Reviewerly/exHarmony huggingface dataset:
SIG-VC: A Speaker Information Guided Zero-Shot Voice Conversion System for Both Human Beings and Machines

Nowadays, as more and more systems achieve good performance in traditional voice conversion (VC) tasks, people’s attention gradually turns to VC tasks under extreme conditions. In this paper, we propose a novel method for zero-shot voice conversion. We aim to obtain intermediate representations for speaker-content disentanglement of speech to better remove speaker information and get pure content information. Accordingly, our proposed framework contains a module that removes the speaker information from the acoustic feature of the source speaker. Moreover, speaker information control is added to our system to maintain the voice cloning performance. The proposed system is evaluated by subjective and objective metrics. Results show that our proposed system significantly reduces the trade-off problem in zero-shot voice conversion, while it also manages to have high spoofing power to the speaker verification system.
Feature-Indistinguishable Attack to Circumvent Trapdoor-Enabled Defense

Deep neural networks (DNNs) are vulnerable to adversarial attacks. A great effort has been directed to developing effective defenses against adversarial attacks and finding vulnerabilities of proposed defenses. A recently proposed defense called Trapdoor-enabled Detection (TeD) deliberately injects trapdoors into DNN models to trap and detect adversarial examples targeting categories protected by TeD. TeD can effectively detect existing state-of-the-art adversarial attacks. In this paper, we propose a novel black-box adversarial attack on TeD, called Feature-Indistinguishable Attack (FIA). It circumvents TeD by crafting adversarial examples indistinguishable in the feature (i.e., neuron-activation) space from benign examples in the target category. To achieve this goal, FIA jointly minimizes the distance to the expectation of feature representations of benign samples in the target category and maximizes the distances to positive adversarial examples generated to query TeD in the preparation phase. A constraint is used to ensure that the feature vector of a generated adversarial example is within the distribution of feature vectors of benign examples in the target category. Our extensive empirical evaluation with different configurations and variants of TeD indicates that our proposed FIA can effectively circumvent TeD. FIA opens a door for developing much more powerful adversarial attacks. The FIA code is available at: https://github.com/CGCL-codes/FeatureIndistinguishableAttack.
Analyzing Midair Object Pointing Mappings for Smart Display Input

One common task when controlling smart displays is the manipulation of menu items. Given current examples of smart displays that support distant bare hand control, in this paper we explore menu item selection tasks with three different mappings of barehand movement to target selection. Through a series of experiments, we demonstrate that Positional mapping is faster than other mappings when the target is visible but requires many clutches in large targeting spaces. Rate-based mapping is, in contrast, preferred by participants due to its perceived lower effort, despite being slightly harder to learn initially. Tradeoffs in the design of target selection in smart tv displays are discussed.
Uranium resources in the Middle East and North Africa

The uranium resources in the Middle East and North African countries have been discussed and explored in this chapter. The chapter highlights the recognition criteria for the identification of uranium resources provinces. These criteria depend on the characteristics of the host rocks and the surrounding geological environments. The chapter provides evidences on the importance of various recognition criteria for uranium in granites and sandstone rocks, and their exploration implications. The presented information in this part of the book indicates that most uranium resources are hosted in the Late Proterozoic granites and the Phanerozoic sandstones. These criteria will be utilized for predicting potential uranium in the Middle East and North African countries. These two rock groups are the most important for hosting the main uranium resources. The relation between uranium-enriched granitoid and geological time-bound is highlighted in this chapter because it found to be of remarkable importance. The exploration history and the mode of distribution of uranium resources in the following Middle East countries are presented and discussed. These countries are Egypt, Iran, Iraq, Jordan, Saudi Arabia, Sudan, Syria, Turkey, United Arab Emirates, Oman, Qatar, Kuwait, and Yemen. The North African countries include Algeria, Libya, Morocco, and Tunisia.
Self-Supervised Convolutional Neural Networks for Fast Gesture Recognition in Human-Robot Interaction

Current autonomous systems (e.g., self-driving cars, autonomous drones, consumer robots, etc.) can already perform a wide variety of tasks and are predicted to be able to collaboratively assist humans in the near future. Thus, the need for efficient human-robot interaction (HRI) methods is greatly increasing. Gesture recognition is an effective HRI approach, since many robots are equipped with cameras and computer vision algorithms have progressed significantly in recent years, with advanced Deep Neural Networks (DNNs) being able to be executed on-board an autonomous system. However, computational/memory limitations are still significant for embedded AI methods, rendering the increase of DNN accuracy without imposing a penalty on runtime requirements a very important research priority. This paper investigates self-supervised DNN pretraining for a novel pretext task, relying on spatiotemporal video frame compression via tensor decomposition and low-rank approximation, as a means to augment gesture recognition performance, without inducing any runtime overhead during the inference stage. Thus, the method permits the use of less complex and much faster neural architectures that are well-suited to robotics applications and HRI. Quantitative evaluation on a gesture recognition dataset for autonomous Unmanned Aerial Vehicle (UAV) handling demonstrates the effectiveness and real-time performance of the proposed method on embedded AI compute hardware.
From heatmaps to structured explanations of image classifiers

This paper summarizes our endeavors in the past few years in terms of explaining image classifiers, with the aim of including negative results and insights we have gained. The paper starts with describing the explainable neural network (XNN), which attempts to extract and visualize several high-level concepts purely from the deep network, without relying on human linguistic concepts. This helps users understand network classifications that are less intuitive and substantially improves user performance on a difficult fine-grained classification task of discriminating among different species of seagulls. Realizing that an important missing piece is a reliable heatmap visualization tool, we have developed I-GOS and iGOS++ utilizing integrated gradients to avoid local optima in heatmap generation, which improved the performance across all resolutions. During the development of those visualizations, we realized that for a significant number of images, the classifier has multiple different paths to reach a confident prediction. This has lead to our recent development of structured attention graphs (SAGs), an approach that utilizes beam search to locate multiple coarse heatmaps for a single image, and compactly visualizes a set of heatmaps by capturing how different combinations of image regions impact the confidence of a classifier. Through the research process, we have learned much about insights in building deep network explanations, the existence and frequency of multiple explanations, and various tricks of the trade that make explanations work. In this paper, we attempt to share those insights and opinions with the readers with the hope that some of them will be informative for future researchers on explainable deep learning.
Sequence and Distance Aware Transformer for Recommendation Systems

Transformer has achieved admirable success in sequential tasks. However, the model only considers the order of items in the sequence, not the relative distances, which weakens the relevance between items. To this end, we propose a novel Sequence and Distance Aware Transformer (SDAT) for recommendation systems. Specifically, we first apply the Transformer to handle the interaction between the items effectively. Then, Gated Recurrent Unit can be designed to aggregate information on an item-by-item basis in sequential information, meanwhile, we adopt the attention mechanism to focus on items with smaller time intervals to indicate high relevance. We also add a time gain function to augment the influence weight of recent items. Finally, the processing result of the time information of our integrated items replaces the positional encoding representation of the original Transformer. Extensive experiments on three real-world datasets show that SDAT outperforms state-of-the-art methods.
The Moderating Effect of Debriefing on Learning Outcomes of IVR-Based Instruction: An Experimental Research

With its ability to afford immersive and interactive learning experiences, virtual reality has been widely used to support experiential learning, of which the learning effectiveness is promoted by the instructional component of debriefing. The current literature on debriefing mainly focuses on the traditional learning contexts while little is known on its effectiveness in immersive virtual reality (IVR) learning environments. Based on the theories of experiential learning and debriefing, this study designed a debriefing strategy based on simulated learning experience and investigated its effectiveness on knowledge and behavioral learning in an IVR learning program, using a randomized controlled trial with 77 elementary students from Hubei province in China. The study results support the efficacy of IVR on improving knowledge acquisition and behavioral performance, and reveal a significant moderating effect of debriefing on the effectiveness of IVR learning environments. The study confirms the critical role of debriefing in IVR-based instruction and provides theoretical and practical implications for the design and implementation of effective IVR learning environments.
The extraction of pixel-wise visual multi-cues for AHP-based privacy measurement

Visual privacy measurement is a computable evaluation method to quantitatively describe privacy-sensitive content in images or videos. The visual cues and their quantitative evaluation model are two key points to obtain satisfactory results for the visual privacy measurement. To this end, this paper proposes a novel visual privacy measurement framework based on analytic hierarchy process to achieve instance-level visual privacy measurement by using pixel-wise multi-cues, including: firstly, a boundary constraint-based multi-objects saliency detection module with gestalt-laws prior is proposed to generate fine salient maps in complex scenes. And a real-time instance segmentation module is built to generate pixel-wise masks by training new privacy-related annotations. The two module are combined to generate multiple visual cues, such as saliency feature, category feature and area feature. Then, a measurement model based on analytic hierarchy process is established to determine the correlated weights of multi-cues to the privacy levels, which depends on the pixel-wise visual cues including saliency feature, the category feature and the area feature. Finally, the privacy levels are calculated according to the feature values and their weights. Experimental results show that the proposed methods provide more reliable visual cues of multi-objects with fine structure and high efficiency, and the measurement results are highly consistent with the human evaluation results.
Decentralised Blockchain-based Solutions for Electronic Healthcare Record with Interacting Social Networking Components

The healthcare industry is a conjunction of monolithic applications based on neutral and time-stamped diagnostics. Hence, it is particularly interested in decentralised technologies to achieve global and social-driven electronic health records. Blockchain is a decentralised networking system where multiple copies of immutable data records are distributed and validated among different independent nodes. While traditionally used to create currently famous digital cryptocurrencies like Bitcoin, its worldwide fever helped to extend its applications beyond the financial industry. However, given how fast completely new technologies evolve, healthcare stakeholders are struggling to find the correct usage of blockchain ecosystem developments. Our goal is to provide a summary of the state-of-the-art efforts in the development of decentralised blockchain-based solutions for electronic healthcare records, in which social networking and wearable technology data is considered in order to provide patient-driven diagnostics - proven to be critical in the current SARS-CoV-2 pandemic. In this paper, we present a comprehensive guide on blockchain technologies competing in the healthcare market, including emerging blockchain-based initiatives and trends. Additionally, we discuss the main caveats of blockchain applications development in this industry based on practical experience.
SpearSim: Design and Evaluation of Synthetic Task Environment for Studies on Spear Phishing Attacks

Despite significant advancements in security technologies, phishing attacks continue to be rampant and successful because distinguishing phishing emails from real messages remains difficult to most end-users, mainly the targeted kind known as spear-phishing. There is a severe lack of human factor studies on spear-phishing attacks due to lack of methods and datasets. We have designed a novel multi-player synthetic task environment, called SpearSim, for conducting laboratory experiments on spear-phishing attacks. Using SpearSim, we have conducted an experiment to understand how information exploitation in spear-phishing attacks influences end-user decision-making. This paper describes the SpearSim system’s design and discusses the results from the experiment conducted with SpearSim. The experiment results show that people are more vulnerable to spear-phishing attacks when attackers can explore and exploit different kinds of personal information available to them about their targets. We discuss the implications of this research for the design of anti-phishing training solutions and privacy enhancing technologies.
The Invisible Side of Certificate Transparency: Exploring the Reliability of Monitors in the Wild

To detect fraudulent TLS server certificates and improve the accountability of certification authorities (CAs), certificate transparency (CT) is proposed to record certificates in publicly-visible logs, from which the monitors fetch all certificates and watch for suspicious ones. However, if the monitors, either domain owners themselves or third-party services, fail to return a complete set of certificates issued for a domain of interest, potentially fraudulent certificates may not be detected and then the CT framework becomes less reliable. This paper presents the first systematic study on CT monitors. We analyze the data in 88 public logs and the services of 5 active third-party monitors regarding 3,000,431 certificates of 6,000 selected Alexa Top-1M websites. We find that although CT allows ordinary domain owners to act as monitors, it is impractical for them to perform reliable processing by themselves, due to the rapidly increasing volume of certificates in public logs (e.g., on average about 5 million records or 28.29 GB daily for the minimal set of logs that need to be monitored in 2018, or more than 7 million records per day in 2020, according to the Chrome CT policy). Moreover, our study discloses that ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${a}$ </tex-math></inline-formula> ) none of the third-party monitors guarantees to return the complete set of certificates for a domain, and ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${b}$ </tex-math></inline-formula> ) for some domains, even the union of the certificates returned by the five third-party monitors can probably be incomplete. As a result, the certificates accepted by CT-enabled browsers are not actually visible to the claimed domain owners, even when CT is adopted with well-functioning logs. The risk of invisible fraudulent certificates in public logs raises doubts on the reliability of CT in practice.
Understating Factors Affecting Traveling During COVID-19 Using Sentiment Analysis

Covid-19 leads to public fear. People are afraid to exist in crowded places like public transportation, airports, and hotels. Covid-19 has affected the field of tourism negatively. Many factors affect the travelers' intentions to travel, including safety and security, space accessibility, travel costs, quality issues, sanitation risks, hygiene, and destination trust. The research aims to propose a sentiment classifier model that analyses travelers' sentiments intention. The TravelerIntention Sentiment model uses three classification techniques: Support Vector Machine, Naïve Bayes and Decision Tree. One thousand sentiments were collected and analyzed. The safety and security factor was the highest important factor based on 326 sentiments. Results have shown that Naïve Bayes has the highest accuracy when using the Term Frequency Inverse Document Frequency feature selection method, and Support Vector Machine has the highest accuracy level when using the Bag of Words feature selection method.
A Novel Method for Network Traffic Prediction Using Residual Mogrifier GRU

Network traffic prediction is essential for network management and resource scheduling within Web information systems. However, existing prediction methods have difficulty fitting mutation values in traffic time-series data and are still inadequate in terms of precision. Here we describe a method for prediction using multimodal web traffic data. The method creates multi-dimensional time series on request traffic, response traffic, and abnormal code traffic, and uses the rich information contained in the different sequences in the preceding time window to make inferences about the traffic scale in subsequent time windows. In addition, we propose an improved algorithm based on the Gated Recurrent Unit (GRU) to reduce the prediction error. The algorithm introduces the residual structure into a stacked multi-layer recurrent network structure and uses the Mogrifier structure to interact the information before it is fed to the gating unit. The experimental results show that the improved method leads to a further reduction in the error between the predicted and true values, providing high usability in the field of network traffic prediction.
Human Face Detection and Tracking Using RetinaFace Network for Surveillance Systems

Face detection is the main component in the development of CCTV-based Intelligent Surveillance System. Face Detection is used to identify a person when a suspicious event occurs. Therefore, the face detection module must be reliable and fast in analyzing every frame produced by CCTV. RetinaFace is a deep learning-based face detection method that produces very high accuracy. However, RetinaFace cannot be fully implemented directly on the ISS due to limitations in detecting faces in environments with illumination changes. Thus, in this paper, we propose to utilize the detection-based tracking to improve the detection results of RetinaFace as post-processing stages. The tracking system successfully increases the recall score of the detection of faces on recordings with 25 FPS by 4.47%.
Sentiment Analysis on Global Warming Tweets Using Naïve Bayes and RNN

The concept is based on interpreting positive or negative sentiments expressed by human beings where the subject of climate change or global warming is concerned. The sentiment analysis was done using machine learning and as well been done using deep learning, with python. An existing dataset on climate change is used. After cleaning and processing the data, a dataset having the comments classified into positive and negative sentiment remains. This paper proposes to train the algorithm to interpret positive or negative sentiments expressed by human beings where the subject of climate change or global warming is concerned and thus using the multinomial Naïve Bayes algorithm and consecutively the long short-term memory algorithm to be able to classify the intentions given a new piece of data once the model is trained, tested and validated.
Detection of Driver Distraction using YOLOv5 Network

The impact of deaths and injuries on families and society is increasing the willingness of the authorities to investigate the cause of the increasing traffic accidents day by day. It is important to emphasize that the majority of traffic accidents are attributed to driver distraction. The role of mobile phones and cigarette usage on driver distraction is a well-known fact. In this study, the authors focus on detecting mobile phone usage and smoking in-vehicle environment. The images collected with a mobile phone docked on the windshield and a yolov5s network is trained with manually labeled images. As a consequence of the study, the authors achieved to distinguish drivers and passengers. Also, they investigate to improve detecting other labeled classes such as ‘DriverHand’, ‘PassengerHand’, ‘DriverHandWithPhone’, etc.
Poster: Towards Verifiable Mutability for Blockchains

Due to their immutable log of information, blockchains can be considered as a transparency-enhancing technology. The immutability, however, also introduces threats and challenges with respect to privacy laws and illegal content. Introducing a certain degree of mutability, which enables the possibility to store and remove information, can therefore increase the opportunities for blockchains. In this paper, we present a concept for a mutable blockchain structure. Our approach enables the removal of certain blocks, while maintaining the blockchain's verifiability property. Since our concept is agnostic to any consensus algorithms, it can be implemented with permissioned and permissionless blockchains.
A Unified PAC-Bayesian Framework for Machine Unlearning via Information Risk Minimization

Machine unlearning refers to mechanisms that can remove the influence of a subset of training data upon request from a trained model without incurring the cost of re-training from scratch. This paper develops a unified PAC-Bayesian framework for machine unlearning that recovers the two recent design principles - variational unlearning [1] and forgetting Lagrangian [2] as information risk minimization problems [3]. Accordingly, both criteria can be interpreted as PAC-Bayesian upper bounds on the test loss of the unlearned model that take the form of free energy metrics.