Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Forward and Backward Private Searchable Encryption using SGX

Dynamic searchable encryption (DSE) schemes enable users to search over encrypted data that are outsourced to untrusted cloud servers with the support of data dynamics such as addition and deletion. Although software-based DSE schemes have been developed to improve security and functionality, many of them still has limitations in terms of efficiency and information leakage. In order to overcome such issues, DSE schemes leveraging trusted execution environment (TEE) such as Intel SGX have been introduced. In this paper, we investigate the existing SGX-based dynamic searchable encryption schemes and conduct a comparative analysis of them. Based on the analysis result, we discuss the challenges and open problems in the literature. and future research directions.
Accelerating Face De-identification System for Real-time Video Surveillance Services

With an increased interest in personal information protection, there are numerous efforts to reduce personal information leakage caused by existing video surveillance services that continuously monitor and analyze multiple video streams at a surveillance control center. In particular, the face images included in the video stream are the most vulnerable information to easily identify specific personal information. In this paper, we propose a real-time face de-identification system that finds the locations of faces through a face detection deep learning model and modifies the corresponding pixels to make the faces unrecognizable. In order to accelerate the overall process, we apply TVM-based deep learning inference optimization and task-level pipeline parallelism to the proposed face de-identification system. Throughout experimental results, we show the effectiveness of the proposed system for providing face de-identification service in real-time.
Immersive Body-Hand Gesture Interfaces for HMD- Based Virtual Environment Navigation

In order to experience Virtual Reality (VR) contents realistically, a Head-Mounted Display (HMD) user should be able to immerse themselves in a virtual environment. Gesture interfaces allow the user to immerse in the virtual environment by recognizing and using the user's movement. To maximize this effect, we propose a new gesture interface that can take the advantages of both hand and body gestures for the navigation of virtual environments. Experimental results show that the proposed gesture interface has expanded the available virtual environment, increasing the immersion.
Statistical Fluctuation Analysis for Quantum Secure Direct Communication with Entanglement source and Single-photon Measurement

Quantum communication is a technology that trans-mits quantum states. Quantum secure direct communication is a promising application because unlike Quantum key distribution, a message can be transmitted directly through a quantum channel. The secrecy capacity of the QSDC protocol can be estimated using the decoy state. However, the secrecy capacity of the practical QSDC protocol is lowered due to the finite-size effect. In this study, we introduce QSDC protocol with an entanglement source and single-photon measurement and we analyze the secrecy capacity considering statistical fluctuation.
A Blockchain Sharding Protocol supporting Dynamic Locality in Mobile Edge Computing

With the proliferation of mobile edge computing (MEC) in 5G networks, there has been growing interest in deploying high performance blockchain sharding protocols over MEC. However, despite numerous blockchain protocols being proposed, most do not consider workload characteristics that exhibit locality and mobility commonly found in MEC environments. We claim that the existing protocols are missing opportunities for performance improvement, e.g., exploiting locality, and have significant performance degradation by not considering dynamically changing locality, e.g., user mobility, resulting in more and more costly cross-shard transactions with remote access in the system. This paper proposes MEC-Chain, a novel blockchain sharding framework that effectively exploits locality and mobility for high performance. MEC-Chain hierarchically organizes sharded local chains, each assigned to state-ownership for locality-aware execution and fine-grained state resharding with the main chain. For exploiting locality, we design a hybrid processing model that allows fast local updates of blockchain state only when its ownership is assigned to the local chain. Otherwise, i.e., the cross-shard transaction is executed and validated against the globally consistent main chain. For handling mobility, we introduce a finegrained state resharding protocol that transfers state ownership dynamically between local chains in a fine-grained way with the help of users. By doing so, our proposal can efficiently handle a common workload that exhibits a high degree of locality that changes gradually over time.
Convolutional Residual Blocks With Edge Guidance for Image Denoising

We propose an image denoising Convolutional Neural Network (CNN), comprises of alternative vertical and horizontal Sobel edge guided residual blocks. The input images are corrupted by additive white Gaussian noise. Initially, the first convolutional layer maps three channel (RGB) input noisy image into depth feature maps. Next, each residual block extracts vertical/horizontal edge using the Sobel edge mask. The edge guidance helps in preserving edges and textures in the given input image. Further, the input depth feature map along with corresponding edges are fed into simple <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1\times 1,1\times 3,3\times$</tex> 1 and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\times 3$</tex> convolutional layers with residual connections. Usage of simple convolutional blocks reduces the overall network parameter count, while maintaining the better performance. The simple and efficient design of the proposed residual convolution block can easily be plugged into any other image reconstruction networks. Experimental results show that the proposed model is more effective in preserving textures and edges while removlng noise.
Graph Feature Generation based on Scene Graph Benchmark Application on Video

In the field of vision research, it is considered that the analysis of scene graphs can help machines comprehend higher-level visual scene contexts. The data structure that both detects objects and defines their relationships is called a scene graph. The scene graph has been applied to diverse tasks, including action recognition, image captioning and visual question answering. It is also suggested as a method for understanding the video context. The amount of video data needed for training to produce a scene graph is nonetheless insufficient. Therefore, the inference model was applied to real-world data, and the possibility of usage of scene graph as a visual feature was identified.
Stance Detection of Political Tweets with Transformer Architectures

The online actions and words of a person can reveal their political sentiments and how they may vote at the polls. For decades, the dominant strategy of determining voter sentiment on policies relied on slow and often inaccurate polling. The creation and subsequent popularity of numerous social media sites, namely Twitter, has presented an opportunity for researchers to apply machine learning models to identify voter stances towards relevant political issues. Stance detection is a sub-task of natural language processing that involves algorithmically determining the stance that a text contains towards a given topic. With recent developments in NLP models and architectures, prior researchers have successfully trained stance detection models to predict the winning candidates in national-level elections. However, the viability of stance detection towards specific policies in city-level and state-level elections is relatively unexplored. In this paper, we train a novel transformer neural network architecture that accurately classifies Twitter users' stances towards Proposition 16 of California's 2020 election. To that end, we created a novel annotated data set of all Tweets regarding Proposition 16. Because not all tweets were opinionated, we also trained a model to filter out irrelevant and neutral tweets. Ultimately, we achieved 82% overall accuracy. Moreover, our model accurately predicted the result of the Prop 16 election. Our results show that a stance detection model can provide a unique perspective to help politicians make decisions that represent their constituents.
A Comparison of 2 Step Classification with 3-Class Classification for Webpage Classification

The content over internet increasing significantly each year and the web page classification is an essential areas of work upon for web-based information management, content retrieval, data scrapping, content filtering, advertisement removal, contextual advertising, expanding web directories etc. Multiclass classification methods is more popular and commonly use to classify web pages, and 2 step classification is our proposed system. In 2 step classifier, we use 2 primary model which works serially and perform binary classification at each level. The primary source of dataset contain thousands of URL(Uniform Resource Locator) of web pages. The content on webpage is extracted and stored on system to avoid the loss of data sue to the change in URL. The comparison between the two methods validated the system improvement and improved in different metric such as precision and recall using 2 step classification technique. 2 step classification technique is faster while training and also shows performance improvement. There proposed system shows improvement in the performance of the results but not something significant.
Dimensional reduction algorithm using feature selection and matrix decomposition

In machine learning area, high dimension problem is difficult to handle because of need of large memory space and taking much time when learning algorithm. Moreover, the algorithm accuracy can decrease. In this paper, we propose an effective matrix decomposition to reduce dimension. Using unsupervised matrix decomposition, machine learning algorithm can enhance accuracy. To apply the matrix decomposition, we use unsupervised feature selection as a pre-processing. In this feature selection process, we can select major features to be decomposed. Using this process, we can derive effective matrix decomposition for supervised learning. To verity the proposed method, we conduct classification experiment with three data sets. Our experimental result showed high accuracy when the unsupervised feature selection is applied.
Generating Curated Photo Collection using Shot Type Pattern

Smartphones are broadly used to take photos frequently in our real lives. As a result, the need for organizing the photos piled up on smartphones is rapidly increasing. To solve this, curated photo collections of user-taken photos based on significant people, places, and events is widely used in recent smartphones. But mostly they generate simple sequences of photos with background music. In this paper, we propose an automatic curated photo collection generation method that utilizes shot type patterns generally used in commercial film making. Through this, the users can enjoy the photo collection organized in a professional cinematographic way. It also provides an improved experience from an aesthetic point of view.
Learning to Intrinsic Image Filter for Instagram Filter Removal

The filter removal task is important because filtered images risks degrading the performance of the computer vision model. We propose a two-branch model which performs filter removal task. Our two-branch model consists of a Palette based Un-filtering Model (PUM) and a Palette Injection Model (PIM). PUM learns an intrinsic filter from the input image using the color palette. It is simple and fast to remove the filter from the input with the learned intrinsic filter. PIM has VGG baseline model as an encoder, and injects palette on each decoding stage. The output is an unfiltered image itself. Our method fuses these two unfiltered results and obtains the final result. This ensures that the filter is removed accurately and the structure of image is maintained. As a result of the experiment, our proposed model performs better on filter removal task than other recent models.
YOLOX-based fast and lightweight single-class object detector

In general, an object detector performs a series of processes finding regions estimated as objects in the image and specifying them as a predefined class. However, in the case of detecting only a single class such as a pedestrian detector or a license plate detector, it may not classify the estimated regions. Based on this idea, we developed a fast and lightweight single-class object detector with high processing speed while minimizing the loss of accuracy by excluding the class prediction layer from the YOLOX network and training the regression layer to respond only to the target class. As can be seen from the experimental results, the proposed method shows similar precision while having a faster inference speed compared to the original YOLOX model. It can be applied to mobile applications that lack resources but require a fast processing speed.
Breaching GPU data on a cloud VM

Recently, the use of GPU-intensive applications such as machine learning is increasing in the cloud virtual machine environment. To mitigate high virtualization overhead from GPU devices emulation, PCI pass-through technologies are introduced, which allows of direct access to physical device from the guest OS. QEMU, a virtual machine monitor, implements multiple PCI pass-through methods, and VFIO is one of popular options for virtual GPU devices. Although VFIO with PCI pass-through virtualization provides efficient GPU performance on the guest OS, there is a concern for the device isolation between the virtual machine boundary. If an attacker in the host OS can access the GPU used by the virtual machine, the attacker can compromise the integrity and confidentiality of the virtual machine. In this paper, we analyze and demonstrate the vulnerability in the current GPU pass-through devices, which can breach the user data in the GPU's VRAM, and compromise the guest PCI configuration.
Overview of the Volumetric Video Capturing System for Immersive Media

Immersive videos shown through virtual reality (VR) devices provide users with realistic viewing experiences. This paper focuses on the capturing process in an immersive video producing workflow. First, it introduces depth estimating technology and models of RGB-D cameras, as well as the general components of VR studios. Next, representative VR studios, such as studio of Microsoft and Fraunhofer Heinrich Hertz Institute, are introduced with a comparison of the used equipment. Furthermore, along with services such as VCL3D and Livescan3D, which provide a portable and flexible capturing environment, a system that produces videos for VR with only a single device is introduced.
An Assessment of Graph Neural Networks for Detecting Pointer and Type Errors

As the number of reported vulnerabilities continues to rise, detecting vulnerabilities remains a significant research challenge. Due to the complexity of current software systems, it becomes more difficult to detect vulnerabilities quickly and precisely using conventional methods such as static program analysis or text-based pattern matching. Recently, graph neural networks (GNN) have attracted much attention in vulnerability identification because they can manage graph structures that can represent different information flows and their relationships within a program. Due to the varying types of vulnerabilities and graph topologies of a program, it is challenging to select an appropriate graph neural network model for vulnerability identification. In this paper, we compare four GNN models to detect vulnerabilities including GCN, GraphSAGE, FastGCN, and AS-GCN. To train these models, we employ eight CWEs that can be categorized into two groups pertaining to pointer and type errors. Based on our experimental results, GraphSAGE shows better performance than other models in most cases, however GCN-based models show better results for some CWEs. As a result, our comparative study can be used as an indicator to select an appropriate model in vulnerability detection using graph neural networks.
A Study on Graph-based Classification for Important Technical Documents

In an era of technological hegemony, protecting innovative technologies worldwide has become an important issue. In particular, technical documents, including national core technologies, need to be protected separately from other general technical documents to maintain national technological competitiveness. Many studies on the classification of technical documents have been developed to analyze technological trends and classify the documents according to their types. However, since the existing methods have not considered the importance of documents, there is a limitation in that it is difficult to protect sensitive information such as national core technology. Therefore, in this study, we propose a new graph-based methodology that can perform binary classification of technical documents based on their importance. On top of that, we focus on explaining three steps of the proposed process using the properties of the graph network: graph representation, keyword extraction, and graph-based classification. We experimented with extracting keywords from each display and OLED document and classifying the test OLED document using PageRank and betweenness centrality. In conclusion, we show that our proposed method can effectively protect important documents in the technology industry by correctly classifying the class of a new document through the keywords we extracted from our experiments.
A Survey on Vulnerabilities of Service Workers

In a Progressive Web App (PWA), a kind of application software of the web, a service worker (SW) plays a key role as a one of the fundamental components to enhance the user's browsing experiences. For this purpose, the SW supports several features such as push notification, offline access, background code execution, etc. Since the SW provides prolific capabilities, it has been the main target to abuse by malicious attackers to deliver diverse attacks through the web applications such as crypto-currency mining, history sniffing, phishing. In this paper, we introduce the SW's functionalities and vulnerabilities, and discuss the existing attack methodologies and their implications.
A Prediction based Autoscaling in Serverless Computing

Nowadays, serverless computing has risen rapidly as an emerging cloud computing paradigm in recent years. Auto-scaling is a crucial enabler for adapting to workload changes by scaling instances corresponding to the number of incoming requests. Recently, a popular Kubernetes-based platform for managing serverless workloads known as Knative has been proposed. Its scaling algorithm increases and decreases resources based on configured parameter values such as a maximum number of requests that can be processed in parallel per pod. However, users have no idea about the appropriate parameter values, while those predefined values can strongly impact the performance. The negative influence can cause response time increase and throughput reduction if those are overprovisioning or under-provisioning. Besides, Knative uses a moving average method to calculate the number of pods based on past data but cannot reflect the future workload trend, leading to the delay effect. In this paper, we focus on maximizing performance with the lowest resource utilization and optimizing response time to satisfy the quality of service (QoS) requirements so that it is content with users' experience while users only must pay the least cost. To do that, our proposed approach first finds effective scaling policies per pod to maximize the performance with the acceptable latency. Then we apply the forecasting model Bi-LSTM to optimize the number of pods calculation and make Knative more adaptive to the workload changes to reduce the delay time. Our preliminary experiments show that our proposed model can significantly improve performance with an effective scaling policy and an adaptive pod calculation method compared to the default Knative auto-scaling scheme.
Gradient Surgery for Text-Dependent Speaker Verification: A Preliminary Study

We propose a method to apply gradient surgery to text-dependent speaker verification. Most text-dependent speaker verification models, whose goal is to discriminate both speaker and phrase, have been built with a multi-task learning approach. This approach enables a single model to discriminative between both speaker and phrase. During multitask learning, however, there could be gradient conflicts, because the aims of speaker classification and phrase classification are opposite. To mitigate the conflict, we perform gradient surgery. Experimental results using Part 2 of RSR 2015 dataset showed relative improvements of up to 2.51%.
Automatic Modulation Classification for Aggregated PDSCH in 5G New Radio

In this paper, we propose a new automatic modulation classification (AMC) scheme for aggregated physical downlink shared channel (PDSCH) in 5G New Radio (NR). First, we develop a generic convolutional neural network (CNN) structure for classifying modulation types specified in 5G NR which are QPSK, 16QAM, 64QAM, and 256 QAM. The received n aggregated PDSCHs are divided into multiple segments, then the received symbols within same group are softly combined together. After that, a set of combined symbols are input to the CNN-based classifier. Then, the classification results are further fused in three different ways to improve the classification accuracy. From the simulations, it is observed that the classification accuracy can be maximized by adjusting the soft-combining level for the received PDSCHs considering trade-off between signal-to-noise ratio gain and diversity gain.
Improving the performance of door detection in color images using deep neural networks and data augmentation techniques on various hardware platforms

Door detection is a crucial task of several automatic systems (e.g., robotic navigation, visual impaired support systems). The paper presents the improvements of the door detection in color images. Firstly, various deep neural networks have been investigated to improve the detection accuracy. Secondly, the data augmentation technique is applied to improve the performance of deep neural networks (DNNs). Finally, the door detection is developed on various platforms including the general-purpose graphics processing unit (GPGPU), Jetson nano and Neural Processing Unit (NPU) VIM3 to analyse the computational time and resources. The proposed method has been evaluated and compared with conventional methods on a public dataset (DeepDoor2) to demonstrate the effectiveness and robustness.