Reviewerly/exHarmony huggingface dataset:
Likelihood-free inference in state-space models with unknown dynamics

Abstract Likelihood-free inference (LFI) has been successfully applied to state-space models, where the likelihood of observations is not available but synthetic observations generated by a black-box simulator can be used for inference instead. However, much of the research up to now has been restricted to cases in which a model of state transition dynamics can be formulated in advance and the simulation budget is unrestricted. These methods fail to address the problem of state inference when simulations are computationally expensive and the Markovian state transition dynamics are undefined. The approach proposed in this manuscript enables LFI of states with a limited number of simulations by estimating the transition dynamics and using state predictions as proposals for simulations. In the experiments with non-stationary user models, the proposed method demonstrates significant improvement in accuracy for both state inference and prediction, where a multi-output Gaussian process is used for LFI of states and a Bayesian neural network as a surrogate model of transition dynamics.
LiDAR-Based Symmetrical Guidance for 3D Object Detection

Object detection from 3D point clouds is an essential task for autonomous driving. Current approaches usually focus on the characteristics of the point cloud while ignoring the structural characteristics of the object itself. This paper designs a new symmetric structure enhancement network SS-PV-RCNN according to the symmetry of car structure, inspired by the human symmetry visual cognition system. Using the symmetrically strengthened point cloud to help the network learn the features of the symmetric structure, improve the robustness of the network in the occlusion area, and then improve the accuracy of 3D object detection. This method is verified on the KITTI dataset, and it has a particular improvement compared to the baseline method.
Research Frontiers for Moving Target Defenses

New software security threats are constantly arising, including new classes of attacks such as the recent spate of micro-architectural vulnerabilities, from side-channels and speculative execution to attacks like Rowhammer that alter the physical state of memory. At the same time, new defensive technologies are proposed and adopted, including advancements in programming languages and novel hardware architectures with a focus on security.
Predicting Traffic Incidents in Road Networks Using Vehicle Detector Data

Prediction models can help to identify traffic incidents in roadways, reducing incident detection and response time. Advances in machine learning techniques to support the safety of roadways is a relatively underexplored area. This research seeks to advance machine learning to predict incidents based on detector data by comparing and analyzing the performance of different learning models. This study first discusses the severe class imbalance affecting the dataset. Then, using to-fold cross validation, the performance of the algorithms J48 Decision Tree, Naïve Bayes, and AdaBoost were compared. In the explored problem, a false positive indicates an incident where there is no incident, and this is highly undesirable. AdaBoost reached an ideal 0% false positive rate, followed by Naïve Bayes and J48. However, these values were achieved with a cost of high false negative rates for all classifiers. AdaBoost was also successful in handling the low correlation between fit and test datasets, which was not observed for the other two.
The Classification of Surfaces

This study aims to prove the existence of classification of compact, connected surfaces (without boundaries) up to homeomorphisms. In the first part of the paper, we prepare the basic knowledge that we should know before we start to consider the top theorem, such as compactness and connectedness. Then, this paper enumerates some typical surfaces such as the Möbius strip, torus, and Klein bottle to set up the standard that what is a surface. Triangulation is applied to analyze the topological graph, helping us calculate the Euler characteristics of abstract surfaces that are difficult to count faces. Moreover, we define the topological invariants of surfaces such as orientability to better compare the different characters of each surface. Finally, we prove the Homeomorphism between the combinatorial sphere and its corresponding surfaces. In other words, we can discriminate the surfaces with the genus (How many handles or Möbius strips are on the protosphere).
Deep nonlinear optical neural networks trained with in situ backpropagation

We experimentally demonstrate an optical neural network whose fundamental building blocks are controllable ultrafast quadratic nonlinear pulse-propagation processes. The network is trained in situ by a new backpropagation algorithm that generalizes to arbitrary physical systems.
PROVES: Establishing Image Provenance using Semantic Signatures

Modern AI tools, such as generative adversarial networks, have transformed our ability to create and modify visual data with photorealistic results. However, one of the deleterious side-effects of these advances is the emergence of nefarious uses in manipulating information in visual data, such as through the use of deep fakes. We propose a novel architecture for preserving the provenance of semantic information in images to make them less susceptible to deep fake attacks. Our architecture includes semantic signing and verification steps. We apply this architecture to verifying two types of semantic information: individual identities (faces) and whether the photo was taken indoors or outdoors. Verification accounts for a collection of common image transformation, such as translation, scaling, cropping, and small rotations, and rejects adversarial transformations, such as adversarially perturbed or, in the case of face verification, swapped faces. Experiments demonstrate that in the case of provenance of faces in an image, our approach is robust to black-box adversarial transformations (which are rejected) as well as benign transformations (which are accepted), with few false negatives and false positives. Background verification, on the other hand, is susceptible to black-box adversarial examples, but be-comes significantly more robust after adversarial training.
Learner-Private Convex Optimization

Convex optimization with feedback is a framework where a learner relies on iterative queries and feedback to arrive at the minimizer of a convex function. It has gained considerable popularity thanks to its scalability in large-scale optimization and machine learning. The repeated interactions, however, expose the learner to privacy risks from eavesdropping adversaries that observe the submitted queries. In this paper, we study how to optimally obfuscate the learner’s queries in convex optimization with first-order feedback, so that their learned optimal value is provably difficult to estimate for an eavesdropping adversary. We consider two formulations of learner privacy: a Bayesian formulation in which the convex function is drawn randomly, and a maximin formulation in which the function is fixed and the adversary’s probability of error is measured with respect to a minimax criterion. Suppose that the learner wishes to ensure the adversary cannot estimate accurately with probability greater than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1/L$ </tex-math></inline-formula> for some <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L &gt; 0$ </tex-math></inline-formula> . Our main results show that the query complexity overhead is additive in <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> in the maximin formulation, but multiplicative in <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> in the Bayesian formulation. Compared to existing learner-private sequential learning models with binary feedback, our results apply to the significantly richer family of general convex functions with full-gradient feedback. Our proofs rely on tools from the theory of Dirichlet processes, as well as a novel strategy designed for measuring information leakage under a full-gradient oracle.
Exploration for the application scope of interactive digital media in the prevention and control of COVID-19 epidemic in the community: interactive digital media design plan for garbage classification in Jingxianli community as an example

In the continuous prevention and control of the COVID-19 epidemic, community work has become an important basic unit for epidemic prevention and control. The prevention and control of the epidemic is inseparable from the maintenance of the community's health environment. Whether garbage can be properly and timely handled is the key to epidemic prevention and control. This research is based on the national and Tianjin municipal garbage classification policies, and analyzes the problems and difficulties of community waste classification during the epidemic period, and explores the auxiliary interactive design plan of garbage classification suitable for the "Jingxianli" community under the background of community epidemic prevention and control.
Rock image segmentation based on salient target detection method

research-article Rock image segmentation based on salient target detection method Share on Authors: Peiwu Li Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Ziming Zou Guilin University of Technology, China Guilin University of Technology, ChinaView Profile Authors Info & Claims ICIIP 2021: 2021 6th International Conference on Intelligent Information ProcessingJuly 2021 Pages 232–236https://doi.org/10.1145/3480571.3480612Online:29 October 2021Publication History 0citation10DownloadsMetricsTotal Citations0Total Downloads10Last 12 Months10Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Analysis Of Response Speed Settings Flame Sensor Fire Fighting Robots Using Pid

The fire fighting robot is designed to automatically maneuver through a room where there are hotspots and is able to extinguish the fire. In order to complete the task of extinguishing fire the robot is also designed to use a fire sensor. The robot uses a flame sensor to detect the light generated by the fire source. The Ercomp fire fighting robot, University of Muhammadiyah Ponorogo uses a 28-bit flame sensor with a detection range of 360o. The robot is set using the KP and KD control system with the parameter values ​​used are KP = 9 and KD = 10. In detecting hotspots, the robot must be fast when tracing hotspots. In this study, the application of PID control is expected to produce the best comparison value of PD, PI and PID values ​​to be implemented on legged fire fighting robots. This study is to determine the value of the control tested using the trial and error method and PID tuner. The best results from the test are PID control with Kp = 90, Ki = 20 and Kd = 10, it can be seen from the response parameters that the system has no overshoot, a rise time of 0.59 seconds, and a settling time of 1.15 seconds.
Multimodal Fusion Using Deep Learning Applied to Driver's Referencing of Outside-Vehicle Objects

There is a growing interest in more intelligent natural user interaction with the car. Hand gestures and speech are already being applied for driver-car interaction. Moreover, multimodal approaches are also showing promise in the automotive industry. In this paper, we utilize deep learning for a multimodal fusion network for referencing objects outside the vehicle. We use features from gaze, head pose and finger pointing simultaneously to precisely predict the referenced objects in different car poses. We demonstrate the practical limitations of each modality when used for a natural form of referencing, specifically inside the car. As evident from our results, we overcome the modality specific limitations, to a large extent, by the addition of other modalities. This work highlights the importance of multimodal sensing, especially when moving towards natural user interaction. Furthermore, our user based analysis shows noteworthy differences in recognition of user behavior depending upon the vehicle pose.
BASNet: improving semantic segmentation via boundary-assistant symmetrical network

Recently, boundary information has gained more attention in improving the performance of semantic segmentation. This paper presents a novel symmetrical network, called BASNet, which contains four components: the pre-trained ResNet-101 backbone, semantic segmentation branch (SSB), boundary detection branch (BDB), and aggregation module (AM). More specifically, our BDB only focuses on processing boundary-related information using a series of spatial attention blocks (SABs). On the other hand, a set of global attention blocks (GABs) are used in SSB to further capture more accurate object boundary information and semantic information. Finally, the outputs of SSB and BDB are fed into AM, which merges the features from SSB and BDB to boost performance. The exhaustive experimental results show that our method not only predicts the boundaries of objects more accurately, but also improves the performance of semantic segmentation.
Block diffusion delay attack and its countermeasures in a Bitcoin network

Summary In the Bitcoin system, transactions and their collections (i.e., blocks) are distributed over a peer‐to‐peer (P2P) network (i.e., Bitcoin network) constructed by participating nodes. Each node maintains a distributed ledger (i.e., blockchain) consisting of retrieved blocks. Therefore, speedy block distribution over the Bitcoin network is essential for all nodes to reach a global consensus on the blockchain. On the other hand, Bitcoin clients are developed as open source software, and thus they can be modified by malicious users. Existing work has pointed out that an attacker can delay the block propagation between neighboring nodes by exploiting the regular timeout mechanism for unexpected slow block transfer caused by temporal network trouble. In this paper, we focus on block diffusion delay attacks, where multiple attackers collude with a specific miner (i.e., a special node that creates new blocks and broadcasts these blocks to the other miners) to disturb the propagation of blocks generated by competing miners. Through simulation experiments, we first reveal that about 30% of honest nodes cannot normally retrieve a block when there are only 1% of the nodes in the system are high‐degree adversary nodes in the system. This indicates that the malicious miner colluding with the attackers can intentionally delay the diffusion of the block mined by the competing miner, so as to win the competitive block diffusion even if it loses at the competitive block mining. To alleviate the block diffusion delay attack, we propose two kinds of countermeasures: a proactive approach that is a speedy recovery method from the interruption by adjusting the timeout value and a reactive approach that is a block retrieval node selection method based on the past download rate from each neighbor. Through simulation experiments, we show the countermeasures can effectively alleviate the risk.
Research on the Construction of High Mathematics Learning Platform Based on Cloud Computing under the Background of Smart Campus

With the development of information technology, new learning models emerge one after another, among which online learning is the most influential. It is a brand-new way for students to learn online by setting up an education platform on the Internet. Smart campus is the upgrade and development of digital campus, and it is an important embodiment of the construction of intelligent campus and economical campus. Through the application of new and advanced information technology, it can not only provide teachers with intelligent teaching environment, but also form an effective assistant to traditional teaching. Higher mathematics is an extremely important basic course with the characteristics of long hours, many contents, extensive knowledge, strong theory, great difficulty, flexibility and strong skill. This paper expounds the design process of cloud computing aided teaching platform for higher mathematics, puts forward the possibility and necessity of reasonably integrating the network teaching platform for higher mathematics with the cloud computing platform, analyzes the specific design process, and gives the technical implementation strategy.
Towards Detection of Synthetic Utterances in Romanian Language Speech Forensics

The latest decade has seen a huge wave of interest in the synthesis of human image and speech. Besides the enormous impact of synthetic voice in the communication between humans and machines, the production of the so-called “fake media” entered the focus of forensic audio and video communities. A large variety of techniques are now available to produce synthetic speech, from the traditional concatenative speech production to multi-million parameter speech and speaker models. Recent work in the field of artificial intelligence (AI) has shown some synthetic speech generators as capable to fool even state-of-the-art automatic speaker verification systems. AI seems to hold the key to successful speaker spoofing attacks, but also for their countermeasures. As a first step on the way, this paper describes a data-centric method to detect the use of synthetically generated spoken digits in the Romanian language.
Augmenting Image Data Sets With Water Spray Caused by Vehicles on Wet Roads

Adverse weather conditions challenge object detection neural networks, because they are mostly trained on clean data sets that were taken in good weather. But autonomous vehicles rely on accurate detection and classification of other road users for safe and reliable operation. Capturing new data sets in rough weather is time consuming and expensive. We therefore propose a method to augment existing image data sets with physically realistic water spray swirled up by vehicles—one of the most influential disturbances for even human drivers on wet roads in or after heavy rain. Using a wide range of newly augmented images, we evaluate the influence on an established convolutional neural network object detection and showcase the potential of training with these new augmented data sets.
ConTraEmSis: The Congested Traffic and Emission Index Impact Analysis Amid the Large-Scale Social Restrictions (LSSR) of COVID-19 in Several Cities of West Java, Indonesia

Abstract In some metropolitan cities of West Java Province, the urban movability affects to the congested traffic. This study analyzes the congested traffic during the large-scale social re-strictions (LSSR) of the coronavirus disease 2019 (COVID-19) around some metropolitan cities of West Java Province in May-June 2020. The national government of the Republic of Indonesia declared a national’s LSSR just for some the essential and critical activities are being acquiesced still enforce. Our proposed method, namely, ConTraEmSis is contributed to analyze the congested traffic two months from May-June 2020. We exploit the geomatic of the congested traffic in the COVID-19 information &amp; coordination center West Java province (PIKOBAR) dataset for Bogor, Depok, and Bekasi (Bodebek areas), Bandung areas, and whole West Java province areas. We exploit the road transport and traffic management center (RTTM) and area traffic control system (ATCS) and index their data for the Bodebek and Bandung areas. The ConTraEmSis demonstrates that the congested traffic the LSSR of COVID-19 reduces around 18%-49% every month. Instead, the Java government always launch the many good governance policies to support the LSSR of the COVID-19 system. proposed model shows that after the LSSR in 2019 and 2020, is reduced between 3.27% and 5.27%. On the week-day afternoon, we scale down between 4.52% and 4.74%. On the weekend morning, we cut down between 1.3% to 1.5% before and after LSSR of 2019 and 2020, respectively. The congested traffic trends get 24% and 41% in the weekdays and weekends, respectively, since the LSSRs. Onto the weekend afternoon, we perform 13.4% and 14.8% for reducing the CO2 emission index during the LSSR’s 2020. We achieve the important congestion get an emission index lower than 0.3.
Silicon Photonic Filter-based Dot Product Engine for Convolutional Neural Networks

We present a silicon photonic filter-based analog engine for computing dot products in convolutional neural networks. It shows a greater energy efficiency compared to electronic solutions with a limited bit resolution degradation of input signals.
Centrality-based Interpretability Measures for Graph Embeddings

Many real-world data are considered as graphs, such as computer networks, social networks and protein-protein interaction networks. Graph embedding methods are powerful tools for representing large graphs in various domains. A graph embedding method projects the components of a graph, such as its nodes or edges, into a vector space with a lower dimensionality than the adjacency matrix of the graph, and aims to preserve the characteristics of the graph. The generated embedding vectors have been utilized in various graph mining applications such as node classification, link prediction and anomaly detection. Despite the wide success of the graph embedding methods, little study has been done to facilitate a better understanding of the graph embeddings. In this paper, inspired by advancements in interpreting word embeddings, we propose two interpretability measures to quantify the interpretability of graph embeddings by leveraging useful network centrality properties and perform comparisons of different graph embedding methods. Using these scores, we can provide insights into the representational power of graph embedding methods.
Understanding the Potential of Augmented Reality in Manufacturing Environments

Manufacturing companies are confronted with challenges due to increasing flexibility requirements and skill gaps. Augmented Reality applications offer an efficient way to overcome these tensions by enhancing the interaction between people and technology. The positive effects of Augmented Reality solutions are often described in individual models in the scientific literature. This research-in-progress aims to aggregate the empirical findings in the usage of Augmented Reality solutions in manufacturing environments. A meta-analysis is conducted to synthesise several small studies into one large study to achieve this. In particular, the meta-analysis will focus on the impact of Augmented Reality applications on cognitive load levels. Furthermore, the effect on processing time and error rates will be evaluated. Initial results of the meta-analysis will be expected and reported at this year’s NeuroIS Retreat.
Group-dependent finite mixture model

We present a Bayesian nonparametric group-dependent mixture model for clustering. This is achieved by building a hierarchical structure, where the discreteness of the shared base measure is exploited to cluster the data, between and within groups. We study the properties of the group-dependent clustering structure based on the latent parameters of the model. Furthermore, we obtain the joint distribution of the clustering induced by the hierarchical mixture model and define the complete posterior characterization of interest. We construct a Gibbs sampler to perform Bayesian inference and measure performances on simulated and a real data.