Reviewerly/exHarmony huggingface dataset:
Sentirec - A Sentiment Analysis with Recommendation System

The standard language is assessed, and the feelings transmitted by the individual are brought up. The purpose of sentiment analysis is to determine the polarity of a person's textual opinion. Most of the people use social media to market ourselves and share all knowledge, thoughts, ideas, and experiences with the rest of the world. Sentiment analysis has been studied by several internet companies that offer materials based on human emotions expressed through social media. Many people use social media to post and share their thoughts and feelings. The sentiment analysis standard assorts a sentence with all forms of emotions, each of which has an intensity. This study includes a sentiment analysis of diverse perspectives on social topics that are trending on social media, along with a recommendation system. This method recommends related content to viewers based on their current statement or issue. The algorithm also suggests customized video sets to users based on their social media activity, such as diverse perspectives on a particular social problem about which they actively express their thoughts on social media. NLP algorithms are utilized for sentiment analysis and recommendation systems.
A Novel Approach to Audio Steganography on Audio Input for Secure Communication

In today’s digital age, one is focused on copyright-dependent people and organizations, especially in the entertainment business, and concerned with the privacy of data. The differences in the human voice makes it challenging to produce the watermark needed to protect audio signals from unauthorized access. Enhancing the security of the transmitted data is the main goal of the steganography method. The steganographic file cannot be accessed or used improperly by the unauthorized user. To maintain data confidentiality and privacy, audio steganography is equally applicable to non-technical sectors. Steganography, is often known as security by obscurity. Steganography functions by substituting bits of different, unseen data for pieces of redundant or unnecessary data in common computer files (such as images, sound, and text). This secret data may be in the form of plain text, cipher text, or even sound. In a computer-based audio Steganography system, digital sound is used to covertly encode messages. By slightly modifying the byte sequence of a sound file, the hidden message is implanted. This paper proposes a robust steganography technique that uses the LSB method for performing steganography and AES for providing added security.
Improved Machine leaning algorithms for sentiment analysis

Numerous research have applied sentiment analysis to Twitter because of the vast amounts of data being created there. These social media platforms produce huge amounts of unstructured data streams that are difficult to deal with. Massive volumes of unstructured data are produced by such social network services, making their management extremely difficult. The study's goal is to use a consensus reached among a variety of algorithms to consistently evaluate the sentiment of trending tweets in the Twitter API data stream. We used a variety of algorithms, including Support-Vector Machine and Naive Bayes, to arrive at a consensus on the tone of trending tweets in the Twitter API data stream. We used a number of different approaches, such as Support-Vector Machine and Naive Bayes. Combining the best features of TextBlob and the Lexicon Method. We hypothesize that by combining these techniques, we will be able to obtain more reliable findings. Lexicon Method and Textblob Storage. We hypothesize that by combining these approaches, we will be able to provide more reliable findings.
An Online Recognition Method of Badminton Stroke Based on Inertial Sensor

Badminton is a sport that is very popular, and it has many benefits to the human body. At present the scope of the research on badminton movement using inertial senors is mainly focused on the relationship between the dynamic speed and speed of the racket, or the dynamic analysis of the swing arm of the badminton players.and There are currently few studies on badminton strokes identification, but a lot of study on the relationship between dynamic speed and racket speed or the dynamic analysis of the swing arm of the badminton players. In this research, they offer an initial online identification system for badminton strokes, and they show how to collect badminton data using an inertial sensor attached to the bottom of a racket. Extracting data on badminton strokes from video sequences is done using a threshold-based detection method and sliding window methodology. For the classification phase, they retrieved a total of twelve characteristics to make up the feature space, and they employ random forest (RF) to identify badminton shots. Experiments with a total of 12 participants show that the proposed algorithm is both successful and useful.
A 3D/2D Coronary Artery Registration Method Based on Deep Reinforcement Learning

At present, vascular interventional surgery mainly depends on the conventional coronary angiography (CCA) and coronary computed tomography angiography (CCTA). However, due to various reasons such as different imaging principles and different operation methods, both CCA and CCTA imaging technologies can only be used separately. In order to integrate the two kinds of medical image information, medical image registration technologies based on artificial intelligence have emerged. In this paper, we propose a 3D/2D coronary artery registration method based on the combination of feature and deep reinforcement learning. Based on the centerline feature, we perform a projection transformation to unify the dimensions between two modes, then design a deep reinforcement learning algorithm, complete the mapping of the state space by convolutional neural network (CNN), discretize the actions of the agent, and use the Euclidean distance to define the reward function for fine coronary artery registration. The method is used for experiments with clinical CCA and CCTA data sets, and compared with other registration algorithms. The results show that the registration error rate of the proposed method is lower than that of other algorithms, it has strong accuracy and robustness, and can handle the registration problems of complex vascular segments.
Colonic Polyp Segmentation Algorithm Based on Improved SegFormer Network

To solve the problem that factors such as the similar color of polyps and background in colon polyp images and the different sizes of polyps affect the segmentation accuracy, an improved SegFormer, U-SegFormer, is proposed for colon polyp image segmentation. Firstly, in the feature fusion phase of the decoder, features at multiple scales from the encoder output are fused in a cascade fashion and the feature representation is enhanced using the Unified Attention Fusion Module; Then, training the network in combination with transfer learning, using a loss function combining Dice Loss and Focal Loss to mitigate the effect of positive and negative sample imbalance on model training. Finally, the U-SegFormer was compared with other segmentation models, and the experimental results showed that the polyp segmentation method based on the U-SegFormer model was superior to the current mainstream segmentation methods and had a certain potential for clinical application.
Detection of Weld Appearance Defects of Ship Hulls with a Crawling Robot and Improved YOLOv5

The detection of weld appearance defects of ship halls is mainly performed manually. Detection results depend on working experience, and working in ship halls is dangerous. In this paper, we design a crawling robot using the improved YOLOv5 to the detection of weld appearance defects on ship halls in real time. The features of weld appearance defects are not obvious and the detection objects are small. In addition, the attention mechanism is often used to enhance the features, and the small target detection layer can be used to strengthen small target recognition ability. Therefore, we improve YOLOv5 with CA and SPD-Conv to achieve good effect on detection of weld appearance defects. We apply the improved YOLOv5 model in the software of crawling robot, which makes the crawling robot detect the weld appearance defects on ship hulls in real time. The experimental results show that the improved model can effectively improve the mAP to increase by 0.026.
Reactively Following Equidistant Curves with Singularities by an Underactuated Nonholonomic Mobile Robot

An underactuated nonholonomic Dubins-vehicle-like robot with a lower-limited turning radius travels in a plane with unknown objects. In its local frame, the robot has access to the nearest point(s) in the union of the objects. The robot has to approach and escort them, with maintaining a given distance to the currently nearest object, so that all objects are enclosed inside the robot's path. The targeted equidistant curve to the objects may contain parts that cannot be perfectly traced due to excessive contortion and singularities. So the task is compulsorily reframed as that of finding, approaching and repeatedly tracing an approximation of the equidistant curve that is featured by a certain degree of optimality among those trackable by the robot. A computationally inexpensive navigation law is presented that solves this mission. Nonlocal convergence of this law is justified by mathematically rigorous results and is confirmed by computer simulation tests and experiments with real robots.
Distributed Robust Particle Filter Based on Wasserstein Metric

In this paper, for nonlinear systems with uncertain interference factors, particle filter can no longer accurately estimate the state when both process noise and measurement noise are unknown, so a distributed robust particle filter method based on Wasserstein metric is proposed. The method mainly uses Wasserstein metric to construct the fuzzy set of real data distribution, replaces the original empirical distribution with the prior estimation distribution of particle filter, and takes it as the center point of the new fuzzy set; Secondly, on the premise of tolerable conservatism, the optimal state estimator in this state is constructed according to the criterion of minimum root mean square error in the worst case; Then, the updating and resampling of particle filter are used to complete the calculation of a posteriori estimation, which solves the problem of state estimation of nonlinear systems under uncertain disturbances; Finally, an example is given to verify the feasibility and effectiveness of the distributed robust particle filter method.
Realtime Mask Detection of Kitchen Staff Using YOLOv5 and Edge Computing

We propose a new end-to-end, edge-device embedded solution for detection of improper mask-wearing of kitchen staff using network cameras. Mask detection on kitchen network cameras is a highly specified task with little ready-to use data. The filming position of network cameras and the enviroment in kitchen leaded to vague images and small targets. Also, the detection model should be light-weighted to be deployed on edge computing devices. To improve accuracy on this task, We constructed a novel dataset from real kitchen cameras of different positions, indoor layouts, light conditions and applied effective data augementation. We conducted transfer learning on our dataset starting from COCO pre-trained YOLOV5s weights. In addition, we optimized the model through parameter tuning and post training model pruning for a single Nvidia Jetson Nano device and achieved high accuracy and sensitivity on multiple HD resolution network camera video feed. Experimental results show that our model achieves a training accuracy of 100 percent mAP(0.5) and test accuracy of 97.6 percent mAP(0.5) with minimal training cost: only 89 epochs on our dataset with early stopping. With an inference speed of 10 FPS on Nvidia Jetson Nano, our solution suffices the application requirements and can handle mutiple parallel HD camera streams simultaneously. Compared with previous research, our solution provides competitive cost efficiency where accurate and sensitive high resolution image detection can be run on a single edge device.
Spatio-Temporal Lattice Planning Using Optimal Motion Primitives

Lattice-based planning techniques simplify the motion planning problem for autonomous vehicles by limiting available motions to a pre-computed set of primitives. These primitives are combined online to generate complex maneuvers. A set of motion primitives <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> -span a lattice if, given a real number <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t\geq 1$ </tex-math></inline-formula> , any configuration in the lattice can be reached via a sequence of motion primitives whose cost is no more than a factor of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> from optimal. Computing a minimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> -spanning set balances a trade-off between computed motion quality and motion planning performance. In this work, we formulate this problem for an arbitrary lattice as a mixed integer linear program. We also propose an A*-based algorithm to solve the motion planning problem using these primitives and an algorithm that removes the excessive oscillations from planned motions – a common problem in lattice-based planning. Our method is validated for autonomous driving in both parking lot and highway scenarios.
Dual-Branch Learning With Prior Information for Surface Anomaly Detection

Visual surface anomaly detection focuses on the classification and location of regions that deviate from the normal appearance, and generally, only normal samples are provided for training. The reconstruction-based method is widely used, which locates the anomalies by analyzing the reconstruction error. However, there are two problems unsettled in the reconstruction-based method. First, the reconstruction error in the normal regions is sometimes large. This might mislead the model to take the normal regions as anomalies, which is named as overkill problem. Second, it has been observed that the anomalous regions sometimes cannot be repaired to normal, which results in a small reconstruction error in the anomalous regions. This misleads the model to take the anomalies as normal, which is called as anomaly escape problem. Aiming at the above two problems, we propose a model named DBPI which is mainly composed of a dual branch autoencoder structure and GA unit. To alleviate the overkill problem, a natural idea is to reduce the reconstruction error in the normal regions, therefore a dual branch autoencoder is proposed. The dual branch autoencoder reconstructs two images with consistent normal regions and different anomalous regions. By analyzing the reconstruction error between the above two reconstructed images, the anomalies can be detected without causing overkill. For the anomaly escape problem, an effective solution is to add prior information of normal appearance to the reconstructive network, which assists in repairing the anomalous regions and increasing the reconstruction error in the anomalous regions. Since the mathematical expectation map of the training data contains crucial features of the normal appearance, we utilize it as the prior information of the normal appearance. And the prior information is selectively introduced by the proposed Gated Attention unit, which effectively assists in reconstructing a normal image and further mitigates the anomaly escape problem. On the AP metric for the anomaly detection benchmark dataset MVTec, the proposed unsupervised method outperforms the current state-of-the-art reconstruction-based method SSPCAB by 7.4%. Meanwhile, our unsupervised method also exhibits comparable performance to the best supervised methods on the surface defect detection DAGM dataset.
Upgraded Software and Embedded Improvements: A Puzzle of User Heterogeneity

The rise in cyberattacks over the past two decades spurred interest in policies improving cybersecurity. One focus of that research is how to create incentives for software vendors to release updates fixing vulnerabilities in their software. An important consideration that has received far less attention in this literature is understanding if software users install available software updates promptly and what factors increase or decrease the responsiveness of firms in installing available updates. In this paper, we empirically investigate the propensity of firms to install software updates on the servers running their websites. We compiled a dataset tracking the server software used by over 150,000 medium and large firms in the United States to host their websites between 2000 and 2018. Treating the discovery of security vulnerabilities in the server software as quasi-natural experiments, we examine if and when firms update their server software. We uncover widespread usage of software with severe security vulnerabilities, with nearly 76% of the firms analyzed forgo installing available software updates for at least six months after a severe security vulnerability was found in their software. Using hazard model analysis that accounts for firms having different organizational routines for updating, we document that usage of cloud-based platforms for hosting websites can decrease the time to installing updates, that technical complexity on sites slows updating, and that the disclosure of severe vulnerability fixes in software updates does not jolt firms into installing them. Finally, we discuss how the relative inattentiveness of firms to software update releases should be incorporated into the design of cybersecurity policies.
Cryogenic Embedded System to Support Quantum Computing: From 5-nm FinFET to Full Processor

Quantum computing can enable novel algorithms infeasible for classical computers. For example, new material synthesis and drug optimization could benefit if quantum computers offered more (qubit). One obstacle for scaling up quantum computers is the connection between their cryogenic qubits at temperatures between a few millikelvin and a few kelvin (depending on qubit type) and the classical processing system on chip (soc) at room temperature (300K). Through this connection, outside heat leaks to the qubits and can disrupt their state. Hence, moving the SoC into the cryogenic part eliminates this heat leakage. However, the cooling capacity is limited, requiring a low-power SoC, which, at the same time, has to classify qubit measurements under a tight time constraint. In this work, we explore for the first time if an off-the-shelf SoC is a plausible option for such a task. Our analysis starts with measurements of state-of-the-art 5 nm FinFETs at 10K and 300K. Then, we calibrate a transistor compact model and create two standard cell libraries, one for each temperature. We perform synthesis and physical layout of a RISC-V SoC at 300K and analyze its performance at 10k. Our simulations show that the SoC at 10k is plausible but lacks the performance to process more than a few thousand qubits under the time constraint.
Efficient Offline/Online Heterogeneous-Aggregated Signcryption Protocol for Edge Computing-Based Internet of Vehicles

The Internet of Vehicles (IoV), which is an extension of traditional vehicular ad hoc networks (VANETs), ensures the provision of safe and efficient vehicular communications. However, the burgeoning IoV faces several challenges in terms of traffic information security and vehicle identity privacy during wireless transmission. Generally, vehicles move faster on the highway necessitating the use of reliable technology to communicate with infrastructure on the roadside efficiently and securely. Lately, researchers have proposed several different message dissemination methods for vehicle-to-infrastructure (V2I) communications. However, they suffer from different concerns (i.e., security weaknesses, computationally heavy, and high communication/storage overhead); therefore, they are unreliable for delay-sensitive communications. To cope with this, we come up with an enhanced certificateless and identity-based offline/online heterogeneous signcryption (CIOOHSC) protocol for V2I communications in edge computing-based IoV to simultaneously achieve confidentiality, integrity, authentication, non-repudiation, and identity anonymity in a logically single step. The CIOOHSC protocol allows a vehicle in certificateless cryptography to transmit a message to an edge node in identity-based cryptography. In the online phase, there are no point multiplication operations as they are already carried out in the offline phase. This reduces the computational load considerably in the online phase. Furthermore, our protocol enables the edge node to aggregate multiple messages and process them simultaneously to boost performance. The CIOOHSC protocol’s security is proved in the random oracle model under the hard problems’ assumptions. Our protocol outperforms recent similar schemes in terms of computational complexity, communication/storage overhead, and average transmission delay, according to the performance evaluation findings.
The Strategic Balance of Games in Logic

Truth, consistency and elementary equivalence can all be characterised in terms of games, namely the so-called evaluation game, the model-existence game, and the Ehrenfeucht–Fraisse game. We point out the great affinity of these games to each other and call this phenomenon the strategic balance in logic. In particular, we give explicit translations of strategies from one game to another.
Radar-Specific Emitter Identification With Only Envelope Power Based on Multidimensional Complex Noncentral Chi-Square Classifier

The signal-level emitter individual identification can provide more accurate and abundant information for radar reconnaissance, interpretation, and combat capability judgment. Unintentional modulation on pulse (UMOP) caused by the differences in the transmitter hardware is distinctive to each emitter. Thus, it is believed as a potential approach to realizing specific emitter identification (SEI) for radar. However, the approach would still meet many issues, like the UMOP is subtle and covered by noise. Meanwhile, such a “fingerprint” exhibit an irregular fluctuation pattern, so it is hard to build a standard and unified feature extraction procedure. In this article, to avoid the influence of pulse modulation on the fingerprint features, we only use the fluctuation of the envelope power to realize SEI. In particular, a probability derivation is demonstrated, and it is concluded that the normalized power of UMOP obeys the complex noncentral chi-square (CNCX) distribution, which establishes a theoretical basis for SEI through UMOP in power. Based on it, a high-precision reconstruction of the UMOP template through several noisy samples is accomplished. Afterward, a Bayesian classifier is naturally built based on the reconstructed UMOP template, which is highly explainable and retains almost the complete distinguishing information of individuals in power. Simulation experiments show that the Bayesian classifier achieves a satisfying accuracy on radar SEI under a low signal-to-noise ratio (SNR) environment. Also, it has a significant advantage over the deep neural network (DNN) under both conditions where sufficient and few-shot training samples are assumed.