Reviewerly/exHarmony huggingface dataset:
Exploring Disentangled Content Information for Face Forgery Detection

Convolutional neural network based face forgery detection methods have achieved remarkable results during training, but struggled to maintain comparable performance during testing. We observe that the detector is prone to focus more on content information than artifact traces, suggesting that the detector is sensitive to the intrinsic bias of the dataset, which leads to severe overfitting. Motivated by this key observation, we design an easily embeddable disentanglement framework for content information removal, and further propose a Content Consistency Constraint ( $$\text {C}^2$$ C) and a Global Representation Contrastive Constraint (GRCC) to enhance the independence of disentangled features. Furthermore, we cleverly construct two unbalanced datasets to investigate the impact of the content bias. Extensive visualizations and experiments demonstrate that our framework can not only ignore the interference of content information, but also guide the detector to mine suspicious artifact traces and achieve competitive performance.
A Dataset for Interactive Vision-Language Navigation with Unknown Command Feasibility

Vision-language navigation (VLN), in which an agent follows language instruction in a visual environment, has been studied under the premise that the input command is fully feasible in the environment. Yet in practice, a request may not be possible due to language ambiguity or environment changes. To study VLN with unknown command feasibility, we introduce a new dataset Mobile app Tasks with Iterative Feedback (MoTIF), where the goal is to complete a natural language command in a mobile app. Mobile apps provide a scalable domain to study real downstream uses of VLN methods. Moreover, mobile app commands provide instruction for interactive navigation, as they result in action sequences with state changes via clicking, typing, or swiping. MoTIF is the first to include feasibility annotations, containing both binary feasibility labels and fine-grained labels for why tasks are unsatisfiable. We further collect follow-up questions for ambiguous queries to enable research on task uncertainty resolution. Equipped with our dataset, we propose the new problem of feasibility prediction, in which a natural language instruction and multimodal app environment are used to predict command feasibility. MoTIF provides a more realistic app dataset as it contains many diverse environments, high-level goals, and longer action sequences than prior work. We evaluate interactive VLN methods using MoTIF, quantify the generalization ability of current approaches to new app environments, and measure the effect of task feasibility on navigation performance.
DCT-based compression algorithm using reduced adaptive block scanning for color image

This paper presents a lossy image compression algorithm dedicated to color still images. Based on DCT, the proposed method utilizes a simple technique to encode efficiently the DCT coefficients. After the step of the RGB to YCbCr transformation, the DCT transform is applied. The desired quality is assured by using the bisection method to threshold the quantized DCT coefficients. Two scan orders (zigzag and vertical) are used to read the DCT coefficients from the retained DCT block coefficients. Following a scan order, an index vector is formed by the length of the zero-run sequence that preceded a nonzero DCT coefficient. The lowest value of the two index vectors maximums determines the best scan. Finally, the nonzero DCT coefficients and the index vector for each block are encoded to form the compressed image. When compared with recent methods, the obtained results show that the proposed algorithm achieves high performance.
Web Scraping based Product Comparison Model for E-Commerce Websites

Existing e-commerce applications offer various functionalities to buy any products from their websites. However, a comparison for any product in terms of price offers, and quality among these applications is time-consuming and involves the user’s time to check reviews and surf other websites to check prices. The objective of this paper is to propose a web application that identifies those basic details for any product from different e-commerce websites. These details are compared, and the result is displayed to the user graphically for the final decision. The proposed web application uses the web-scraping methodology with selenium and is implemented on the python framework using various algorithms and techniques which are discussed in the paper. As an outcome, the system will give the simulation results, so that the user can get the recommendations on purchasing the relevant product with better user satisfaction and in minimum clicks and time.
Blockchain-Powered Bandwidth Trading on SDN-Enabled Edge Network

Bandwidth trading procedures can be made to incentivize users to sell their needless traffic and indirectly reduce the probability of traffic congestion.However, implementation of bandwidth trading is opex-heavy from Internet Service Provider (ISP) perspective, while on the other hand, users also do not trust network executions from the ISP due to its heavily centralized control.These issues hinder the applicability of bandwidth trading and become our motivation to propose this paper.Our bandwidth-trading framework utilize software-defined networking (SDN) and blockchain.SDN automates the bandwidth trading executions from the ISP side and reduces the opex.Meanwhile, the smart contract is a trusted platform for building a trading marketplace where buyers, sellers, and SDN controllers can negotiate the trading terms.Once the trading is executed, SDN controllers generate proof of trading that must be submitted to the smart contract as proof of provisioning.We implement our works using Ethereum and POX SDN controllers, and the results prove that it can provide a seamless bandwidth trading experience with reasonable overhead.Furthermore, by committing to our framework, bandwidth trading can be executed fairly and securely because all previous provisioning can be cross-checked through the provided proof-oftrading.
LP-HPA:Load Predict-Horizontal Pod Autoscaler for Container Elastic Scaling

In the cloud environment, application elastic scaling is very important. The number of copies can be dynamically adjusted according to load. A good elastic scaling scheme can not only ensure the stability of application, but also improve resource utilization of platform. The existing responsive scaling strategy of Kubernetes platform has many problems, which can not meet requirements of web system for service quality. This paper optimizes the default elastic scaling scheme in Kubernetes cluster, and proposes a container dynamic scaling scheme LP-HPA (load predict horizon pod autoscaling) based on load prediction. This scheme uses LSTM-GRU model to predict the application load, comprehensively considers predicted data and current data, realizes dynamic scaling of container, and ensures the service quality of application. Finally, by building Kubernetes cluster, this paper uses open source data set to verify the LP-HPA scheme. Experimental results show that our proposed scheme is better than Kubernetes' default scaling scheme in three scenarios: load rise, load drop and load jitter.
Frequency-Domain Deep Guided Image Denoising

Despite the tremendous advances in image denoising techniques, there still remains a challenge to restore a clean image with salient structures based only on one single noisy observation, especially at high noise levels. In this work, to address this issue, we propose a novel frequency-domain guided denoising algorithm to conduct denoising with the help of an additional well-aligned guidance image. Thanks to the structural correlation between the input paired images, the frequency characteristics of the guidance image can indicate whether the frequency coefficients of the noisy target image are contributed by noise or textures. Therefore, the explicit frequency decomposition enables our model to avoid over-smoothing those detailed contents when removing noise. However, as two input images are usually captured in different fields, their structures are not always consistent with each other. Therefore, we model guided denoising with an optimization problem which takes into account both the representation model with respect to the guidance image and the structural fidelity to the noisy target image. Based on this, we further design a convolutional neural network, called as FGDNet, to explore the optimal solution. Due to the visual masking phenomenon, the human visual system is sensitive to noise in the flat areas, but may not perceive noise around edges or textures. Therefore, to handle a denoising task, we expect to remove as much noise as possible to guarantee the spatial smoothness of the flat contents, while preserving the high-frequency structures as well. Through frequency decomposition, our model can process the low-frequency and high-frequency contents separately with different denoising schemes. In the training stage, we also adopt a frequency-relevant loss function to optimize the network. Experimental results show that, compared with the state-of-the-art guided and non-guided denoising algorithms, our FGDNet can achieve higher denoising accuracy and also better visual quality in both flat and texture-rich regions. Source codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lustrouselixir/FGDNet</uri> .
A Hybrid A* Path Planning Algorithm Based on Multi-objective Constraints

To provide a safe, smooth and efficient global planning path for nonholonomic mobile robots. Aiming at the problems of traditional hybrid $A^{*}$ algorithm in path planning, such as approaching obstacles, unnecessary reversing and redundant turning, a multi-objective constraint method based on hybrid $A^{*}$ algorithm is proposed. To speed up the path planning, the heuristic function is dynamically weighted and the overall path cost function is designed. The path planning experiments are carried out in ROS (Robot Operation System) simulation environment and actual environment respectively, and the results show that. The hybrid $A^{*}$ algorithm with multi-objective constraints increases the minimum distance between the robot and obstacles by more than 50%, reduces the unnecessary times of reversing and turning, and reduces the total running time by 14.2% on average, thus improving the navigation efficiency of the mobile robot.
DT-SV: A Transformer-based Time-domain Approach for Speaker Verification

Speaker verification (SV) aims to determine whether the speaker's identity of a test utterance is the same as the reference speech. In the past few years, extracting speaker embeddings using deep neural networks for SV systems has gone mainstream. Recently, different attention mechanisms and Transformer networks have been explored widely in SV fields. However, utilizing the original Transformer in SV directly may have frame-level information waste on output features, which could lead to restrictions on capacity and discrimination of speaker embeddings. Therefore, we propose an approach to derive utterance-level speaker embeddings via a Transformer architecture that uses a novel loss function named diffluence loss to integrate the feature information of different Transformer layers. Therein, the diffluence loss aims to aggregate frame-level features into an utterance-level representation, and it could be integrated into the Transformer expediently. Besides, we also introduce a learnable mel-fbank energy feature extractor named time-domain feature extractor that computes the mel-fbank features more precisely and efficiently than the standard mel-fbank extractor. Combining Diffluence loss and Time-domain feature extractor, we propose a novel Transformer-based time-domain SV model (DT-SV) with faster training speed and higher accuracy. Experiments indicate that our proposed model can achieve better performance in comparison with other models.
Don’t Forget Me: Accurate Background Recovery for Text Removal via Modeling Local-Global Context

Text removal has attracted increasingly attention due to its various applications on privacy protection, document restoration, and text editing. It has shown significant progress with deep neural network. However, most of the existing methods often generate inconsistent results for complex background. To address this issue, we propose a Contextual-guided Text Removal Network, termed as CTRNet. CTRNet explores both low-level structure and high-level discriminative context feature as prior knowledge to guide the process of text erasure and background restoration. We further propose a Local-global Content Modeling (LGCM) block with CNNs and Transformer-Encoder to capture local features and establish the long-term relationship among pixels globally. Finally, we incorporate LGCM with context guidance for feature modeling and decoding. Experiments on benchmark datasets, SCUT-EnsText and SCUT-Syn show that CTRNet significantly outperforms the existing state-of-the-art methods. Furthermore, a qualitative experiment on examination papers also demonstrates the generalizability of our method. The code of CTRNet is available at https://github.com/lcy0604/CTRNet.
How Perceptions of Trust and Intrusiveness Affect the Adoption of Voice Activated Personal Assistants

Voice Activated Personal Assistants (VAPA) are unique and different from other Information Systems (IS) due to their personalized, intelligent, and human-like behavior. Given the unique characteristics of these VAPA’s, current technology adoption models are not comprehensive enough for explaining the usage of these systems. While trust and privacy have been identified as relevant issues affecting adoption of VAPA’s, both these have been treated in a simplistic fashion that is not effective keeping in mind the complex nature of these factors. Moreover, being “always on”, VAPA’s are intrusive by nature: another aspect that current research has overlooked. Drawing on current findings in IS and artificial intelligence, we propose two different types of trust (cognitive and emotional) together with their antecedents (anthropomorphism, intelligence, VAPA privacy concern, household privacy concern, vendor & third-party privacy concern, and government privacy concern). The moderation effect of perceived intrusiveness on usage behavior is also examined. The proposed research model is empirically validated with data obtained from 466 VAPA users in India using a Structure Equation Modelling approach. We observe that perceived anthropomorphism does not affect emotional trust, whereas the effect of perceived intelligence on cognitive trust is significant. Social privacy concerns like VAPA and household privacy affect both forms of trust, whereas the effect of institutional privacy category is weak with only vendor & third-party privacy concern affecting emotional trust. Additionally, the findings establish the moderating role of perceived intrusiveness in dampening and negatively influencing the usage of VAPA’s, with a stronger effect for large households.
LSM: A Lightweight Security Mechanism for IoT Based Smart City Management Systems using Blockchain

Smart cities utilize digital technologies for the improvement of its services’ quality and performance by reducing resources’ cost and consumption, with a commitment of action and efficiency to its citizens. The increased urban migration has led to many problems in cities, such as traffic congestion, waste management, noise pollution, energy consumption, air pollution, etc., as nowadays COVID-19 pandemic has seized the whole world. So, it is necessary to carry out its standard operating procedures (SOPs), including less human interaction. Thus, technology plays a vital role via Internet-of-Things (IoT) based systems. In this paper, a lightweight security mechanism (LSM) is proposed to enrich the IoT based systems. Blockchain technology is integrated, and its completely decentralized peer-to-peer (P2P) technology enables the users’ authentication and authorizes legitimate procedures. The IoT based management system is developed to monitor some of the aforementioned problems and solve solid waste, air, and noise monitoring systems. The Ethereum blockchain is used to implement a smart contract based framework for the system’s security and access control. The evaluation of performance of the LSM demonstrates that it is an efficient and lightweight tool in terms of cost, resources, and computation and superior over related security studies.
Fast Light-Weight Near-Field Photometric Stereo

We introduce the first end-to-end learning-based solution to near-field Photometric Stereo (PS), where the light sources are close to the object of interest. This setup is especially useful for reconstructing large immobile objects. Our method is fast, producing a mesh from 52 512x384 resolution images in about 1 second on a commodity GPU, thus potentially unlocking several AR/VR applications. Existing approaches rely on optimization coupled with a far-field PS network operating on pixels or small patches. Using optimization makes these approaches slow and memory intensive (requiring 17GB GPU and 27GB of CPU memory) while using only pixels or patches makes them highly sus-ceptible to noise and calibration errors. To address these issues, we develop a recursive multi-resolution scheme to estimate surface normal and depth maps of the whole image at each step. The predicted depth map at each scale is then used to estimate 'per-pixel lighting, for the next scale. This design makes our approach almost 45x faster and 2° more accurate (11.3° vs. 13.3° Mean Angular Error) than the state-of-the-art near-field PS reconstruction technique, which uses iterative optimization.
Computational Understanding of Narratives: A Survey

Storytelling, and the delivery of societal narratives, enable human beings to communicate, connect, and understand one another and the world around them.Narratives can be defined as spoken, visual, or written accounts of interconnected events and actors, generally evolving through some notion of time.Today, information is typically conveyed over online communication mediums, such as social media and blogging websites.Consequently, the act of narrative delivery itself has shifted from simply imparting information through self-contained structures such as books, to more fragmented structures, such as social media websites, where evolving story events are constructed over multiple online sources.Ubiquitous online conversation can manifest into sophisticated narratives that have the potential to influence wide-spread user interpretations of cultural sentiments, attitudes, values, as well as geopolitical events and facts.As a result, narratives are actively being used as strategic tools for shaping local events, promoting collective opinions, and asserting ideologies and propaganda, making them sources of interest for identifying themes, intentions, and goals across multiple communities and potential adversaries.Identifying fragmented narratives, extracting thematic and temporal components that constitute evolving narratives, and locating signs of active rhetoric framing tactics, are difficult to detect and analyze without large-scale automation.This problem can be addressed through the use of natural language understanding technologies.Our goal is to document and discuss methods to efficiently construct, extract, and detect evolving online narratives.The novel contribution of this paper is the formal collation and documentation of such technologies and research areas, as well as extensive discussion on open research challenges and goals in the definition, identification, construction, generation, and representation of online narratives.To our knowledge, there is currently no existing formal documentation that organizes and provides extended discussion on narrative understanding research areas and open challenges.
Unwinding Rotations Improves User Comfort with Immersive Telepresence Robots

We propose unwinding the rotations experienced by the user of an immersive telepresence robot to improve comfort and reduce VR sickness of the user. By immersive telepresence we refer to a situation where a 360° camera on top of a mobile robot is streaming video and audio into a head-mounted display worn by a remote user possibly far away. Thus, it enables the user to be present at the robot's location, look around by turning the head and communicate with people near the robot. By unwinding the rotations of the camera frame, the user's viewpoint is not changed when the robot rotates. The user can change her viewpoint only by physically rotating in her local setting; as visual rotation without the corresponding vestibular stimulation is a major source of VR sickness, physical rotation by the user is expected to reduce VR sickness. We implemented unwinding the rotations for a simulated robot traversing a virtual environment and ran a user study (N=34) comparing unwinding rotations to user's viewpoint turning when the robot turns. Our results show that the users found unwound rotations more preferable and comfortable and that it reduced their level of VR sickness. We also present further results about the users' path integration capabilities, viewing directions, and subjective observations of the robot's speed and distances to simulated people and objects.
Distributed Filter With Biased Measurements: A Scalable Bias-Correction Approach

This paper addresses the distributed state estimation over a large scale sensor network wherein each sensor may have an unknown measurement bias. In order to mitigate the influence of biases to state estimation, a bias estimator only depending on the structure of local sensor is constructed to ensure the algorithm's scalability for sensor networks. It is proved that the proposed bias estimator can effectively realize the bias estimation for the slow time-varying measurement bias. In addition, a distributed Kalman filter embedded with local bias estimator is developed to efficiently correct the biased measurements. Both consistency and stability of the filter are guaranteed under a general collective observability condition for linear time-varying systems. A numerical simulation is provided to illustrate the effectiveness of the developed results.
Localized Learning: A Possible Alternative to Current Deep Learning Techniques

At present, the most efficient deep learning technique is the use of deep neural networks. However, recent empirical results show that in some situations, it is even more efficient to use “localized” learning—i.e., to divide the domain of inputs into sub-domains, learn the desired dependence separately on each sub-domain, and then “smooth” the resulting dependencies into a single algorithm. In this paper, we provide theoretical explanation for these empirical successes.