Reviewerly/exHarmony huggingface dataset:
Data Association Between Event Streams and Intensity Frames Under Diverse Baselines

This paper proposes a learning-based framework to associate event streams and intensity frames under diverse camera baselines, to simultaneously benefit camera pose estimation under large baselines and depth estimation under small baselines. Based on the observation that event streams are globally sparse (a small percentage of pixels in global frames are triggered with events) and locally dense (a large percentage of pixels in local patches are triggered with events) in the spatial domain, we put forward a two-stage architecture for matching feature maps. LSparse-Net uses a large receptive field to find sparse matches while SDense-Net uses a small receptive field to find dense matches. Both stages apply Transformer modules with self-attention layers and cross-attention layers to effectively process multi-resolution features from the feature pyramid network backbone. Experimental results on public datasets show a systematic performance improvement for both tasks compared to state-of-the-art methods.
Research on relationship extraction for constructing knowledge graphs

A knowledge graph, consisting of concepts, entities, and their relationships, is the most effective way to handle vast amounts of information. This study presents a relationship extraction approach for designing and constructing a graph structure, given that current domain knowledge graphs are unable to identify links between various kinds of data structures utilizing particular building methods. Data from the Network Encyclopedia's categorization system and web page classification labels, both structured and semi-structured, provide the basis for hyponymy. Non-superior connections are covered as well. The convolution residual network that is based on cross-entropy loss function has also been enhanced as a consequence of the suggested solution. Using a comparable existing item as a reference, we evaluate the design to see whether it performs as predicted.
Neural Network Model Reconstructed from Entangled Quantum States

Neural Networks (NNs) have received extensive attention and research due to their ability to extract and combine different features non-manually and to mine the internal relationships between features. In quantum mechanics, the entangled state simultaneously describes the classical and non-classical correlation between subsystems, so it can reveal important quantum phenomena such as non-locality, entanglement, etc., but its essence is the characterization of strong statistical relationships. Considering the superiority of the entangled state, this article attempts to use the entangled state to reconstruct the neurons of NNs to achieve that the network model can characterize the strong statistical relationship between the features, namely, the classical and non-classical correlation. Specifically, based on concurrence, a quantification method of entanglement, we propose a regularizer that can constrain a state vector to an entangled state, and apply it to the optimization process to ensure that the vector passed to the neuron is a legal entangled state. Finally, the entangled state is measured to obtain the output of the neuron. Experimental results show that our model is better than the baseline. Moreover, it performs well compared to the model inspired by quantum entanglement.
Comprehensive Assessment Method for the Intelligence Level of Extraterrestrial Unmanned Detection System

This paper presents a synthesis evaluation scheme of intelligence level for the Extraterrestrial Unmanned Detection System (EUDS). Specifically and firstly, the evaluating index framework (EIF) of intelligence level is constructed for data collection. Then, an extensible judgment matrix of EIF is defined for calculating the index weight distribution. To eliminate the vagueness and consistency of the judgment matrix, Extension AHP (EAHP) algorithm is employed. Additionally, with the purpose of objectivity and rationality of assessment, the fuzzy synthesis evaluation method is leveraged to calculate the quantitative result. Finally, the implementation process of the evaluation scheme is demonstrated by the experimental data and simulation example, and the assessment results are analyzed for the improvement of EUDS intelligence level.
A Formal Methodology for Verifying Side-Channel Vulnerabilities in Cache Architectures

Security-aware CPU caches have been designed to mitigate side-channel attacks and prevent information leakage. How to validate the effectiveness of these designs remains an unsolved problem. Prior works assess the security of architectures empirically without a formal guarantee, making the evaluation results less convincing. In this paper, we propose a comprehensive methodology based on formal methods for security verification of cache architectures. Specifically, we design an entropy-based noninterference reasoning framework with two unwinding conditions to assess the information leakage of the cache designs. The reasoning framework quantifies the dependency relationships by the mutual information between the distributions of input and output of side channels. Given a cache design, we formalize its behavior specification along with the cache layouts into an abstract state machine, to instantiate the parameterized reasoning framework that discloses any potential vulnerabilities. We use our methodology to assess eight state-of-the-art cache architectures to demonstrate reliability as well as flexibility.
From Object-Attribute-Relation Semantic Representation to Video Generation: A Multiple Variational Autoencoder Approach

Video generation refers to synthesizing high-resolution video from latent representations or features. In an end-to-end encoder-decoder generation framework, the intermediate latent representation is expected to contain important semantic information within a small amount of structural data, such that the generated videos have high-fidelity and good perceptual quality. With these considerations, we propose a multiple variational autoencoder approach for video generation with object-attribute-relation (OAR) model. The proposed framework generates a video by decoding semantic latent representations in an OAR pattern (objects, attributes and their relations) into plausible high-fidelity videos. Specifically, the videos are first represented in terms of a well-organized, and easily parsed OAR structure and the remaining background. We use multiple encoders to learn the latent embeddings of objects, attributes, relations, and the remaining background separately, which are viewed as different semantic components. Correspondingly, multiple decoders are used to reconstruct these components, which are then fused by a UNet to generate the full videos. We improve the video generation quality by introducing the relations between the objects. Experimental results on the challenging Google Research Football dataset, along with detailed comparison to the advanced methods, verify the effectiveness of the proposed framework.
Efficient Video Privacy Protection Against Malicious Face Recognition Models

The proliferation of powerful facial recognition systems poses a serious threat to user privacy. Attackers could train highly accurate facial recognition models using public data on social platforms. Therefore, recent works have proposed image pre-processing techniques to protect user privacy. Without affecting people's normal viewing, these techniques add special noises into images, so that it would be difficult for attackers to train models with high accuracy. However, existing protection techniques are mainly designed for image data protection, and they cannot be directly applied for video data because of high computational overhead. In this paper, we propose an efficient protection method for video privacy that exploits unique features of video protection to eliminate computation redundancy for computational acceleration. The evaluation results under various benchmarks demonstrate that our method significantly outperforms the traditional methods by reducing computation overhead by 35.5%.
Analysing Finnish Cybersecurity Thesis Topics Using Taxonomic Frameworks

This paper presents an analysis of Bachelor’s and Master’s cybersecurity theses in Jyväskylä, Finland. The theses were gathered from publicly available publishing platforms of Finnish universities and were analysed using the NICE Cybersecurity Workforce Framework (NCWF) categories and European Cyber Security Organization’s (ECSO) The European Cybersecurity Taxonomy. The aim of this research was to find whether there clearly were emphasis on certain framework categories or work roles. Similarly, industry sectors about which cybersecurity theses were done were of interest. The results can be used by education providers to align and plan their education based on regional needs, and cybersecurity students, before starting their thesis project, can use this information to deliberate suitable work sectors in which theses are lacking. As our research results point out, there is a clear emphasis on certain NICE categories and work roles that are more common within the dataset. However, it is prudent to take into account the scope of the dataset, which was specific to one region in Finland. While this research presents findings about this one region, researchers from around the world can consider using the same research methods on a similar datasets gathered from their respective regions.
Design of a Modular Integrated System for Augmented Tourism Destinations

In the tourism sector, identifying and promoting relevant attractions and points of interest to visitors, especially through AR/VR technologies, has been proven to enhance the on-site overall visiting experience. Throughout this paper, the use of multimedia, augmented and virtual reality content is presented, as a means to improve the experience of both on-site and remote (website) visitors. Therefore, a generally applicable platform, consisting of interconnected modules and based on open source technologies is proposed, which in turn allows the end-users to provide their own content and stakeholders to gather meaningful metrics and feedback.
A Study on the Factors Development of the Prediction Model of Freight Volume Using the Deep Learning

Due to the recent slowdown in the delivery market, there is a movement to increase profitability through cost reduction through the efficiency of network infrastructure construction, manpower, and resource operation rather than increasing profits by attracting new customers. As the channels in the delivery industry diversify, more than 5 million parcels are generated per day. Using the data generated in this process, a scientific and accurate analysis method is required, not a passive prediction method. In this study, it is expected to be used for efficient network infrastructure construction, manpower, and resource operation in courier companies by developing a demand prediction model based on the Recurrent Neural Network using time series data.
E-Commerce Product Matching at Internet Scale

In E-Commerce, Product Matching is one of the fundamental problems for various use cases like (1) Competitive pricing of products, (2) deduplication of products in catalog, (3) grouping items from various merchants (4) Recommending products. The requirement is to match a product accurately against a catalog spread across tens of thousands of taxonomy nodes and millions of items. Product matching results must be accurate, and the margin for error is minimal to use Product Matching across use cases. This paper proposes a combination of Deep Learning models integrated into the scalable architecture to achieve the required results. Here we have approached the problem at the grass-root level consisting of five stages (1) Identifying attributes per taxonomy node (2) classification of products (3) Attribute Enrichment from NER (Text) and Image feature extraction (4) Search against multiple indices and filter results for mandatory attributes (5) Re-rank to improve the relevancy of shortlisted results. We have defined product data quality and measured it at every stage to improve the overall performance of Product Matching. This approach has yielded accurate product matching results at scale minimising false-positives
Nonlinear Multi-Model Reuse

The goal of model reuse is to build a model in a new target domain by reusing some pre-trained source models. It can significantly reduce the training costs and the data required for training, and hence has various potential applications. Most of the existing model reuse approaches only reuse the output features or labels of the source model, and more information contained in the model are ignored. Besides, only a single model can be utilized in these approaches. A recently proposed multi-model reuse method is able to remedy these drawbacks by utilizing the hidden layer representations of multiple source models to help improve the representations in the target model, but it assumes that there are linear connections between the source and target models. This assumption is too restrictive and may be not valid in real-world applications. In this paper, we relax this assumption by introducing the manifold regularization scheme to exploit arbitrary nonlinear relationships between the source and target models. Effectiveness of our method is demonstrated empirically by the extensive experiments in the popular person re-identification task for smart city application.
Research on Geometry Problem Text Understanding Based on Bidirectional LSTM-CRF

The challenge of enabling a computer to understand a geometry problem in natural language has been attracting increasingly attention. As society becomes more data oriented, knowledge graphs (KG) that represents structured relations between entities are also gaining ground. However, it is still brand new that to combine knowledge graph with geometry problem understanding. Focusing on understanding geometry problem text in Chinese natural language, we propose two methods: (1) three-level representation normalization; (2) acquiring structured geometric relation by integrating entity extraction using Bilstm-CRF and constructing relation triad using template mapping. Based on these, we construct a system that implements obtaining geometric relation triads and finally put them in knowledge graph. With our three-level representation normalization, the accuracy in its subsequent processing, entity recognition and extraction, has significantly and constantly improved about 3 percent compared with directly processing on the raw problem text with simple data cleaning. The results of the final test confirm the possibility that the system successfully recognizes and obtains all geometry relation triads of each problem sentence in the entire testing set, achieving complete transformation from an unstructured geometry problem text to a structured relation triad set.
Impact of the Implementation of Resources with Augmented Reality in Education

Augmented reality (AR) has shown positive effects in education. In the present investigation, the results of an educational intervention, which consisted in the implementation of AR resources in the teaching process, are presented. The research had as objectives A) to analyze students’ perspectives on the implementation of AR in the educational field; B) to design resources with AR with the participants, as products of their projects; and C) to compare the performance of students doing projects with AR with their performance in a project done previously. The study was carried out with a population of 39 students between 12 and 16 years old who developed research projects on various topics in which they had to develop a product as the result of their research. The duration of the project was 6 weeks, for which the students worked in groups with the PBL methodology. To collect information about the learning experience, a survey was applied to the participants at the end of the intervention. Products, because of the project, were developed and presented by the students, and the grades obtained in the development of the project and qualifications obtained in previous projects were compared. Once the results were analyzed, it was evident that A) the vast majority of students surveyed gave answers in favor of the use of the technology to improve their motivation to learn, which significantly improved their willingness to carry out learning activities; B) the products developed were brochures and posters assembled with AR resources; and C) an increase of 2.24 points in the course average occurred in relation to the previous project.
Development of Animation as a Medium for Public Health Education

One of the diseases still suffered by many Indonesian people, especially in the city of Depok, is respiratory disease. Currently, respiratory tract disease is a case with a high number of sufferers. An effort to handle these cases is to provide counseling and education for the community. This education aims to enable people to apply a healthy lifestyle independently and prevent various types of diseases. During the COVID-19 pandemic, this role was somewhat difficult to carry out due to limited movement and space. As a solution to this problem, the part of information technology is crucial. One is utilizing multimedia technology as a medium for health education for the community, namely animation. Animation can describe and visualize something so that information can be presented clearly and visually. This study aims to develop animation as a medium of health education for the community, especially in respiratory health. The animation video was generated using the MDLC (Multimedia Development Life Cycle) method. The resulting animated video can inform the public about health that focuses on the health of the respiratory system. 85 % of users stated that this animated video helps them to understand the material presented.
Sharing Device Identification on Images from Social Media Platforms

With social networks reaching unprecedented numbers of active users and data traffic worldwide, forensic scientists have been working to secure the trustworthiness of online information against the threat of misinformation. Several works have already investigated the possibility to trace malicious contents back to its perpetrators by following the chain of sharing operations. In real-world scenarios, however, users also have the chance to upload images in multiple ways, such as via smartphone apps or desktop browsers. Being able to detect different sharing modalities may represent a valuable and still unexplored insight for forensic purposes. Following this line, in this work we present SHADE, a first collection of real-world images shared from different devices, operating systems, and user interfaces. This database provides the forensic community with a new asset for investigating the peculiar artifacts introduced by different sharing modes, and for validating detection algorithms. The dataset was evaluated by applying a set of feature descriptors borrowed from platform provenance analysis, allowing us to reach promising results in the classification of sharing modalities.
Context-Aware Human Activity Recognition (CA-HAR) Using Smartphone Built-In Sensors

Context-awareness has the potential to enhance human activity recognition (HAR) by identifying daily activities such as driving, studying, cooking, or showering. Most existing context-aware HAR approaches that utilize smartphone sensors assume that the phone is placed on certain locations on the body such as trouser pockets, attached to the waist or arm, or held by hand. However, when the smartphone is no longer worn by the person, recognizing human activities becomes a challenging task. This paper proposes a context-aware human activity recognition (CA-HAR) approach to recognize human activities even when the smartphone is no longer placed on the body. The CA-HAR approach performs aggregation of multiple sensor data from the smartphone to recognize human activities by applying deep learning and ripple-down rules (RDR). It uses a context-activity model to build and formulate the RDR rules that consider additional contextual information to deal with the on-body location problem. The paper presents a proof-of-concept implementation of the CA-HAR as an Android app and discusses two types of evaluations that were conducted to validate the performance of CA-HAR.
Resolving Copycat Problems in Visual Imitation Learning via Residual Action Prediction

Imitation learning is a widely used policy learning method that enables intelligent agents to acquire complex skills from expert demonstrations. The input to the imitation learning algorithm is usually composed of both the current observation and historical observations since the most recent observation might not contain enough information. This is especially the case with image observations, where a single image only includes one view of the scene, and it suffers from a lack of motion information and object occlusions. In theory, providing multiple observations to the imitation learning agent will lead to better performance. However, surprisingly people find that sometimes imitation from observation histories performs worse than imitation from the most recent observation. In this paper, we explain this phenomenon from the information flow within the neural network perspective. We also propose a novel imitation learning neural network architecture that does not suffer from this issue by design. Furthermore, our method scales to high-dimensional image observations. Finally, we benchmark our approach on two widely used simulators, CARLA and MuJoCo, and it successfully alleviates the copycat problem and surpasses the existing solutions.
Tutorial: LLVM for Security Practitioners

Many security researchers need to build tools that analyze and transform code. For example, researchers may want to build security hardening tools, tools that find vulnerabilities within software, or tools that prove that a program is invulnerable to attack. This tutorial will guide attendees through creating extensions to the LLVM compiler that perform simple analysis and transformation operations.
Real-time Measurement Technology of Digital Speckle Deformation Based on Fourier Transform

This paper proposes a real-time measurement technology of digital speckle deformation based on Fourier transform by slightly improving the existing out-of-plane deformation measurement technology of planar objects. The speckle image collected by the CCD is subjected to carrier elimination, inverse Fourier transform and other methods to obtain the speckle interference fringes with low noise and high contrast. The experimental results of the out-of-plane deformation measurement of the aluminum plate show that the technology can measure the real-time deformation of the tested piece.