A Natural Language Understanding Model Based on Encoding Fusion For Power Marketing Indicator Answering

Accurate understanding of user questions is the core of a domain oriented task oriented dialogue system. To apply the Natural Language Understanding Model (NLU) to power marketing indicator Q&A, the first is to define the NLU task schema based on domain background knowledge, and manually annotate a training dataset for model training. Due to the lack of historical conversation data, manually organizing problem and annotating is labor-intensive. Insufficient sample size affects the performance of the model. We further propose an approach to improve the end-to-end NLU model with marketing domain triple knowledge, which provide rich contextual information for the slot representation. During the NLU model coding stage, the representation of entity relationships is incorporated into the token coding, enhancing the model's understanding of domain terms that do not appear in the samples. Practice has shown that introducing domain knowledge do make up for the lack of training samples and significantly improve the accuracy of slot value recognition.
Attribute-Based Searchable Encryption in Edge Computing for Lightweight Devices

The development of the Internet has dramatically facilitated the present society. While a large amount of data is produced, private data security involves users' data rights. Traditionally, attribute-based encryption (ABE) still needs to improve efficiency in the face of a large amount of data. The introduction of edge computing environments reduces the operating costs of lightweight devices by outsourcing some encryption and decryption operations. Therefore, we design an efficient attribute-based encryption scheme under edge computing that supports keyword search. Data owners and users do not disclose private information to the cloud during the search phase. Our work considers the computational efficiency of ordinary and lightweight users, and partial operations can be outsourced according to the demand during decryption, which is more flexible and applicable to practical applications. The scheme also supports efficient attribute revocation and ciphertext update in the cloud environment. Moreover, the proposed algorithm has been proven to be IND-sCP-CPA security and IND-CKA security. The work compares our algorithm with some typical schemes in terms of storage overhead and computation cost in the end. The performance analysis results indicate that the proposed work is efficient and suitable for edge computing.
DIT and Beyond: Interdomain Routing With Intradomain Awareness for IIoT

Along with the ever-increasing amount of data generated from industrial devices, cross domain (also known as Autonomous Systems, AS) data transmission problem has attracted more and more attention in Industrial Internet of Things (IIoT). As mature and widely used inter-domain routing protocols, BGP-based solutions often take the number of domains (i.e., AS hops) of each path as a criterion to make routing decisions, which is simple and effective. However, such protocols can only meet reachability requirements while ignoring performance requirements. That is, the path with the minimum AS hops will be selected to carry flows, even if the actual performance of this path does not meet the transmission requirements due to the unawareness of intra-domain information on that path. But it is not impractical to directly access intra-domain information for making better routing decisions given data privacy concerns. In this paper, we propose M-DIT, which can make inter-domain routing decisions with the assistance of desensitized intra-domain information for multiple-requirement transmissions. To do so, we design a homomorphic encrypted-based private number comparison scheme to export intra-domain information securely and thus assist in routing decisions. The results of some experiments based on 5 real topologies (ATMnet, Claranet, Compuserve, NSFnet, and Peer1) with thousands of inter-domain flows demonstrate that reduced flow completion time by about 60% or selected high bandwidth paths flexibly for inter-domain routing for IIoT scenarios.
Stealthy Backdoor Attack Against Speaker Recognition Using Phase-Injection Hidden Trigger

Deep learning has achieved significant breakthroughs in speaker recognition, driven by continual advancements in foundation models. However, malicious third-party platforms have introduced a severe security concern through backdoor attacks, in which attackers can manipulate a model to output a specific label by implanting a trigger. Existing speech backdoor attack methods typically utilize fixed and unnoticeable perturbations as triggers, but these may still be audible and thus detected during training and inference stages. To overcome this limitation, we propose a novel backdoor attack paradigm (PhaseBack) injecting triggers in the phase spectrum. PhaseBack exhibits sufficient stealth by leveraging the fact that the human ear is insensitive to phase information. Besides, injecting partial perturbations in the frequency domain results in global perturbations throughout the time domain, making the attack more effective. Extensive experiments on the Voxceleb1 dataset demonstrate the effectiveness and stealthiness of PhaseBack. Moreover, it has strong resistance to bypass several defense methods.
A Feasible and Explainable Network Traffic Classifier Utilizing DistilBERT

While user-oriented service industries are rapidly growing, various network devices provide these services through different access paths.Accordingly, the network flow is also increasing explosively.As demand for management related to limited network resources increases, the network traffic classification grows to prominence.Usually, a quick classification task was possible with hundreds of data composed of dozens of features.Afterward, deep learning models have proliferated owing to an outstanding performance that overwhelms existing performance based on hundreds of thousands of features and data.However, the deep learning models showing one of the best performances cannot be free from two facts.One is a lot of time and resource consumption.The other is an uncertain explanation of the process.We solved these problems.Firstly, we used two methods to overcome resource constraints.We modified the DistilBERT applied with knowledge distillation for using a compressed model and securing a remarkable performance.We used a lightweight packet with a header and partial payload for feature reduction.Consequently, our XENTC can process four multi-attribute packets simultaneously and effectively by removing the superfluity of features.And it achieved 97.0~98.1% F1 scores.The required time to classify a packet using a trained model is 0.0093 seconds.Therefore, it can be one of the feasible solutions.Secondly, to approach humanunderstandable XAI, we analyzed the relationships between the features by associating them with the packet structure.At the specific point of the model's finished training, it was revealed what the important features of the packet were by counting the Top-5 number of times among the attention values.In addition, we visualized the classification performance of the model using t-SNE to enable intuitive understanding.
On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based Approach

Machine learning is becoming ubiquitous. From finance to medicine, machine learning models are boosting decision/making processes and even outperforming humans in some tasks. This huge progress in terms of prediction quality does not however find a counterpart in the security of such models and corresponding predictions, where perturbations of fractions of the training set (poisoning) can seriously undermine the model accuracy. Research on poisoning attacks and defenses received increasing attention in the last decade, leading to several promising solutions aiming to increase the robustness of machine learning. Among them, ensemble-based defenses, where different models are trained on portions of the training set and their predictions are then aggregated, provide strong theoretical guarantees at the price of a linear overhead. Surprisingly, ensemble-based defenses, which do not pose any restrictions on the base model, have not been applied to increase the robustness of random forest models. The work in this paper aims to fill in this gap by designing and implementing a novel hash-based ensemble approach that protects random forest against untargeted, random poisoning attacks. An extensive experimental evaluation measures the performance of our approach against a variety of attacks, as well as its sustainability in terms of resource consumption and performance, and compares it with a traditional monolithic model based on random forest. A final discussion presents our main findings and compares our approach with existing poisoning defenses targeting random forests.
Multilevel Attention Siamese Network for Keypoint Detection in Optical and SAR Images

Optical and synthetic aperture radar (SAR) image keypoint detection is an important foundation for multimodal remote sensing image matching. The influence of nonlinear radiometric differences and geometric deformation between optical and SAR images leads to low repeatability of existing keypoint detection methods. To address the problem that existing keypoint detection methods cannot provide the required homonymous points for heterogenous image matching, we propose a keypoint detection method (SKD-Net) for optical and SAR images, and improve it in terms of both network structure and network optimization. First, we propose a multilevel attention Siamese network, which is composed of multiple convolutional modules and transformer modules with shared weights to extract common features at different levels for keypoint detection. We introduce a transformer module in the keypoint detection pipeline and fuse shallow and deep features to obtain more spatial and rich semantic information to facilitate heterogeneous image keypoint detection. Then, to ensure that the detected keypoints have more homonymous points and localization accuracy, we propose a position consistent loss. Unlike previous loss functions, our designed position-consistent loss function takes the differences between heterogeneous image score maps into account, and it autonomously selects the optimized correct point pairs to enable the network to perform correct learning. Finally, extensive experiments show that our detection method outperforms the current state-of-the-art keypoint detection methods in terms of repeatability, localization accuracy, and matching performance. Our source code is available at https://github.com/zhangschen/ SKD-Net.
A Geo-Indistinguishable Context-Based Mix Strategy for Trajectory Protection in VANETs

Vehicular Ad-Hoc Networks (VANETs), the backbone of Intelligent Transportation Systems (ITS), can significantly improve traffic efficiency and road safety. However, preserving privacy during data transmission and storage is a significant issue in vehicular communications. To prevent malicious adversaries from tracking vehicles from the beacons they broadcast, pseudonym-changing or obfuscation strategies have been proposed in literature. However, it is challenging to protect the privacy of vehicles while simultaneously maintaining the quality of service of location-based services. In this article, we present a new context-based mix strategy that provides a higher privacy level and reduces information loss during the pseudonym-changing process. A global passive adversary model is developed to evaluate its performance and the effectiveness of the proposed mechanism is demonstrated. Finally, a uniform framework to evaluate different strategies for vehicular privacy is developed and used to compare their strengths and weaknesses in different scenarios.
Side Channel Security Oriented Evaluation and Protection on Hardware Implementations of Kyber

The emergence of quantum computing and its impact on current cryptographic algorithms has triggered the migration to post-quantum cryptography (PQC). Among the PQC candidates, CRYSTALS-Kyber is a key encapsulation mechanism (KEM) that stands out from the National Institute of Standards and Technology (NIST) standardization project. While software implementations of Kyber have been developed and evaluated recently, Kyber’s hardware implementations especially those designed with parallel architecture, are rarely discussed. To help better understand Kyber hardware designs and their security against side-channel analysis (SCA) attacks, in this paper, we first adapt the two most recent Kyber hardware designs for FPGA implementations. We then perform SCA attacks against these hardware designs with different architectures, i.e., parallelization and pipelining. Our experimental results show that Kyber designs on FPGA boards are vulnerable to SCA attacks including electromagnetic (EM) and power side channels. An attacker only needs <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$27 \sim 1,600$</tex-math> </inline-formula> power traces or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$60 \sim 2,680$</tex-math> </inline-formula> EM traces to recover the decryption key successfully. Furthermore, we propose two first-order IND-CPA Kyber decapsulation masking protected designs, and then we evaluate their securities and overheads. The experimental results demonstrate that the side channel security of masked Kyber designs has increased by more than 10x.
Bat Planner: Aggressive Flying Ball Player

In this letter, an aggressive quadrotor ball playing system called Bat is proposed, whose goal is to intercept a flying ball and volley it towards a designated target. Aggressive means Bat operates the quadrotor aggressively to intercept balls that are far away and hit them to distant positions in ways that are beyond the reach of existing methods. The trajectory prediction of the ball is achieved by integrating forward the current position and velocity estimates using an extended kalman filter, and implementing cubic interpolation at the time resolution to calculate the continuous gradient for optimization. Facing the challenge of finding feasible hitting actions under extreme circumstances, we propose a two-stage planning approach, including transition point design and hitting primitive generation, with a simplified expression of uncoupled hitting actions. To obtain the best hitting motion, a trajectory optimization method is proposed, which can jointly optimize the hitting terminal states and time cost, considering dynamic feasibility and anticollision constraints. To avoid pathological hitting, a defensive rule constraint and its constraint transcription method are proposed. The largest difference from the existing methods is that Bat Planner can independently decide how to execute more aggressive keyvolleying maneuvers. A large number of simulation and real-world experiments are conducted, which prove the flying ball player can hit arriving balls from different directions and distances to arbitrary targets. To the best of our knowledge, Bat is currently the closest a quadrotor ball player has approached to human ball players' volleying ability.