Reviewerly/exHarmony huggingface dataset:
MetaFake: Few-shot Face Forgery Detection with Meta Learning

With remarkable progress achieved by facial forgery technologies, their potential security risks cause serious concern to society since they can easily fool face recognition systems and even human beings. Current forgery detection methods have achieved excellent performance when training with a large-scale database. However, they usually fail to give correct predictions in real applications where only a few fake samples created by unseen forgery methods are available. In this paper, we propose a novel method to boost the performance of identifying samples generated by unseen techniques, dubbed MetaFake, which requires only a few fake samples. Our MetaFake enjoys the part features located by meta forgery prototypes created adaptively based on each task. The local-aggregated module helps to integrate these part features for the final prediction. Besides, we establish a large database of about 0.6 million images to verify the proposed method, including fake samples synthesized by 18 forgery techniques. Extensive experiments demonstrate the superior performance of the proposed method.
茶室設計コンペティションとセルフビルドによる実践教育 その１：間伐材の茶室

This study was a self-build project of the tea house that selected as the first prize of competition held by the faculty and reunion of graduates. The theme of the competition was thinned timber, the material produced in Osaka prefecture, where Kindai University situate. The study was a collaboration between local Industry and University.
STUDY ON PEDESTRIAN BEHAVIORAL MEASUREMENT SYSTEM BY USING OPENPOSE

There is a growing demand for technology to measure human behavior. To measure it with greater accuracy, we need to measure micro and macro behavioral characteristics at the same time. This system uses a pose estimation library called OpenPose based on the obtained joint information. We build a real-time system to measure the number of people and their walking trajectories through IoT cameras.
塩化ビニル管の繰返し曲げ挙動

Many buildings have been reported to stop their service due to damage to non-structural members such as equipment piping after large-scale earthquakes. Although data on deformation capacity and strength of carbon steel pipe for ordinary piping is being collected, few studies are conducted on polyvinyl chloride pipes (PVC pipes) for drainage. Cyclic bending tests are conducted of PVC pipes with nominal diameters of 40A, 50A and 75A to evaluate their deformation capacity and strength. The result is that the deformation of PVC pipes is larger than that of steel pipes, and shows brittle failure.
Legal Document Summarization Using Ripple Down Rules

This paper presents an approach for legal document summarization using Ripple Down Rules(RDR). RDR is an Artificial Intelligence(AI) based approach and an alternative technique to Machine Learning(ML) algorithms for incrementally building the knowledge base. In this implementation, we have used RDR to develop an improving and increasing knowledge base of classification rules for assigning rhetorical role labels to the sentences in a legal document. The RDR rules for classification are developed using a set of syntactic, semantic and statistical features at word, sentence and document level. For each sentence in the legal document we have assigned an rhetorical role with the help of Ripple Down Rule. We have generated the final summary using the identified thirteen rhetorical roles. The proposed system is evaluated using 50 legal documents from four different domains. Experiments demonstrate that the RDR based Legal Document summarization approach has advantages over supervised and unsupervised ML techniques such as, independence from the need of annotated dataset and continuous updation of classification rules for rhetorical role labeling with the help of expert knowledge.
DNN Based Speaker Identification System Under Multi-Variability Speech Conditions

Speech is the natural source of information for human identification in most biometrics, forensics, and access control systems. Mismatch in speech data is one of the biggest challenges preventing speaker identification systems from being employed in real-world scenarios. This research explores how speaker identification tasks are affected by degraded speech. Our preliminary investigation into mismatch effects in training and test conditions utilizing the IIT-G database includes mismatch in sensor and speaking styles. Convolutional neural networks (CNNs) have surpassed traditional techniques in speaker identification (SI) systems in recent years. This paper proposes a novel architecture based on a VGG-like network for an end-to-end speaker identification system. The proposed architecture outperforms the statistical methods with an improvement of 8% accuracy in identifying the speakers. The results show that the suggested approach is more accurate than state-of-the-art speaker identification techniques and notable performance deterioration compared to the matched scenario.
On the Detection, Localization, and Reverse Engineering of Diverse Image Manipulations

With the abundance of imagery data captured in our daily life, there is an increasing amount of diverse manipulations being applied to imagery, including generative model based image generation and manipulation, adversarial attacks, classic image editing such as splicing, etc. As the imagery plays important roles in our society, it is necessary to understand whether, where and how a given image is manipulated. From the perspective of a defender, this talk will introduce our recent efforts on detecting these manipulations individually and jointly, as well as reverse engineering the various information regarding the manipulation process. We will also share some of the topics that warrant future research.
Applying a Zero-Knowledge Watermarking Protocol to Secure Elections

Zero-knowledge protocols for digital watermarking allow a copyright holder to prove that a signal is hidden in a cover without revealing the signal or where it is. This same approach can be applied outside of watermarking to other problems where anonymity is required, and in particular to secure elections. We outline steganographic zero-knowledge protocols---proving in zero knowledge the existence of a hidden object in a cover---and adapt them to prove that selected ballot values must exist within a volume of anonymized voter receipts. We also improve the space efficiency of such protocols by employing camouflaged elliptic curve cryptography.
An Improved Reversible Database Watermarking Method based on Histogram Shifting

Database watermarking is typically employed to address the issues of data theft, illegal replication, and copyright infringement that may arise during the sharing of databases. Unfortunately, the existing methods often cause permanent distortion to the original data, and it is challenging to strike a balance between the watermark embedding capacity and data distortion. Therefore, this paper proposes a reversible database watermarking method based on histogram shifting, rhombus prediction, and double embedding with high capacity and low distortion, called RPDE-HSW. By utilizing the rhombus prediction, we respectively constructed two prediction error histograms in each subgroup and expanded the watermark capacity through the adoption of double-layer embedding and single-bin embedding 2 bits. A scrambling algorithm is used to make the attribute value distribution more discretized, resulting in a sparse distribution of the database histogram. Subsequently, we optimized the selection rules for the watermark embedding carrier, effectively eliminating the redundant distortion caused by histogram shifting. Experimental results demonstrate that the proposed method achieves smaller data distortion and higher watermark embedding capacity, outperforming some other state-of-the-art works, and does not affect the classification results and data mining.
Perceptual Robust Hashing for Video Copy Detection with Unsupervised Learning

In this paper, we propose an end-to-end perceptual robust hashing scheme for video copy detection based on unsupervised learning. Firstly, the spatio-temporal information in videos is effectively fused and condensed into high-dimensional features through a 3D self-attention, multi-scale feature fusion model based on 3D-CNN, in which the Inception block and the 3D self-attention mechanism are integrated. Then, we calculate the correlation distances between the extracted features to differentiate perceptual contents. Based on the similarity relationship, we can dynamically generate the pseudo-labels and exploit them to further guide the model training for video hash generation. In addition, we design the dual constraints to make the hash code obtain satisfactory robustness and discrimination. Extensive experiments demonstrate that the proposed scheme achieves superior performance of copy detection compared with existing schemes and performs well even in the case of untrained manipulations.
集中系·分布系のカオス同期化を用いたRGB画像の秘匿通信

This paper is concerned with secure communication of RGB images using lumped parameter/distributed parameter chaotic synchronization. As a lumped parameter system generating chaos, we use a discrete-time system describing by a generalized Henon map and construct its synchronization system. Since the discrete-time chaotic system has three-dimensional state variables, it is convenient when designing a secure communication system for RGB images. However, the generalized Henon system and its synchronization system have only a few common encryption keys. To increase the common encryption key, we introduce another chaotic system described by a hyperbolic partial differential equation with logistic boundary condition. Several snapshots of the solution of the hyperbolic system are used to generate a long-length common encryption key of the secure communication system proposed in this paper.
Implementation of MIMO-OFDM system with deep learning based channel estimation and channel equalization

The advanced communication technologies like 5G and 6G need high speed data rates, high spectrum efficiency, and low error rates. But, the conventional Multiple Input Multiple Output (MIMO) systems are failed to meet these properties due to inefficient channel estimation methods. So, this work is focused on implementation of Hybrid MIMO-Orthogonal frequency division multiplexing (OFDM) system with optimal deep graph convolutional network (ODGCN) based channel estimation. Further, optimal hyper convolutional neural network (OHCNN) is used to equalize the resultant channel coefficients. Finally, OFDM demodulation operation is carried out to restore the original transmitted data. The overall prototype of proposed environment called as MIMO-OFDM channel estimation and equalization network (MOCEE-Net).The simulation results conducted using Matlab R2021a shows that the proposed MOCEE-Net method resulted in reduced Bit Error Rate (BER), Mean Square Error (MSE) performance as compared to state of art approaches for various parameters.
Vision transformer-based deep learning techniques for improper face mask-wearing detection

COVID-19 pandemic causes a global catastrophe that remarkably affects individual lives and society, as well as the economy. The world has taken numerous defenses against these contagious diseases and the use of facemasks is one of the most crucial defense mechanisms. However, this preventative strategy is fruitful if the face masks are worn correctly. Unfortunately, only less than 25% of people use masks correctly. In this paper, we proposed to apply a most recent deep learning-based image classification model named vision transformer to automatically detect improper face mask-wearing, i.e the facemasks are being worn correctly or not. In contrast with the traditional image processing or deep learning model, the vision transformer base model shows a significant impact on incorrect face mask detection. We did the experiment on a large custom dataset consisting of 2,03,780 digital images with 3 class labels namely With Mask (when people are wearing the mask properly), Without Mask (when people are not wearing the mask) and Incorrect Mask (when people are not wearing the mask properly) to train, validate and test vision transformer model that can classify the use of masks correctly. The results show that the accuracy achieved with pre-trained vision transformer models is highly remarkable.
WAMS: A Flexible API for Visual Workspaces Across Multiple Surfaces

Applications that use multiple devices and surfaces provide new opportunities for innovative interaction -- but despite the wide variety of research that has been carried out on multi-surface systems, building these kinds of applications is still difficult. In particular, multi-surface apps that use interactive visual workspaces are complicated because current tools do not provide low-level access to a connected and interactive graphical canvas that is shown on different devices. This difficulty limits the explorations that designers and developers can carry out within the multi-surface design space. To address this problem, we have developed WAMS -- an open-source web-based toolkit that provides several programming abstractions for building visual-workspace applications across multiple surfaces. WAMS uses three main concepts -- a virtual visual workspace, views onto that workspace, and graphical workspace objects -- and provides support for connecting multiple devices, creating and manipulating objects, managing and laying out views, and handling events from multiple surfaces. WAMS simplifies the development of a wide variety of applications including composite display configurations, shared-workspace groupware, systems that place different UI elements onto different devices, and bring-your-own-device applications. We describe WAMS's main abstractions and concepts, provide several examples that show the breadth of the approach, and assess the toolkit in terms of effectiveness, coverage, extensibility, and integration with existing practices and tools.
Incorporating topic membership in review rating prediction from unstructured data: a gradient boosting approach

Rating prediction is a crucial element of business analytics as it enables decision-makers to assess service performance based on expressive customer feedback. Enhancing rating score predictions and demand forecasting through incorporating performance features from verbatim text fields, particularly in service quality measurement and customer satisfaction modelling is a key objective in various areas of analytics. A range of methods has been identified in the literature for improving the predictability of customer feedback, including simple bag-of-words-based approaches and advanced supervised machine learning models, which are designed to work with response variables such as Likert-based rating scores. This paper presents a dynamic model that incorporates values from topic membership, an outcome variable from Latent Dirichlet Allocation, with sentiment analysis in an Extreme Gradient Boosting (XGBoost) model used for rating prediction. The results show that, by incorporating features from simple unsupervised machine learning approaches (LDA-based), an 86% prediction accuracy (AUC based) can be achieved on objective rating values. At the same time, a combination of polarity and single-topic membership can yield an even higher accuracy when compared with sentiment text detection tasks both at the document and sentence levels. This study carries significant practical implications since sentiment analysis tasks often require dictionary coverage and domain-specific adjustments depending on the task at hand. To further investigate this result, we used Shapley Additive Values to determine the additive predictability of topic membership values in combination with sentiment-based methods using a dataset of customer reviews from food delivery services.
Optimal Design of Circularly Symmetric and Elliptical 2d-Fir Filter Based on Salp Swarm Optimization Algorithm

In this paper an approach for the design and optimization of the performance of circular symmetric and elliptical 2D-FIR filters has been explained. For performance optimization of the FIR filter, the Salp Swarm optimization algorithm has been used. It shows better results in pass-band and stop-band ripples and significant improvement in the number of fitness function evaluations compared with other optimization algorithms such as ABC, GA, PSO, GFPA, CSA, etc.
The Identification of Cybersecurity Work Roles for the Water Sector in South Africa

This paper presents the results of a content analysis conducted on the work roles of cybersecurity practitioners for the water sector of South Africa. The paper presents literature review findings on national and international frameworks and guidelines detailing cyber security considerations for the South African water sector as well as national and international guidelines and frameworks which detail the various work roles carried out by cybersecurity practitioners in an organisation. The study found that cyber security considerations and work roles such as physical security of assets, testing and assessment of cybersecurity methods, supply chains cyber security as well as incident investigation and interfacing with law enforcement, were not well defined for cyber security discipline. The study delivers a framework detailing the work roles of cybersecurity practitioners which can be applied to the South African water sector.
Development of a Gaze-Driven Electric Wheelchair with 360° Camera and Novel Gaze Interface

A novel gaze-based user interface (UI) is proposed for the remote control of robots and electric wheelchairs. A task-based experiment showed that this UI is more suitable for remote control than a conventional interface. The UI was applied to the control of a commercially available low-cost electric wheelchair. By using a 360° camera and an eye tracker that can be used outdoors, the visibility of obstacles and the usability of the gaze-driven electric wheelchair were greatly improved. The gaze-driven electric wheelchair exhibited good performance in a task-based evaluation experiment.
Toward a Prosperous Future Where Humans and Robots Cooperate

Based on the roadmap formulated by “Consortium for Human-Centric Manufacturing Innovation” (here-in after referred to as HCMI Consortium) of National Institute of Advanced Industrial Science and Technology, an industry-academia-government collaborative platform launched on April 10, 2019, this paper introduces initiatives and future prospects for human-centric robotics that collaborate with humans and human-in-the-loop cyber-physical system to support future industrial infrastructure for a sustainable and prosperous society. In particular, in cooperation with humans, it is important to understand the behavior of workers. For this purpose, it is important to have a fundamental technology that can represent models of human in cyber-physical systems and simulate the relationship between human and machines. Based on this fundamental technology, we are proceeding with demonstrations in various use cases.
Enhancing Offensive Language Detection with Data Augmentation and Knowledge Distillation

Offensive language detection has received important attention and plays a crucial role in promoting healthy communication on social platforms, as well as promoting the safe deployment of large language models. Training data is the basis for developing detectors; however, the available offense-related dataset in Chinese is severely limited in terms of data scale and coverage when compared to English resources. This significantly affects the accuracy of Chinese offensive language detectors in practical applications, especially when dealing with hard cases or out-of-domain samples. To alleviate the limitations posed by available datasets, we introduce AugCOLD (Augmented Chinese Offensive Language Dataset), a large-scale unsupervised dataset containing 1 million samples gathered by data crawling and model generation. Furthermore, we employ a multiteacher distillation framework to enhance detection performance with unsupervised data. That is, we build multiple teachers with publicly accessible datasets and use them to assign soft labels to AugCOLD. The soft labels serve as a bridge for knowledge to be distilled from both AugCOLD and multiteacher to the student network, i.e., the final offensive detector. We conduct experiments on multiple public test sets and our well-designed hard tests, demonstrating that our proposal can effectively improve the generalization and robustness of the offensive language detector.
A Phenomenological Study of Learner's Virtual Place Experience

This study investigates the virtual place experience (VPE) approach, using 360° images in Google Street View (GSV), to help learners attain a sense of place. It examines their experience and attitude in the virtual setting of the place of knowledge inquiry. Interpretive phenomenological analysis (IPA) was adopted to examine learners' cognitive learning of place identity, interpretation of place meanings, and attitudes toward the place. Findings indicate that learners developed their sense of presence in VPE through visual elements reflecting the place identity. Place attribute learning requires additional research to understand place identity. Place meanings in the virtual setting were interpreted through the sense of community and historical evidence. Positive affection towards the place was identified, leading to motivation to visit the real place. The study provides insight into how learners' experience with the virtual place phenomenon may contribute to constructing their sense of place, which shall guide their design cognition in future design exercises.
Experience Design's Impact on Graphic Design

Even straightforward objects whose nature we believe we comprehend go through the design process. The task of design is to connect people and technology. Every advance in technology brings about new experiences and ways of life. In this chapter the authors explain how design alters the abilities, motivations, and expectations in daily life, and how graphic design reacts to this shift. If we want to anticipate the future, we need to look closely at the digital technologies that emerged recently and first analyze the transition—how it occurred and how quickly it happened.
Federated Clique Percolation for Privacy-preserving Overlapping Community Detection

Community structure is a typical characteristic of complex networks. Finding communities in complex networks has many important applications, such as the advertisement and recommendation based on social networks and the discovery of new protein molecules in biological networks, which make it a hot topic in the field of complex network analysis. With the increasing concerns about the leakage of personal privacy, discovering communities spread across the local networks owned by multiple participants accurately while preserving each participant’s privacy has become an emerging challenge in distributed community detection. In this article, we propose a general federated graph learning model for privacy-preserving distributed graph learning and develop two federated clique percolation algorithms (CPAs) based on it to discover overlapping communities distributed across multiple participants’ local networks without disclosing any participant’s network privacy. Homomorphic encryption and hash operation are used in combination to protect the privacy of the vertices and edges of each local network. Furthermore, vertex attributes are involved in the calculation of clique similarity and clique percolation when dealing with attributed networks. The experimental results on real-world and artificial datasets demonstrate that the proposed algorithms achieve identical results to those of their stand-alone counterparts and more than 200% higher accuracy than the simple distributed CPAs without federating learning.