Reviewerly/exHarmony huggingface dataset:
One-Class Classification for Intrusion Detection on Vehicular Networks

Controller Area Network bus systems within vehicular networks are not equipped with the tools necessary to ward off and protect themselves from modern cyber-security threats. Work has been done on using machine learning methods to detect and report these attacks, but common methods are not robust towards unknown attacks. These methods usually rely on there being a sufficient representation of attack data, which may not be available due to there either not being enough data present to adequately represent its distribution or the distribution itself is too diverse in nature for there to be a sufficient representation of it. With the use of one-class classification methods, this issue can be mitigated as only normal data is required to train a model for the detection of anomalous instances. Research has been done on the efficacy of these methods, most notably One-Class Support Vector Machine and Support Vector Data Description, but many new extensions of these works have been proposed and have yet to be tested for injection attacks in vehicular networks. In this paper, we investigate the performance of various state-of-the-art one-class classification methods for detecting injection attacks on Controller Area Network bus traffic. We investigate the effectiveness of these techniques on attacks launched on Controller Area Network buses from two different vehicles during normal operation and while being attacked. We observe that the Subspace Support Vector Data Description method outperformed all other tested methods with a Gmean of about 85%.
The Generalized Capacity of a Quantum Channel

The transmission of classical information over a classical channel gave rise to the classical capacity theorem with the optimal rate in terms of the classical mutual information. Despite classical information being a subset of quantum information, the rate of the quantum capacity problem is expressed in terms of the coherent information, which does not mathematically generalize the classical mutual information. Additionally, there are multiple capacity theorems with distinct formulas when dealing with transmitting information over a noisy quantum channel. This leads to the question of what constitutes a mathematically accurate quantum generalization of classical mutual information and whether there exists a quantum task that directly extends the classical capacity problem. In this paper, we address these inquiries by introducing a quantity called the generalized information, which serves as a mathematical extension encompassing both classical mutual information and coherent information. We define a transmission task, which includes as specific instances both classical information and quantum information capacity problems, and show that the transmission capacity of this task is characterized by the generalized information.
Learning Restricted Boltzmann Machines with greedy quantum search

Restricted Boltzmann Machines (RBMs) are widely used probabilistic undirected graphical models with visible and latent nodes, playing an important role in statistics and machine learning. The task of structure learning for RBMs involves inferring the underlying graph by using samples from the visible nodes. Specifically, learning the two-hop neighbors of each visible node allows for the inference of the graph structure. Prior research has addressed the structure learning problem for specific classes of RBMs, namely ferromagnetic and locally consistent RBMs. In this paper, we extend the scope to the quantum computing domain and propose corresponding quantum algorithms for this problem. Our study demonstrates that the proposed quantum algorithms yield a polynomial speedup compared to the classical algorithms for learning the structure of these two classes of RBMs.
Extreme Parkour with Legged Robots

Humans can perform parkour by traversing obstacles in a highly dynamic fashion requiring precise eye-muscle coordination and movement. Getting robots to do the same task requires overcoming similar challenges. Classically, this is done by independently engineering perception, actuation, and control systems to very low tolerances. This restricts them to tightly controlled settings such as a predetermined obstacle course in labs. In contrast, humans are able to learn parkour through practice without significantly changing their underlying biology. In this paper, we take a similar approach to developing robot parkour on a small low-cost robot with imprecise actuation and a single front-facing depth camera for perception which is low-frequency, jittery, and prone to artifacts. We show how a single neural net policy operating directly from a camera image, trained in simulation with large-scale RL, can overcome imprecise sensing and actuation to output highly precise control behavior end-to-end. We show our robot can perform a high jump on obstacles 2x its height, long jump across gaps 2x its length, do a handstand and run across tilted ramps, and generalize to novel obstacle courses with different physical properties. Parkour videos at https://extreme-parkour.github.io/
UnitedHuman: Harnessing Multi-Source Data for High-Resolution Human Generation

Human generation has achieved significant progress. Nonetheless, existing methods still struggle to synthesize specific regions such as faces and hands. We argue that the main reason is rooted in the training data. A holistic human dataset inevitably has insufficient and low-resolution information on local parts. Therefore, we propose to use multi-source datasets with various resolution images to jointly learn a high-resolution human generative model. However, multi-source data inherently a) contains different parts that do not spatially align into a coherent human, and b) comes with different scales. To tackle these challenges, we propose an end-to-end framework, UnitedHuman, that empowers continuous GAN with the ability to effectively utilize multi-source data for high-resolution human generation. Specifically, 1) we design a Multi-Source Spatial Transformer that spatially aligns multi-source images to full-body space with a human parametric model. 2) Next, a continuous GAN is proposed with global-structural guidance and CutMix consistency. Patches from different datasets are then sampled and transformed to supervise the training of this scale-invariant generative model. Extensive experiments demonstrate that our model jointly learned from multi-source data achieves superior quality than those learned from a holistic dataset.
DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention

Most of the existing multi-modal models, hindered by their incapacity to adeptly manage interleaved image-and-text inputs in multi-image, multi-round dialogues, face substantial constraints in resource allocation for training and data accessibility, impacting their adaptability and scalability across varied interaction realms. To address this, we present the DeepSpeed-VisualChat framework, designed to optimize Large Language Models (LLMs) by incorporating multi-modal capabilities, with a focus on enhancing the proficiency of Large Vision and Language Models in handling interleaved inputs. Our framework is notable for (1) its open-source support for multi-round and multi-image dialogues, (2) introducing an innovative multi-modal causal attention mechanism, and (3) utilizing data blending techniques on existing datasets to assure seamless interactions in multi-round, multi-image conversations. Compared to existing frameworks, DeepSpeed-VisualChat shows superior scalability up to 70B parameter language model size, representing a significant advancement in multi-modal language models and setting a solid foundation for future explorations.
Encoding optimization for quantum machine learning demonstrated on a superconducting transmon qutrit

Qutrits, three-level quantum systems, have the advantage of potentially requiring fewer components than the typically used two-level qubits to construct equivalent quantum circuits. This work investigates the potential of qutrit parametric circuits in machine learning classification applications. We propose and evaluate different data-encoding schemes for qutrits, and find that the classification accuracy varies significantly depending on the used encoding. We therefore propose a training method for encoding optimization that allows to consistently achieve high classification accuracy. Our theoretical analysis and numerical simulations indicate that the qutrit classifier can achieve high classification accuracy using fewer components than a comparable qubit system. We showcase the qutrit classification using the optimized encoding method on superconducting transmon qutrits, demonstrating the practicality of the proposed method on noisy hardware. Our work demonstrates high-precision ternary classification using fewer circuit elements, establishing qutrit parametric quantum circuits as a viable and efficient tool for quantum machine learning applications.
Large Language Models Bias Issues Solving Through SDRT

&lt;p&gt; The paper "Large Language Models Bias Issues Solving Through SDRT" discusses the challenges and ethical concerns posed by large language models (LLMs) such as GPT-3 and GPT-4 in the realm of natural language processing (NLP) and artificial intelligence research. It proposes a solution in the form of Segmented Discourse Representation Theory (SDRT) to address these challenges. By integrating SDRT into existing transformer models and incorporating it into both encoders and decoders, the paper aims to reduce bias, enhance semantic understanding, and foster more meaningful and transparent conversations. This approach recognizes the importance of responsible LLM development and the need for solutions to mitigate issues like misinformation, biased content, and lack of contextual understanding. Through its technical details and architectural improvements, the paper contributes to the ongoing discourse on enhancing the capabilities and ethical use of large language models in complex NLP environments. &lt;/p&gt;
GAST: A generic AST representation for language-independent source code analysis

Organizations use various programming languages to develop their systems. These aim to take advantage of the most appropriate features of each language for a given domain and require programmers to command different languages and also to face the growing complexity of software development and maintenance. So, they need tools to help them analyze programs to identify relationships between their internal elements, uncover patterns, and calculate quality metrics. However, most tools have limited support for parsing multiple programming languages and high acquisition costs. Therefore, there is a need for new methods to analyze code written in multiple programming languages. This article describes the design of a method to automatically transform the syntax of various programming languages into a universal language with a generic syntax. The function of the generic language is to encapsulate the specificities of each specific language, so that the analysis of programs is facilitated in a single programming syntax and not in multiple syntaxes. The advantage of this approach is that only one analysis engine is required, not multiple code analyzers, to study the programs.
The Practice and Application of Computer-Aided Translation Technology in English Teaching

English teaching is a critical link in computer-aided translation technology (CATT). It can improve students’ learning efficiency and cultivate their comprehensive ability. It can also promote China’s cultural knowledge system and international exchange level. This paper mainly introduces the characteristics of computer-aided language technology and how to effectively apply it to real life to analyze and study its significant impact on our daily work. Then this paper designs an English teaching-assisted translation model based on computer-aided technology. Then the performance of the English teaching model is tested by simulation. The test results show that the accuracy of computer-assisted translation is more than 92%, and the translation time is within 10s. This shows that the model can meet users' needs and provide reference suggestions and opinions for English teachers for relevant departments to improve.
A Tiered Model for Data Management, Curation, and Sharing Support in Grant Proposals and Budgets

Recent trends in research funder policies and guidelines increasingly point to requirements for more detailed plans for data management, curation, and sharing with an emphasis on including allowable costs in grant budgets to support these activities as well. As a result, libraries and other collaborating units at academic institutions have an opportunity to develop streamlined models that allow researchers to incorporate institutional resources for data management, curation, and sharing into their grant proposals along with costs to support these resources. While the current literature contains examples and discussions of general funding approaches to activities like data curation and long-term preservation, little has been written on formal models or strategies for incorporating data management and curation support into the grant proposal and budget process itself. This article describes the development and implementation of a tiered grant support model at the University of Colorado Boulder that provides three levels of services, infrastructure, and expertise to researchers who are looking to incorporate support and costs for data management, curation, and sharing into their grant proposals. The article also presents lessons learned since the soft launch of the grant support model as well as key challenges that have emerged. This model may serve as an example for other institutions to use or adapt to continue to help researchers meet funder requirements while simultaneously increasing the visibility of existing data management and curation resources and identifying potential new revenue or cost recovery streams.
Comparative Analysis of Short-term Photovoltaic Power Generation Forecasting Based on LSTM and BP Neural Network

The solar photovoltaic (PV) generation is environmentally friendly but is not stable enough to be put into mass use. An accurate forecasting method is vital to improve the stability of PV power generation. This paper proposed a comparative study about the better method used to forecast PV power generation between LSTM and BP. For comparative study, Long Short-Term Memory (LSTM) was compared with Back Propagation (BP). The dataset used for forecasting was optimized by Pearson Correlation Coefficient method, so it consists of wind speed, humidity, temperature and global horizontal radiation at end. The dataset was divided into three parts: training set 70%, validating set 20%, and testing set 10%. The results show that LSTM is more accurate than BP in the whole view. However, BP is closer to the shape when the PV power generation become lower. To analysis the whole study, difference of principles of two methods affects forecasting accuracy.
Video Anomaly Detection Based on Spatial Awareness and Attention Fusion Method

Anomaly detection in video is one of the challenging problems in the field of computer vision. To address the problem of insufficient prediction capability due to inadequate extraction of spatial features of object appearance in video anomaly detection methods based on predicting future frames, this paper proposes a video anomaly detection method based on spatial perception and attention fusion. The method uses spatially-aware coding network to extract the appearance spatial feature information of objects in video frames, while modeling the global dependencies of deep spatial coding by a non-local module. Then the attention fusion module is used to establish the interaction between the spatial coding and the temporal coding extracted by the traditional coding network to enhance the discriminative representation of the network for video frames. Experimental analysis shows that the method in this paper is able to achieve 84.2% and 95.7% of frame-level AUC in the public benchmark datasets UCSD Ped1 and Ped2, respectively, with good detection performance for video anomalous behavior.
Intelligent Identification of Modulation Patterns Based on the GA-BP Neural Network

Traditional modulation pattern recognition is based on extensive manual experience and sophisticated signal processing equipment, which leads to complex and costly steps. The algorithm in this paper uses the GA-BP neural network to classify and identify eight modulated signals such as GPSK, PAM4, CPFSK, FM, and DSB. The algorithm in this paper extracts the instantaneous parameters of the signal, the higher-order cumulants, and uses a wavelet packet energy spectrum analysis-based method for feature extraction of the signal. The technique optimizes the initial weights and thresholds of the BP neural network and constructs a neural network classifier with higher performance. When the signal-to-noise ratio (SNR) is 20 dB, the average modulation recognition accuracy of this algorithm is higher than 98%.
Dynamic Object Detection and Segmentation Based on Mask R-CNN

In real situations, problems such as false detection or omission can occur in dynamic scene object detection, so this document aims to solve this problem. This paper proposes a new dynamic object detection and segmentation method based on improvements to the Mask R-CNN algorithm. First, the Mask-R-CNN algorithm is improved to introduce temporal information to improve model robustness through temporal context fusion. The network structure is then optimized, and multi-layered pyramid feature fusion and hierarchical prediction mechanisms are used to improve model accuracy and speed. Experimental results show that the proposed method performs better in dynamic object detection and segmentation tasks.
PENGARUH MODEL PEMBELAJARAN KOOPERATIF TIPE TEAMS GAMES TOURNAMENT TERHADAP HASIL BELAJAR PKN SISWA SEKOLAH DASAR

Learning outcomes for each student are varied and not all students have high learning outcomes. This can be caused by various factors including factors that exist in the student who is less active during the learning process based on the lack of variety of learning models applied during the learning process, so that students do not understand the learning material properly. This study uses a cooperative learning model of teams games tournament type with the aim of knowing the improvement of student learning outcomes after using a cooperative learning model of teams games tournament type in class V and to determine the effect of cooperative learning model of teams games tournament type on student learning outcomes. With the research method used is pre experimental one group pre-test post-test design. The sample in this study was class V students totaling 28 people. The sampling used simple random sampling technique. Data collection in this study used tests and non-tests, namely questions about students' Civics learning outcomes and documentation. The results of this study state that the use of the teams games tournament type cooperative learning model has a positive effect on the Civics learning outcomes of elementary school students with a coefficient of determination (R Square) of 0.438 or 43.8% with an average N-Gain value of 46.59%. As well as having a significant difference between
Edge-Computing-Empowered Vehicle Tracking and Speed Estimation Against Strong Image Vibrations Using Surveillance Monocular Camera

A significant number of camera-based solutions suffer from different kinds of performance issues, caused by unstable weather conditions e.g., wind, object overlapping due to heavy traffic flow, and lack of adjustment for a certain location where the camera is installed and fixed. In this paper, we developed an effective approach to traffic flow monitoring under daytime conditions by applying machine learning and computer vision techniques to extract motion traffic data parameters from the videos captured by the static surveillance camera installed and fixed at the intersection. We address the issue of video camera vibration and jittering by applying image-matching algorithms. In order to calculate the vehicle speed, we utilize the projective transformation to compute the real object distance from an image taken by a single camera installed at the road intersection. We adapt and employ state-of-the-art object detectors, transfer learning, and analytical computer vision methods to detect and track vehicles and measure moving speed and vehicle volume from a predefined detection area extracted from monocular videos. The developed approach is proven to be effective in estimating vehicle speed and vehicle volume using video sequences captured from a stationary camera. Experimental results obtained during the study prove the efficiency of the proposed method under strong vibration conditions. The proposed framework achieved promising tracking performance on custom-labeled test video clips with a small absolute error of 3.97% for traffic flow average speed estimation. An additional test on a video from BrnoCompSpeed Dataset shows that the proposed method can reduce the average speed estimation error by 51.8%.
Artificial intelligence applications in media archives

The aim of this paper is to present an international overview of the use of artificial intelligence in the context of media archives in broadcasters, preservation institutions and press agencies, through a comprehensive analysis of sources primarily focusing on case studies presented at international conferences and seminars, together with the results of the survey on the use of artificial intelligence conducted by FIAT/IFTA. Once the most commonly used technologies have been defined and we have identified the stages of the production workflow in which they are used, we will discuss the specific applications of these technologies in television archives, audiovisual heritage preservation organisations, press agencies and innovation projects where technology vendors and media companies collaborate. Finally, we will deal with the challenges related to the implementation of AI in media archives, the need for datasets in the development of language models, and the relevance of a sensible use of technology.
Residual Attention Mechanism for Remote Sensing Target Hiding

In this paper, we investigate deep-learning-based image inpainting techniques for emergency remote sensing mapping. Image inpainting can generate fabricated targets to conceal real-world private structures and ensure informational privacy. However, casual inpainting outputs may seem incongruous within original contexts. In addition, the residuals of original targets may persist in the hiding results. A Residual Attention Target-Hiding (RATH) model has been proposed to address these limitations for remote sensing target hiding. The RATH model introduces the residual attention mechanism to replace gated convolutions, thereby reducing parameters, mitigating gradient issues, and learning the distribution of targets present in the original images. Furthermore, this paper modifies the fusion module in the contextual attention layer to enlarge the fusion patch size. We extend the edge-guided function to preserve the original target information and confound viewers. Ablation studies on an open dataset proved the efficiency of RATH for image inpainting and target hiding. RATH had the highest similarity, with a 90.44% structural similarity index metric (SSIM), for edge-guided target hiding. The training parameters had 1M fewer values than gated convolution (Gated Conv). Finally, we present two automated target-hiding techniques that integrate semantic segmentation with direct target hiding or edge-guided synthesis for remote sensing mapping applications.