Reviewerly/exHarmony huggingface dataset:
Push the Limit of Adversarial Example Attack on Speaker Recognition in Physical Domain

The integration of deep learning on Speaker Recognition (SR) advances its development and wide deployment, but also introduces the emerging threat of adversarial examples. However, only a few existing studies investigate its practical threat in physical domain, which either evaluate its feasibility only by directly replaying generated adversarial examples, or explore the partial channel interference for robustness improvement. In this paper, we propose a physical adversarial example attack, PhyTalker, which could generate and inject perturbations on voices in a live-streaming manner on attacking various SR models in different physical channels. Compared with the typical adversarial example for digital attacks, PhyTalker generates a subphoneme-level perturbation dictionary to decouple the perturbation optimization and injection. Moreover, we introduce the channel augmentation to compensate both device and environmental distortions, as well as model ensemble to improve the perturbation transferability. Finally, PhyTalker recognizes and localizes the latest recorded phoneme to determine the corresponding perturbations for real-time broadcasting. Extensive experiments are conducted with a large-scale corpus in real physical scenarios, and results show that PhyTalker achieves an overall Attack Success Rate (ASR) of 85.5% in attacking mainstream SR systems and Mel Cepstral Distortion (MCD) of 2.45dB in human audibility.
M5

Multi-user volumetric content delivery can enable numerous appealing applications, such as online education, telehealth, multiuser AR/VR training, immersive collaborative analytics, etc. However, the bandwidth-intensive nature of volumetric video streaming makes existing systems for single-user experiences hard to scale to multi-user scenarios. To address this critical issue, in this paper, we first perform a scaling experiment on mmWave networks that offer the needed multi-Gbps throughput and identify two key challenges of streaming high-quality volumetric videos to multiple users: frequent blockages of mmWave links and high transmission redundancy among users. To solve these problems, we propose a first-of-its-kind, agile, and cross-layer system, dubbed M5, for improving the performance and quality of experience for multi-user volumetric video streaming. M5 utilizes the 6DoF motion prediction of users to proactively adapt mmWave beams and prefetch frames to mitigate the blockage effects. Furthermore, it takes advantage of the multicast transmission to deliver the overlapped common content within users' viewports to reduce the bandwidth requirement. Our extensive experiments on a real testbed and with a trace-driven simulator show that M5 can effectively improve the frame rate by 44.1% and volumetric video quality by 62.3% compared to the state-of-the-art system.
Gaze Tracking on Any Surface with Your Phone

This paper introduces ASGaze, a new gaze tracking system designed using the common RGB camera from mobile phones. In addition to improving the accuracy of existing RGB camera-based gaze tracking methods, a novelty of ASGaze is that it can be configured to track gaze points on various surface areas commonly required in different applications, such as mobile phone screens, computer displays or even non-electronic surfaces like whiteboards or paper - a situation that is difficult for existing RGB camera-based methods to handle. To achieve the design of ASGaze, we revisit the 3D geometric model of the eye, which is widely adopted by high-end and commercial gaze trackers, and it has the potential to achieve our design goals. To avoid the high cost of commercial solutions, we identify three key issues to be addressed when processing the eye model with an RGB camera, including how to first accurately extract eye iris boundary that is the meta-information in our gaze tracking design, and then how to remove gaze ambiguity from iris boundary to gaze point transformation, and finally how to precisely map gaze points to the target tracking surface. In this paper, we propose a series of effective techniques to address these issues. We develop a prototype system and conduct extensive experiments on three different typical tracking surfaces to show promising performance gains compared to the recent solution.
A Modular Approach to the Security Analysis of Two-Permutation Constructions

Constructions based on two public permutation calls are very common in today’s cryptographic community. However, each time a new construction is introduced, a dedicated proof must be carried out to study the security of the construction. In this work, we propose a new tool to analyze the security of these constructions in a modular way. This tool is built on the idea of the classical mirror theory for block cipher based constructions, such that it can be used for security proofs in the ideal permutation model. We present different variants of this public permutation mirror theory such that it is suitable for different security notions. We also present a framework to use the new techniques, which provides the bad events that need to be excluded in order to apply the public permutation mirror theory. Furthermore, we showcase the new technique on three examples: the Tweakable Even-Mansour cipher by Cogliati et al. (CRYPTO ’15), the two permutation variant of the pEDM PRF by Dutta et al. (ToSC ’21(2)), and the two permutation variant of the nEHtM $$_p$$ MAC algorithm by Dutta and Nandi (AFRICACRYPT ’20). With this new tool we prove the multi-user security of these constructions in a considerably simplified way.
Traceable Receipt-Free Encryption

CCA-like game-based security definitions capture confidentiality by asking an adversary to distinguish between honestly computed encryptions of chosen plaintexts. In the context of voting systems, such guarantees have been shown to be sufficient to prove ballot privacy (Asiacrypt’12). In this paper, we observe that they fall short when one seeks to obtain receipt-freeness, that is, when corrupted voters who submit chosen ciphertexts encrypting their vote must be prevented from proving how they voted to a third party. Since no known encryption security notion can lead to a receipt-free ballot submission process, we address this challenge by proposing a novel publicly verifiable encryption primitive coined Traceable Receipt-free Encryption (TREnc) and a new notion of traceable CCA security filling the definitional gap underlined above. We propose two TREnc instances, one generic achieving stronger guarantees for the purpose of relating it to existing building blocks, and a dedicated one based on SXDH. Both support the encryption of group elements in the standard model, while previously proposed encryption schemes aiming at offering receipt-freeness only support a polynomial-size message space, or security in the generic group model. Eventually, we demonstrate how a TREnc can be used to build receipt-free protocols, by following a standard blueprint.
Use of Mathematical Models in the Energy Sphere of Human Activity

It is impossible to imagine a modern human society without energy, and the need for it is increasing every year.To meet this need, first of all, it is necessary to conduct research in this area.Conducting such studies will show the current state of energy efficiency in the enterprise.It allows you to analyze the use of energy resources of the enterprise, the cost of them, to detect places of unreasonable use of resources, to develop a program for the implementation of energy-saving events and projects.Since these technologies are complex and expensive, scientists suggest testing new technologies on mathematical models, which is faster and much cheaper.This paper provides a literature review of examples of the use of such models in various energy sectors.On these examples it will be possible to clearly see the relevance of this topic.It is concluded that mathematical modeling of energy facilities has widely penetrated into many branches of the national economy.Moreover, the environment for the software implementation of the developed models varies in a wide range, from the universal MathCad type to specialized software packages.The paper considers only some areas of energy, where this issue is most important, and also summarizes the benefits obtained by using mathematical models.
Proof of Work Consensus Based Peer to Peer Energy Trading in the Indian Residential Community

Rooftop solar power generation is becoming more widespread in residential microgrids. As well as new concepts of electricity markets, such as peer-to-peer (P2P) markets, where consumers and prosumers can directly exchange locally generated energy with each other without any intermediary third party for sustainable development. Data security is a big concern with energy trading; therefore, blockchain technology is being used more and more in energy markets. It has the potential to simplify P2P energy trading. In this paper, blockchain is designed to fit into the decentralized nature of the P2P market, securing the payment mechanism and transaction data store. The blockchain-enabled platform is developed using the Proof-of-Work (PoW) consensus algorithm, and is verified with the help of the Postman application programming interface (API). All transactions involving the buying and selling of energy are handled by a miner without the help of any third parties. The study of a five-user residential community, whether the strategy is recommended or not, is validated through simulation findings. An overview of the results revealed that all users benefited from the developed, secure P2P platform.
An Efficient Certificateless Forward-Secure Signature Scheme for Secure Deployments of the Internet of Things

As an extension of the wired network, the use of the wireless communication network has considerably boosted users’ productivity at work and in their daily lives. The most notable aspect of the wireless communication network is that it overcomes the constraints of the wired network, reduces the amount of cost spent on wire maintenance, and distributes itself in a manner that is both more extensive and flexible. Combining wireless communication with the Internet of Things (IoT) can be used in several applications, including smart cities, smart traffic, smart farming, smart drones, etc. However, when exchanging data, wireless communication networks use an open network, allowing unauthorized users to engage in communication that is seriously destructive. Therefore, authentication through a digital signature will be the best solution to tackle such problems. Several digital signatures are contributing to the authentication process in a wireless communication network; however, they are suffering from several problems, including forward security, key escrow, certificate management, revocations, and high computational and communication costs, respectively. Keeping in view the above problems, in this paper we proposed an efficient certificateless forward-secure signature scheme for secure deployments in wireless communication networks. The security analysis of the proposed scheme is carried out using the random oracle model (ROM), which shows that it is unforgeable against type 1 and type 2 adversaries. Moreover, the computational and communication cost analyses are carried out by using major operations, major operations cost in milliseconds, and extra communication bits. The comparative analysis with the existing scheme shows that the proposed scheme reduces the computational cost from 19.23% to 97.54% and the communication overhead from 11.90% to 83.48%, which means that the proposed scheme is efficient, faster, and more secure for communication in the wireless communication network.
Generation of Boxes and Permutations Using a Bijective Function and the Lorenz Equations: An Application to Color Image Encryption

Some images that contain sensitive information and travel through the network require security. Therefore, a symmetric cryptosystem that encrypts images and resists known attacks is developed. Subsequently, in this work, an encryption algorithm known as Image Cipher utilizing Lorenz equation and a Bijective Function—ICLEBF are proposed. In the proposal, the Lorenz equations and the Bijective function are used to generate boxes, the permutation, and schedule keys, considering that all these elements are different in each encryption process. The encryption procedure consists of 14 rounds, where a different box is applied in each round. In this type of algorithm, the impact of quantum computers will be less forceful and can be useful for that epoch. On the other hand, the quality of the encrypted images and the loss of sharpness in decoded images with damage are measured. In addition, an attack from five types of noise (one of which is a developed proposal) is carried out by applying it to encrypted images. Finally, the results of the proposed ICLEBF are compared with other recent image encryption algorithms, including the Advanced Encryption Standard. As a result, this proposal resists known attacks and others that the current standard does not support.
The Choice of Textual Knowledge Base in Automated Claim Checking

Automated claim checking is the task of determining the veracity of a claim given evidence retrieved from a textual knowledge base of trustworthy facts. While previous work has taken the knowledge base as given and optimized the claim-checking pipeline, we take the opposite approach—taking the pipeline as given, we explore the choice of the knowledge base. Our first insight is that a claim-checking pipeline can be transferred to a new domain of claims with access to a knowledge base from the new domain. Second, we do not find a “universally best” knowledge base—higher domain overlap of a task dataset and a knowledge base tends to produce better label accuracy. Third, combining multiple knowledge bases does not tend to improve performance beyond using the closest-domain knowledge base. Finally, we show that the claim-checking pipeline’s confidence score for selecting evidence can be used to assess whether a knowledge base will perform well for a new set of claims, even in the absence of ground-truth labels.
A Framework for Determining the Big Five Personality Traits Using Machine Learning Classification through Graphology

Along with the progress of the times, the development of graphology has changed towards computerization. The fundamental problem in automated graphology is how to determine personality traits through digital handwriting using the principles of graphology. Although various models and approaches have been developed in research related to automated graphology, there are still obstacles to overcome such as the selection of preprocessing techniques and image processing algorithms to extract handwriting features and proper classification techniques to get maximum accuracy. Therefore, this study aims to design a reliable framework using image processing and machine learning approaches such as filtering, thresholding, and normalization to determine the personality traits through handwriting features. Then, handwriting features are classified according to the Big Five model. Experiments using the decision tree, SVM (kernel RBF), and KNN produced an accuracy above 99%. These results indicated that the proposed framework can be well applied to predict the personality of the Big Five model through handwriting analysis features.
The landscape of research data management services in Malaysian academic libraries: librarians’ practices and roles

Purpose The debate about academic librarians’ roles in research data management (RDM) services is currently relevant, especially in the context of making research data findable, accessible, interoperable and reproducible. This study aims to explore the RDM services offered by Malaysian academic libraries and the implementation progress based on the librarians’ practices and roles. Design/methodology/approach This descriptive study involves three sequential forms of data collection: a website analysis of 20 academic libraries relating to RDM services, training and policy; an online survey of the academic libraries’ RDM implementation progress; and semi-structured interviews with three academic librarians to gauge their practices and roles in RDM services. Findings Malaysian academic libraries provide RDM services based on their related or basic skills which are bibliographic management tools, institutional repository and openness of research data rather than impacted services to support RDM, such as data analysis, data citation, data mining or data visualisation services. Although the librarians were aware of RDM and their roles in research data services, the progress of practicing and implementation of the RDM services still has not been fully delivered to support the main RDM elements. Practical implications This study illustrates the RDM roadmap on the current landscape of areas and types of services that the libraries are doing well. The list of services can be used and implemented as the best practices or strategies to be applied within Malaysian academic libraries. Originality/value This study highlights the gaps of RDM services in Malaysian academic libraries. To the best of the authors’ knowledge, as this is the first study in Malaysia that articulates the case of RDM services in academic libraries, it has paved the way for further research.
Emotion analysis in socially unacceptable discourse

Texts often express the writer’s emotional state, and it was shown that emotion information has potential for hate speech detection and analysis. In this work, we present a methodology for quantitative analysis of emotion in text. We define a simple, yet effective metric for an overall emotional charge of text based on the NRC Emotion Lexicon and Plutchik’s eight basic emotions. Using this methodology, we investigate the emotional charge of content with socially unacceptable discourse (SUD), as a distinct and potentially harmful type of text which is spreading on social media. We experiment with the proposed method on a corpus of Facebook comments, resulting in four datasets in two languages, namely English and Slovene, and two discussion topics, LGBT+ rights, and the European Migrants crisis. We reveal that SUD content is significantly more emotional than non-SUD comments. Moreover, we show differences in the expression of emotions depending on the language, topic, and target of the comments. Finally, to underpin the findings of the quantitative investigation of emotions, we perform a qualitative analysis of the corpus, exploring in more detail the most frequent emotional words of each emotion, for all four datasets. The qualitative analysis shows that the source of emotions in SUD texts heavily depends on the topic of discussion, with substantial overlaps between languages.
Improving the processing rate of VCSEL-based reservoir computing through adopting variable polarization information injection

A novel information injection method is proposed for improving the information processing rate in reservoir computing (RC) systems based on vertical-cavity surface-emitting lasers (VCSELs), and its effectiveness is numerically verified via Santa-Fe chaotic time series prediction task and waveform recognition task. For VCSEL-based RC systems, conventional information injection (CII) method is usually adopted, namely the polarization direction of the injection light loaded by the information is along either X-polarization component (X-PC) or Y-polarization component (Y-PC) of VCSEL. In this work, the polarization direction of the injection light loaded by the information is set at 45-degree deviated from the X-PC of the VCSEL, which is named as variable polarization information injection (VPII) method. By utilizing the VCSEL-based RC to deal with two different scenarios of single task and two tasks in parallel, the numerical results demonstrate that after adopting VPII to replace CII, the processing rate can be doubled while the system performance maintains the same level.
Sequential generalized measurements: Asymptotics, typicality, and emergent projective measurements

The relation between projective measurements and generalized quantum measurements is a fundamental problem in quantum physics, and clarifying this issue is also important to quantum technologies. While it has been intuitively known that projective measurements can be constructed from sequential generalized or weak measurements, there is still lack of a proof of this hypothesis in general cases. Here we prove it from the perspective of quantum channels. We show that projective measurements naturally arise from sequential generalized measurements in the asymptotic limit. Specifically, a selective projective measurement arises from a set of typical sequences of selective generalized measurements. We provide an explicit scheme to construct projective measurements of a quantum system with sequential generalized measurements. Remarkably, a single ancilla qubit is sufficient to mediate sequential generalized measurements for constructing arbitrary projective measurements of a generic system.
On four intuitionistic fuzzy feeble topological structures

The concepts of an Intuitionistic Fuzzy Feeble Topological Structure (IFFTS) and Intuitionistic Fuzzy Modal Feeble Topological Structure (IFMFTS) are introduced. It is proved that the two extended intuitionistic fuzzy topological operators ${\mathcal{C}^{\varepsilon ,\eta }}$ and ℐ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ε,η</sup> generate IFFTSs, and together the two standard intuitionistic fuzzy modal operators □ and ♢ they generate IFMFTSs. Some basic properties of these structures are discussed.
ColorByte: A real time MOT method using fast appearance feature based on ByteTrack

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. With the wide application of embedded devices, MOT methods with acceptable accuracy that performed in real-time on these weak devices is becoming more and more important. In order to realize real-time MOT on embedded IoT devices, the best trade-off between accuracy and inference latency is the key to achieve. To achieve high accuracy, popular MOT methods introduce Re-ID module to integrate with the detection-based MOT method and train two DNNs simultaneously. However, the integration causes a conflict between computing cost and training both tasks to achieve good results. To address this key issue, we design a fast appearance feature, which is a simple but relatively accurate method, to substitute cumbersome Re-ID component. Besides, ByteTrack is the new SOTA association algorithm in MOT benchmarks which introduce an extra association on objects with low score. Based on ByteTrack, we propose an improved association method to remove most of the background interference based on the results from appearance extraction and recover part of lost detection boxes after the association based on IoU. In addition, we turn down the detection threshold and release more boxes for the low sensitivity of our own feature extraction method. We evaluate our methods and achieve 70.1 MOTA, 81.9 MOTP and 69.2 IDFI with real-time running speed on NVIDIA Xavier NX.
A Semantic Segmentation Method for High-resolution Remote Sensing Images Based on Encoder-Decoder

Image segmentation is a key technology in remote sensing image interpretation, and it is widely used in many fields. Aiming at the common problems of low segmentation accuracy and blurred target boundary in the semantic segmentation of high-resolution remote sensing images, a semantic segmentation method of high-resolution remote sensing images based on encoder-decoder structure is proposed, in which an attention mechanism is introduced to highlight important features, and an optimized Pyramid pooling module is used to extract multi-scale features from different layers. Finally, a multi-level and multi-scale feature fusion strategy is adopted to achieve fine-grained segmentation of high-resolution remote sensing images. The method is also compared and tested on the ISPRS Vaihingen dataset to verify the effectiveness.