Reviewerly/exHarmony huggingface dataset:
Badminton Action Analysis Using LSTM

Badminton is a popular racquet sport that involves quick movements, agility, and reflexes. A badminton action analysis is a process in which quantify the various actions performed by the player using various sensors or image processing-based approaches. In this paper, a sensor-based approach to collect player action data for various shots, analyse the same and create a Long short-term memory-based model to classify various shots. The trained Long short-term memory model has a Micro F1 score of 0.84. The Model would then generate a shot accuracy score between 1-10. A professional badminton coach also rated the shots, and a comparison was made between the score given by the coach and the ML model. Additionally, arm section-wise comparison of the shot was made. This allows for a more granular analysis of the shot. A comparison of the arm-wise shot is also conducted. It can be seen from the results that the scores given by the coach and the Long short-term memory-based ML model matched by 98.6%. So it can be concluded that such a sensor-based wearable badminton teaching technique can be deployed to enable underprivileged beginner badminton players to achieve higher shot accuracy by allowing them to correct postures during the swing action of the arm.
Fault Detection and Classification of Microgrid Based on Mode Decomposition and Extreme Learning Machine

Accurate fault detection and classification is a measure issue in a microgrid (MG). The MG often experiences shunt faults inside or outside of it, the circuit breaker connected between the utility grid and MG must immediately respond and open the circuit. If the fault is not detected accurately, it hampers the system's reliability and load performances also increase faulty line restoration costs. This research proposes a robust fault detection and classification technique based on Empirical Mode Decomposition (EMD) and Extreme Learning Machine (ELM). Energy of Decomposed current signals are used for unbiased feature extraction in presence of noise. whereas ELM is used for accurate fault detection and classification. The proposed EMD-ELM technique is validated in standard test system and found to be performing better as compared to other competitive techniques.
CSurF: Sparse Lexical Retrieval through Contextualized Surface Forms

Lexical exact-match systems perform text retrieval efficiently with sparse matching signals and fast retrieval through inverted lists, but naturally suffer from the mismatch between lexical surface form and implicit term semantics. This paper proposes to directly bridge the surface form space and the term semantics space in lexical exact-match retrieval via contextualized surface forms (CSF). Each CSF pairs a lexical surface form with a context source, and is represented by a lexical form weight and a contextualized semantic vector representation. This framework is able to perform sparse lexicon-based retrieval by learning to represent each query and document as a "bag-of-CSFs", simultaneously addressing two key factors in sparse retrieval: vocabulary expansion of surface form and semantic representation of term meaning. At retrieval time, it efficiently matches CSFs through exact-match of learned surface forms, and effectively scores each CSF pair via contextual semantic representations, leading to joint improvement in both term match and term scoring. Multiple experiments show that this approach successfully resolves the main mismatch issues in lexical exact-match retrieval and outperforms state-of-the-art lexical exact-match systems, reaching comparable accuracy as lexical all-to-all soft match systems as an efficient exact-match-based system.
A Recommendation-type Dialogue System Responding to Potential Requests in Consideration of Personal Attributes

A dialogue system that responds mechanically to given queries can be used for public relations for prospective university students. However, as a user-specific problem, collecting information from first-time entrances and passive examinations is difficult, and a conventional dialogue system may not collect sufficient information. Therefore, we developed a recommendation-type dialogue system that supports information gathering by evoking potential user requests. Based on the preference analysis results using previous and real-time query histories and recommended information from universities, information in the form of queries is recommended to users when considering their personal attributes. We evaluated the presence or absence of a recommendation function and implemented an introduction evaluation targeting public relations for those who wish to enroll in a university. The average number of queries increases by evoking potential requests, and the information gathering of the user is supported.
Intelligent Spectrum Sensing with ConvNet for 5G and LTE Signals Identification

The paper presents an intelligent spectrum sensing approach for next-generation wireless networks by exploiting deep learning, in which we develop a deep convolutional network (ConvNet) to automatically identify Fifth Generation New Radio (5G NR) and Long-Term Evolution (LTE) signals under standards-specified channel models with diversified RF impairments. In particular, we design a semantic segmentation ConvNet to detect and localize the spectral content of 5G NR and LTE in a synthetic signal featured by spectrum occupancy. A received signal is first converted by a short-time Fourier transform and represented as a wideband spectrogram image which is then passed through the ConvNet, incorporated by DeepLabv3+ and ResNet18 to improve the accuracy of pixel-wise segmentation to further increase the accuracy of signal identification. In the simulations, our ConvNet achieves around 95% mean accuracy and 91% mean intersection-over-union (IoU) at medium SNR level and demonstrates robustness under various practical channel impairments.
PASTA: Pragmatic Automated System-Theoretic Process Analysis

System- Theoretic Process Analysis (STPA) is a relatively new hazard analysis technique. Several tools supporting the STPA process already exist. However, they are mostly textual. In this paper we present a Domain Specific Language (DSL) for STPA with an automatic visualization of the STPA components. This combines the advantages of textual and graphical approaches. The DSL is implemented with open source tooling, realized as a Visual Studio Code Extension using Langium for the language server and Sprotty for the visualization. A comparison suggests that the DSL has potential to be a good alternative to other STPA supporting tools.
IRIS: a Record and Replay Framework to Enable Hardware-assisted Virtualization Fuzzing

Nowadays, industries are looking into virtualization as an effective means to build safe applications, thanks to the isolation it can provide among virtual machines (VMs) running on the same hardware. In this context, a fundamental issue is understanding to what extent the isolation is guaranteed, despite possible (or induced) problems in the virtualization mechanisms. Uncovering such isolation issues is still an open challenge, especially for hardware-assisted virtualization, since the search space should include all the possible VM states (and the linked hypervisor state), which is prohibitive. In this paper, we propose IRIS, a framework to record (learn) sequences of inputs (i.e., VM seeds) from the real guest execution (e.g., OS boot), replay them as-is to reach valid and complex VM states, and finally use them as valid seed to be mutated for enabling fuzzing solutions for hardware-assisted hypervisors. We demonstrate the accuracy and efficiency of IRIS in automatically reproducing valid VM behaviors, with no need to execute guest workloads. We also provide a proof-of-concept fuzzer, based on the proposed architecture, showing its potential on the Xen hypervisor.
ReFace: Adversarial Transformation Networks for Real-time Attacks on Face Recognition Systems

In this work, we propose ReFace, a real-time, highly-transferable attack on face recognition models based on Adversarial Transformation Networks (ATNs). Past attacks on face recognition models require the adversary to solve an input-dependent optimization problem using gradient descent making the attack impractical in real-time. Such adversarial examples are also tightly coupled to the victim model and are not as successful in transferring to different models. We find that the white-box attack success rate of a pure U-Net ATN falls substantially short of gradient-based attacks like PGD on large face recognition datasets. We therefore propose a new architecture for ATNs that closes this gap while maintaining a 10000X speedup over PGD. Furthermore, we find that at a given perturbation magnitude, our ATN adversarial perturbations are more effective in transferring to new face recognition models than PGD. We demonstrate that our attacks transfer effectively to models with different architectures, loss functions, and training procedures. ReFace attacks can successfully deceive commercial face recognition services via transfer attack and reduce face identification accuracy from 82% to 16.4% for AWS SearchFaces API and Azure face verification accuracy from 91% to 50.1%.
Weapon Object Detection Using Quantized YOLOv8

Video surveillance is essential for creating a secure and hassle-free environment in all areas of life. It helps identify theft, detect unusual events in crowded locations, and monitor the suspicious behavior of individuals. However, monitoring surveillance cameras manually is quite challenging, and thus, fully automated surveillance with smart video-capturing capabilities is gaining popularity. This approach uses deep learning methodology to remotely monitor unusual actions with accurate information about the location, time of occurrence, and identification of criminals. Detecting criminal conduct in public settings is difficult due to the complexity of real-world scenarios. CCTV cameras can record suspicious incidents in public areas, such as carrying weapons, which helps authorities to take preventive measures to protect citizens. The proposed system employs the state-of-the-art YOLOv8 model for real-time weapon detection, which is faster, more accurate, and better than YOLOv5. To ensure fast performance, the weights of YOLOv8 were quantized. In our experiments, we evaluated the performance of the YOLOv8 and YOLOv5 models for weapon detection. The mean Average Precision (mAP) value achieved using YOLOv8 was 90.1%, which outperformed the mAP value of 89.1% obtained with YOLOv5. Furthermore, by applying weight quantization to the YOLOv8 model, we reduced the inference time by 15% compared to the original YOLOv8 configuration.
Optimization Model of Knowledge Graph Reasoning Process Based on Generative Adversarial Network

The knowledge map for most of the real world is incomplete, that is, there are problems of missing real facts and containing false facts. In recent years, most of the work, such as ConvE and TransE, reasoned the knowledge map by querying the implicit knowledge related to rules or based on the path, but the inference process was affected by large-scale long-distance and complex relationships, which led to the lack of interpretability and low training efficiency of the reasoning process. This paper proposed the optimization method of the reasoning process of the knowledge map based on the confrontation network, GAPO, The R-GCN auxiliary network is introduced into the GAN network to generate mixed data with high confidence as far as possible during the period of generating negative sample data, so as to improve the discriminator's ability to distinguish true and false triple facts. At the same time, reinforcement learning algorithm is introduced to treat the reasoning process of knowledge atlas as state space, and hierarchical information is used to ensure the reliability and authenticity of the reasoning link. The obtained hierarchical information data improves the interpretability of the reasoning process to a certain extent. The experiment shows that GAPO model has better performance than ConvE and TransE in reasoning, which proves that it is effective.
Breast Cancer Detection Using Variational Mode Decomposition (VMD) and Weighted Bidirectional Extreme Learning Machine

Breast cancer is a principal cause of mortality among women globally, and early identification and diagnosis can significantly increase patients' survival rates. This paper introduces a novel approach for detecting breast cancer using Variational Mode Decomposition (VMD) and a Weighted Bidirectional Extreme Learning Machine (WBELM). The proposed technique utilizes VMD to break mammographic images into multiple modes, each signifying distinct frequency constituents. Following this decomposition, the WBELM is deployed to classify these modes into benign or malignant categories. The method's efficacy is assessed using the Digital Database for Screening Mammography (DDSM), and the simulations demonstrate a commendable accuracy rate of 97.61 %, a sensitivity rate of 97.54 %, and a specificity rate of 97.68%.
Balanced Knowledge Distillation with Contrastive Learning for Document Re-ranking

Knowledge distillation is commonly used in training a neural document ranking model by employing a teacher to guide model refinement. As a teacher may not be correct in all cases, over-calibration between the student and teacher models can make training less effective. This paper focuses on the KL divergence loss used for knowledge distillation in document re-ranking, and re-visits balancing of knowledge distillation with explicit contrastive learning. The proposed loss function takes a conservative approach in imitating teacher's behavior, and allows student to deviate from a teacher's model sometimes through training. This paper presents analytic results with an evaluation on MS MARCO passages to validate the usefulness of the proposed loss for the transformer-based ColBERT re-ranking.
Handwritten Digit Detection and Prototyping on Field Programmable Gate Array (FPGA)

Neural Networks is a subset of Machine Learning (ML) where real-world data and algorithms are used to make the machine learn to mimic the way a human learns. An Extreme Learning Machine (ELM) is a specialized form of those Neural Networks for the training of Single Hidden Layer Feedforward Network (SLFN). The ELM algorithm has been presented to be faster than many other conventional algorithms with acceptable accuracy and good generalized performance. The algorithm allows the learning to be completed in a single step, which is made possible by using a pseudo-inverse matrix instead of back propagation to obtain the trained weights. The system uses random values for its initial set of weights, which are generated using a Linear Feedback Shift Register (LFSR). For the hardware implementation, a 256-bit input register is used to store the image. After the generation of weights, an intermediate signal is set to reset, moving the Finite State Machine (FSM) to the next weight fetching state. When the next set of weights has been calculated, the maximum output is given out through one hot encoding. An ELM is trained to detect handwritten digits from an image. Then a Register transfer level (RTL) is designed and further prototyped on a field programmable gate array (FPGA). Basys 3 is the FPGA board that will be used to implement the design. The one hot encoded output can be observed on the FPGA. The experimental results show a greater software accuracy of 99% and hardware accuracy of 90% in detecting handwritten digits from the dataset.
Online Video Stabilization using Mesh Flow with Minimum Latency

Most existing video stabilization techniques are used for post-processing, where previously recorded videos are given to the model to obtain stabilized versions. Online video stabilization usually relies on sensors like gyroscopes or assumes constant motion, which is not suitable for videos with changing motions. This work introduces a video stabilization technique with just one-frame latency. The algorithm operates at the spatial level in the infrequent domain, tracking the motion of mesh vertices. Motion tracks of feature marks are combined with the nearest mesh vertex using two median gauges, assigning each vertex a smooth motion track. The proposed approach, called anticipated foster track leveling, smoothes the motion profiles by utilizing previous motions and adapting accordingly for smoother results. This method can handle changes in movement in space and time and works in real-time, allowing applications in security systems, robotics, and unmanned aerial vehicles (UAVs). When evaluated against other models, MeshFlow gives an overall good performance in all comparison metrics evaluated. Hence MeshFlow can be used as a reliable low-latency technique for real-time video stabilization in remote devices.
Entangled Pair Resource Allocation Under Uncertain Fidelity Requirements

In quantum networks, effective entanglement routing facilitates remote entanglement communication between quantum source and quantum destination nodes. Unlike routing in classical networks, entanglement routing in quantum networks must consider the quality of entanglement qubits (i.e., entanglement fidelity), presenting a challenge in ensuring entanglement fidelity over extended distances. To address this issue, we propose a resource allocation model for entangled pairs and an entanglement routing model with a fidelity guarantee. This approach jointly optimizes entangled resources (i.e., entangled pairs) and entanglement routing to support applications in quantum networks. Our proposed model is formulated using two-stage stochastic programming, taking into account the uncertainty of quantum application requirements. Aiming to minimize the total cost, our model ensures efficient utilization of entangled pairs and energy conservation for quantum repeaters under uncertain fidelity requirements. Experimental results demonstrate that our proposed model can reduce the total cost by at least 20% compared to the baseline model.
Performance Analysis of 2D-DCT based JPEG Compression Algorithm

Image Compression is an important process in digital image processing, allowing for the reduction of data size while maintaining image quality. The technique of Discrete Cosine Transform (DCT) is commonly employed in the compression of images, which involves transforming an image from the spatial domain to the frequency domain. Further, the image is quantized, and encoded. In this paper, the effectiveness of DCT for image compression is examined and the compressed image quality is analyzed using Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) metrics. Results demonstrate that DCT is an effective method for image compression, providing high compression ratios while maintaining reasonable image quality. Using the algorithm discussed in this paper, for a Quality factor (Q-factor) of 50, a compression ratio of 26.42:1, PSNR of 31.95 dB, and MSE of 0.0007 was achieved.
Intrusion Injection for Virtualized Systems: Concepts and Approach

Virtualization is drawing attention due to countless benefits, leaving Hypervisors with the paramount responsibility for performance, dependability, and security. However, while there are consolidated approaches to assessing the performance and dependability of virtualized systems, solutions to assess security are very limited. Key difficulties are evaluating the system in the presence of unknown attacks and vulnerabilities and comparing the security attributes of different systems and configurations when an intrusion occurs. In this paper, we propose a novel concept and approach of intrusion injection for virtualized environments, which consists of directly driving the system into the erroneous states that mimic the ones resulting from actual intrusions (in the same way errors are injected to mimic the effects of residual faults). We present a prototype capable of injecting erroneous states related to memory-corruption in the Xen Hypervisor to show that the concept and approach proposed here are feasible. The prototype is evaluated using publicly disclosed exploits across three different versions of Xen. Results show that our tool can inject erroneous states equivalent to those resulting from attacks that exploit existing vulnerabilities, even on versions where those vulnerabilities do not exist.
Coordinate-Based Geometric Features and Nearest Neighbor Performance in 2D Facial Classification

Geometric feature is one of many kinds of feature that is used in facial recognition system. This feature represents the relative position between facial objects. In recent days, those relative positions are represented by facial landmarks. Many extracted features from facial landmark had been used in facial recognition, except the coordinate-based feature. This paper transforms raw facial landmarks coordinate into coordinate-based geometric features. The raw two dimensions facial landmarks coming from 68-points facial landmark dataset. The feature extraction process utilizes two points of outer eye (OE), right OE is placed at (0,0) and the left OE is placed at (100, 0) using translation, rotation, and scaling matrices. There are 208 class in classification process. This process involves 208 subjects, where each subject has one file in probe image and nine files in gallery images. The k-NN (nearest neighbor) with k=1 is chosen as classification method. On test session, the use of 11 to 17 features is resulting high recognition accuracy (more than 90%) with 94.23% at the peak. Compared to other studies which use triangular-based feature, angular-based feature, and distance-based feature, coordinate-based feature gives better accuracy.
Epileptic Seizure Classification Using Adaptive Sine Cosine Algorithm-Whale Optimization Algorithm Optimized Learning Machine Model

Epileptic seizure leads to the unconsciousness of the brain due to the lack of sleep, toxic consumption mainly. Now a days the death rate becomes high due to the negligence of the people who suffered from the seizure. The diagnosis of epileptic seizure at the early stage is essential. The manual diagnosis of detection and classification of seizure is difficult for radiologists. Several researchers have proposed automatic detection and classification of seizure, but somehow failed in detecting and classifying seizures related the computational time and accuracy. We are proposing a novel hybrid using Adaptive Sine cosine Algorithm-Whale Optimization Algorithm optimized Extreme Learning Machine (ASCA-WOA-ELM) model for classification of epileptic seizure. The hybrid ASCA-WOA technique is proposed to optimize the weights of the ELM model to improve the performance of the conventional ELM model. The EEG signals from University of Bonn dataset are considered for the research. First, the statistical features are extracted from the EEG signals using wavelet transform. The ASCA-WOA-ELM is fed with features for classification. The proposed ASCA-WOA method's uniqueness is shown by optimizing benchmark functions. The performance measure parameters such sensitivity, specificity and accuracy are evaluated from the proposed ASCA-WOA-ELM model. The ASCA-WOA-ELM model achieved 99.42% accuracy, 99.47% specificity, and 99.53% sensitivity. Further, the computational time of 21.2841 seconds achieved by the proposed ASCA-WOA-ELM model. The comparison results with other optimized models such as SCA-ELM, WOA-ELM, ASCA-ELM, WOA-ELM, along with the proposed ASCA-WOA-ELM model are presented