Reviewerly/exHarmony huggingface dataset:
Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignment

Anomaly segmentation plays a crucial role in identifying anomalous objects within images, which facilitates the detection of road anomalies for autonomous driving. Although existing methods have shown impressive results in anomaly segmentation using synthetic training data, the domain discrepancies between synthetic training data and real test data are often neglected. To address this issue, Multi-Granularity Cross-Domain Alignment (MGCDA) framework is proposed for anomaly segmentation in complex driving environments. It uniquely combines a new Multi-source Domain Adversarial Training (MDAT) module and a novel Cross-domain Anomaly-aware Contrastive Learning (CACL) method to boost the generality of the model, seamlessly integrating multi-domain data at both scene and sample levels. Multi-source domain adversarial loss and a dynamic label smoothing strategy are integrated into MDAT module to facilitate the acquisition of domain-invariant features at the scene level, through adversarial training across multiple stages. CACL aligns sample-level representations with contrastive loss on cross-domain data, which utilizes an anomaly-aware sampling strategy to efficiently sample hard samples and anchors. The proposed framework has decent properties of parameter-free during the inference stage and is compatible with other anomaly segmentation networks. Experimental conducted on Fishyscapes and RoadAnomaly datasets demonstrate that the proposed framework achieves the state-of-the-art performance.
MV-Diffusion: Motion-aware Video Diffusion Model

In this paper, we present a Motion-aware Video Diffusion Model (MV-Diffusion) for enhancing the temporal consistency of generated videos using autoregressive diffusion models. Despite the success of diffusion models in various vision generation tasks, generating high-quality and realistic videos with coherent temporal structure remains a challenging problem. Current methods have primarily focused on capturing implicit motion features within a restricted window of RGB frames, rather than explicitly modeling the motion. To address this, we focus on improving the temporal modeling ability of the current autoregressive video diffusion approach by leveraging rich temporal trajectory information in a global context and explicitly modeling local motion trends. The main contributions of this research include: (1) a Trajectory Modeling (TM) block that enhances the model's conditioning by incorporating global motion trajectory information, (2) a Motion Trend Attention (MTA) block that utilizes a cross-attention mechanism to explicitly infer motion trends from the optical flow rather than implicitly learning from RGB input. Experimental results on three video generation tasks using four datasets show the effectiveness of our proposed MV-Diffusion, outperforming existing state-of-the-art approaches. The code is available at https://github.com/PKU-ICST-MIPL/MV-Diffusion_ACMMM2023.
Efficient continuous subgraph matching scheme considering data reuse

As the utilization of graph streams in various applications increases, a continuous subgraph matching scheme is required to search for subgraphs that change in real time. In this paper, we present a novel and effective continuous subgraph matching scheme that leverages indexing and employs distributed processing in graph stream environments. To achieve distributed processing, we employ a query graph decomposition policy based on the degree of nodes, allowing us to manage the decomposed subqueries as an index. By reusing indexing information, we significantly reduce the indexing load, which becomes crucial in scenarios where multiple queries are issued simultaneously. To optimize query allocation in the distributed environment, we introduce a cost model that accurately calculates the indexing load for each server. This ensures a balanced distribution of queries, enhancing overall system performance. To perform distributed processing efficiently in stream environments, the proposed scheme is implemented in Storm. We conduct various performance evaluations to demonstrate the superiority of the proposed scheme.
Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models, Benchmark and Efficient Evaluation

Abstract Robot visual navigation is a relevant research topic. Current deep navigation models conveniently learn the navigation policies in simulation, given the large amount of experience they need to collect. Unfortunately, the resulting models show a limited generalization ability when deployed in the real world. In this work we explore solutions to facilitate the development of visual navigation policies trained in simulation that can be successfully transferred in the real world. We first propose an efficient evaluation tool to reproduce realistic navigation episodes in simulation. We then investigate a variety of deep fusion architectures to combine a set of mid-level representations, with the aim of finding the best merge strategy that maximize the real world performances. Our experiments, performed both in simulation and on a robotic platform, show the effectiveness of the considered mid-level representations-based models and confirm the reliability of the evaluation tool. The 3D models of the environment and the code of the validation tool are publicly available at the following link: https://iplab.dmi.unict.it/EmbodiedVN/ .
SCLert: A Span-Based Joint Model for Measurable Quantitative Information Extraction From Chinese Texts

Measurable quantitative information is a fundamental set of information related to the quantity of objects, which widely exists in various texts including medical records, national standards and technical reports. In recent years, some researchers have recognized the importance of measurable quantitative information in helping to analyze healthcare and have proposed pipeline-based approaches, but there are still many challenging issues with measurable information extraction. This paper proposes a span-based joint model (SCLert) for measurable quantitative information extraction, which incorporates Chinese generic features and lexicon information for improving the effectiveness of measurable quantitative information recognition. Compared to pipeline extraction methods that require two independent models, our joint extraction model combines both measurable quantitative information recognition and association within one model. The span-based representation enhances the boundary information of entity and enables more accurate extraction of measurable quantitative information. We evaluated the model on a Standard Quantitative Information dataset with 3106 Chinese text sentences and a Clinical Quantitative Information dataset with 1359 Chinese electronic medical records. Experiment results show that our joint model outperforms baseline models by an improvement of 3.67% on recognition and 6.61% on association in F1-score on the standard datasets.
On the Efficacy of Smart Contract Analysis Tools

Distributed Ledger Technologies are an emerging reality opening the way to new application design paradigms like smart contracts-based distributed applications. If on one side they are creating new markets and opportunities, on the other they are exposing users to new security issues deriving from the scarce maturity in terms of security practices in their design and development. This paper raises a warning about the efficacy of a state-of-the-art software testing tool, namely Mythril, by challenging it with real smart contracts extracted from the Code4arena competitions and comparing its performance with security audits released during the contests. The paper highlights possible root causes of inefficiency, opening the way toward more scalable and efficient smart contract testing tools.
A Novel Robust Kalman Filtering Approach Based on Time-Dependent Structure

In this article, we consider the Kalman filtering problem in the presence of non-Gaussian measurement noise contaminated by time-correlated outliers. A novel robust filtering approach integrating a time-dependent outlier rejection structure into the hierarchical statistic model is proposed to address the sequential outliers in the Bayesian framework. The Gaussian-Gamma prior is first used to model the contaminated measurements, and a weight for each measurement is estimated online to assess the contribution of the current measurement in the state estimation. Furthermore, a novel structured Beta-Bernoulli prior is developed to model the continuous occurrence sequence pattern of outliers by imposing an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${l}$ </tex-math></inline-formula> th-order time-dependent structure and to identify and exclude outliers automatically. The mean-field variational Bayesian (VB) inference method is then utilized to estimate approximate posterior distributions of the state of interest iteratively at each time instant. The superiority of the proposed method over state-of-the-art algorithms is demonstrated by both numerical and experimental datasets.
Design of an optimization scheme for compressed circuits in hardware implementation of large number multiplication

The frequent use of 1024-bit large number multiplication in encryption algorithms like SM9 often involves parallel execution of multiple small-bit Booth multipliers. However, this design approach leads to significant area consumption in the compression circuit and intermediate registers. In this paper, we propose a novel reconfigurable and reusable compressed circuit design that reduces the area of the compression circuit without compromising speed, thus minimizing the overall area of the large number multiplication circuit. The proposed design is simulated using Design Compiler in the SMIC0.18 technology library under the SSG process corner. The results demonstrate a 21% reduction in circuit area and a 2% decrease in power consumption.
A Scalable and Cross-Technology Quantum Control Processor

Quantum control processors (QCPs) bridge the gap between the quantum software and the hardware backend to construct full-stack quantum computers. In this study, the quantum backend interface is responsible for generating pulses to control qubits, where commercial waveform generators or specific converters are typically needed. A radio frequency system-on-chip (RFSoC)-based QCP supports a direct control pulse synthesis without additional components. Therefore, in this work, we propose an RFSoC-based QCP that integrates the processor and pulse generation logic onto a single board. With the help of the proposed instruction set and efficient microarchitecture implementation, the QCP offers large scalability for controllable qubits, while supporting different physical platforms.
Application of SHAP on CatBoost classification for identification of variabels characterizing food insecurity occurrences in Aceh Province households

Classification is the process of building a model that can distinguish between different classes of data. The model aims to predict the class of testing data based on patterns or relationships learned from training data. One of the data processing algorithms used to build classification models is Categorical Boosting (CatBoost). However, in general, the resulting models are difficult to interpret. To facilitate the interpretation of complex classification models, methods such as SHAP (SHapley Additive exPlanations) are needed. SHAP is a method to explain individual predictions. SHAP is based on the game theoretically optimal shapley values. In this study, an analysis of important SHAP variables was conducted on the CatBoost classification model to identify variables characterizing occurrences of food insecurity in households. The data used in this study was obtained from the Survei Sosial Ekonomi Nasional (Susenas) in March 2021 in Aceh Province, sourced from the Badan Pusat Statistik (BPS). There are 13,126 observations in the research data. The results from four evaluated classification models on the testing data showed that the best model had accuracy, sensitivity, specificity, and AUC values of 0.703, 0.349, 0.798, and 0.637, respectively. Furthermore, the results of the analysis of important SHAP variables showed that the variables number of household members who smoke ( ), education of the household head ( ), wall types ( ), drinking water source ( ), and decent sanitation ( ) significantly contributed to the occurrences of food insecurity in households in Aceh Province in the year 2021.
A Comparative Study of Deep Learning Methods for Short-Term Solar Radiation Forecasting

Energy and climate challenges have led to significant growth in solar power generation, and because of this, smart grids are increasingly utilizing solar power. The solar power is intermittent because solar energy is mainly dependent on radiation and other atmospheric factors. To ensure the reliable economic operation of micro grids and smart grids, precise forecasting of solar radiation is crucial. Deep learning techniques are proposed in this study to model the solar radiation production. Four artificial intelligence and deep learning-based forecasting models, the fully connected artificial neural network (ANN), the convolutional neural network (CNN), the long short-term memory network (LSTM), and the bidirectional neural network (Bi-LSTM) were examined for this study. To compare the prediction accuracy of all models, three performance evaluation metrics RSME, MAE, and R2 are used. The results obtained indicate that all four methods produce reasonable estimates of solar radiation generation. As a result of RMSE, MSE, and R2, the Bi-LSTM forecasting model offers the best estimate of forecasting accuracy.
Blueprints of Tomorrow: Co-Designing Speculative Urban Futures

Urban landscapes hold the potential to shape and be shaped by the collective imagination of their inhabitants. In this workshop, we aim to actively engage participants in a process of speculative envisioning of future urban environments. This workshop invites participants to envision future urban landscapes through the lens of speculative design and co-design. We will embark on a city walk, observing and reflecting on the current urban context, followed by a brainstorming session to reimagine these spaces under various "what if" scenarios. Participants will then craft design visions, creating artefacts that represent their concept of the future city. These artefacts will stimulate further discussion and reflection on the potential social and cultural implications of these envisioned futures. The workshop aims to establish a more comprehensive understanding of the relationships between people, places, and potential futures, with the aim of envisioning more resilient and sustainable future urban spaces.
“English Speaking Club” Pendampingan Untuk Meningkatkan Kepercayaan Diri Dan Kualitas Speaking Mahasiswa Program Studi Tadris Bahasa Inggris Fakultas Tarbiyah Dan Keguruan Iai Darussalam Blokagung

The underlying of this community service is the TBIG students’ inability to speak English well. This is due to their lack of self-confidence, lack of vocabulary and practice. It is necessary to overcome through English Speaking Club program. The mentoring method used is Asset-based Community Development (ABCD) which views the community as potential people. The results show that students' speaking ability improves. This improvement is shown by positive changes in terms of vocabulary and pronunciation. Their Vocabularies are increasingly varied and the pronunciation is also precise. Meanwhile, Students' self-confidence also improves. As their vocabularies increase, students feel more confidence to communicate and speak English in public. The fear and lack of confidence toward their ability slowly disappears. Furthermore, the English tutor's ability improves. This improvement can be seen from the micro teaching which was followed after the workshop activities. They shared their experiences with each other on how to open and animate the class atmosphere and close the class
A Survey of Cell Decomposition-based Path Planning

In this article, six obstacle-avoidance path planners are introduced to find the free path in the Euclidean plane with obstacles. Using the Dijkstra’s shortest path algorithm in a connected graph, this study discusses six path planners based on cell decompositions of the free space (obstacle avoidance space) and uses their centroids to plan the shortest free path from the start point to the end point. The six free path planners include: trapezoid decomposition, wavefront decomposition, polygon decomposition, windmill decomposition, brushfire decomposition and delaunay triangulation. In this research, we compare the time and space complexities of connected graph and advantages and disadvantages of each path planner.
Generative Adversarial Networks as Weight Generators in Ensemble Classification

Ensemble classifiers are constructed from various component classifiers by aggregating the results of the individual classifiers in some manner. While there are many ways to do this, one of the least computationally intensive is to use a voting ensemble, where the vote of each classifier is weighted by a number representing its performance. Choosing the appropriate weights is an involved process, that ranges from using trial and error to using sophisticated machine learning algorithms. In this paper, we proposed a novel technique that uses generative adversarial networks to customize the weights used for each individual input to the ensemble. Specifically, we propose to use conditional generative adversarial networks to generate the weights, with the input image acting as the label to the network and the appropriate weight being the output. We test the proposed system on an image classification task and show that the proposed method produces better results than using fixed weights by 2.43 percent.
ARGUS: Visualization of AI-Assisted Task Guidance in AR

The concept of augmented reality (AR) assistants has captured the human imagination for decades, becoming a staple of modern science fiction. To pursue this goal, it is necessary to develop artificial intelligence (AI)-based methods that simultaneously perceive the 3D environment, reason about physical tasks, and model the performer, all in real-time. Within this framework, a wide variety of sensors are needed to generate data across different modalities, such as audio, video, depth, speech, and time-of-flight. The required sensors are typically part of the AR headset, providing performer sensing and interaction through visual, audio, and haptic feedback. AI assistants not only record the performer as they perform activities, but also require machine learning (ML) models to understand and assist the performer as they interact with the physical world. Therefore, developing such assistants is a challenging task. We propose ARGUS, a visual analytics system to support the development of intelligent AR assistants. Our system was designed as part of a multi-year-long collaboration between visualization researchers and ML and AR experts. This co-design process has led to advances in the visualization of ML in AR. Our system allows for online visualization of object, action, and step detection as well as offline analysis of previously recorded AR sessions. It visualizes not only the multimodal sensor data streams but also the output of the ML models. This allows developers to gain insights into the performer activities as well as the ML models, helping them troubleshoot, improve, and fine-tune the components of the AR assistant.
A Smart Contract-based Policy Domain Access Control Framework For Distributed Industrial IoT

Access control is critical for collaboration and resource sharing between nodes in the industrial IoT(IIoT). Existing blockchain combined with access control frameworks cannot address privacy protection issues and dynamic authorization of distributed IIoT equipment. Policy Domain-based Access Control (PDAC) is an access control scheme with dynamic authorization, granularity, and distributed decision-making in a distributed environment. It abstracts unidentified nodes in a distributed system as policy domains and uses the policy domain’s certificate authority to issue access rights. This paper proposes a smart contract-based policy domain access control (SC-PDAC) framework to address the above problems in distributed IIoT. The framework mainly uses smart contracts to write SigContracts, RequestContracts, DecisionContracts, and DelegationContracts with different levels of security access control for different types of resources. To demonstrate the feasibility of the framework, a local private blockchain was created using the Ethereum blockchain system to conduct scenario experiments. The threat model and features of the framework were also analyzed, concluding that the framework has new features of agentability and controllability. In addition, the costs of existing access control frameworks were compared. The results show that the framework proposed in this paper has relatively low computational and gas costs and is more suitable for distributed IIoT.
State-Feedback Optimal Motion Planning in the Presence of Obstacles

In this letter, a solution to the kinematic optimal motion planning problem is presented, where a previous nearly globally optimal approach is extended to workspaces with internal obstacles. The method is inspired by fundamental properties of velocity fields in the presence of obstacles, where topological restrictions inhibit naive approaches. The topological perplexity problem presents itself as a challenging issue for optimal control, even for low-dimensional cases with simple dynamics. Our scheme is formulated such that a locally optimal workspace decomposition enables extracting a close-to-optimal solution. Several synthetic workspace examples are demonstrated, along with comparisons against existing optimal approaches, where our scheme is superior w.r.t. both cost value and execution time.
Book Review: Digital Forensics and Cyber Investigation

Due to the multifaceted nature of the textbook, we believe it presents benefits for the private sector, academics and law enforcement.Below is a summary of the utility of the textbook for each group. Benefits of the Text for Private SectorAs cybercrime rates escalate globally, the costs of being victimized by a malicious cyber-event are becoming increasingly impactive on individuals and organizations.The need to become more aware of how our actions can lead to becoming a cyber-victim, and best practices to avoid doing so, can be found in the well-structured chapters within this textbook.Primarily directed at students wishing to learn more about this area of study, Digital Forensics & Cyber Investigation is an easily understandable reference for anyone interested in learning how to become more cyber-safe.Choi, Back, and Toro-Alvarez (2022) provide readers with a comprehensive history of cybercrime, types of cybercrime, how users of technology (UoT) can become cyber-victims, and many tools that can be utilized to investigate cybercrimes using digital forensics.Many cybercriminals are proficient in the field of computer science, and social engineering -the art of convincing and manipulating recipients of a cyber-intrusion to respond to a bad actor's pursuit to elicit information to use for malicious purposes.This text provides an understanding of techniques used by cybercriminals, aspects of technology that can be used by bad actors, examples of actual cybercrimes and how they were investigated, and an introduction to the intricacies and secretive characteristics of the darkweb.Readers will also learn how cybercriminals are able to remain virtually undetected when committing cybercrimes, and the many challenges related to attempts to identify, charge, and successfully prosecute those responsible for committing cybercrimes.When completed, readers will have gained a greater understanding of why cybercrime has become the most impactive and lucrative form of crime in history producing significant rewards with minimal risks.